1 00:00:00,160 --> 00:00:05,600 Speaker 1: This podcast is brought to you by HKX, Asia's ETF marketplace. Well, 2 00:00:05,640 --> 00:00:08,719 Speaker 1: you'll find a gateway to liquidity and a diverse selection 3 00:00:08,800 --> 00:00:13,320 Speaker 1: of opportunities across asset classes, sectors and themes in Asia 4 00:00:13,400 --> 00:00:16,760 Speaker 1: and beyond. Search HKX to learn more. 5 00:00:18,000 --> 00:00:21,680 Speaker 2: Good morning, good afternoon, good evening, and good nights wherever 6 00:00:21,720 --> 00:00:23,520 Speaker 2: in this vast, beautiful planet. 7 00:00:23,560 --> 00:00:24,720 Speaker 3: You're joining us from. 8 00:00:25,280 --> 00:00:30,560 Speaker 2: Welcome to Tiger Money, a Bloomberg podcast about investing stocks, commodities, bonds, 9 00:00:30,600 --> 00:00:34,239 Speaker 2: crypto and everything in between, with a focus on ETF 10 00:00:34,400 --> 00:00:37,840 Speaker 2: in Asia and also beyond. I'm your host, David Ingless, 11 00:00:37,960 --> 00:00:40,040 Speaker 2: Chief Markets Editor for the Asia Pacific, host of a 12 00:00:40,159 --> 00:00:43,800 Speaker 2: China I should also notte non award winning journalist joining 13 00:00:43,800 --> 00:00:45,839 Speaker 2: me in my co pilot on this journey as we 14 00:00:45,920 --> 00:00:51,120 Speaker 2: recassent head of Asia Pacific ETF Research at Bloomberg Intelligence. Now, 15 00:00:51,120 --> 00:00:53,040 Speaker 2: if you like what you hear, do not forget, please 16 00:00:53,120 --> 00:00:55,960 Speaker 2: to like, subscribe and also to share. 17 00:00:56,600 --> 00:00:58,960 Speaker 4: Today we're very excited to have a very special guest, 18 00:00:59,080 --> 00:01:03,480 Speaker 4: Katy with CEO it Cio of ARC Investment Management. She 19 00:01:03,520 --> 00:01:06,240 Speaker 4: has over forty years of experience in investment management and 20 00:01:06,280 --> 00:01:12,920 Speaker 4: her focus is on disruptive innovation technology including DNN sequencing, robotics, AI, 21 00:01:13,319 --> 00:01:17,000 Speaker 4: energy storage, and blockchain technology. An interesting fact about her 22 00:01:17,080 --> 00:01:19,800 Speaker 4: was when she lived in Ireland, she was fluent in Gaelic. 23 00:01:20,080 --> 00:01:22,080 Speaker 4: Welcome Kathy, and we're so excited to have you here 24 00:01:22,080 --> 00:01:23,320 Speaker 4: today on Tiger Money. 25 00:01:23,840 --> 00:01:26,720 Speaker 5: Oh, thank you Rebecca and David. Great to be here. 26 00:01:27,120 --> 00:01:28,120 Speaker 5: Thank you for inviting me. 27 00:01:29,040 --> 00:01:32,360 Speaker 2: Right, I can't let that go without asking you, Kathy, 28 00:01:32,400 --> 00:01:34,840 Speaker 2: to just say hello to our viewers in Gaelic. We 29 00:01:34,920 --> 00:01:36,679 Speaker 2: won't know what you're gonna say, but we'll trust it's 30 00:01:36,720 --> 00:01:39,320 Speaker 2: something like good day, good evening, or whatever the case 31 00:01:39,360 --> 00:01:39,560 Speaker 2: may be. 32 00:01:39,640 --> 00:01:41,880 Speaker 5: Go ahead, Kathy, you know what I will say. I'll 33 00:01:41,920 --> 00:01:44,440 Speaker 5: be more polite than that. I will say thank you 34 00:01:44,520 --> 00:01:47,520 Speaker 5: since that word was drummed into my head when I 35 00:01:47,600 --> 00:01:49,320 Speaker 5: was a child. Got a mahugate. 36 00:01:50,040 --> 00:01:51,400 Speaker 3: Okay, you're welcome. 37 00:01:53,440 --> 00:01:57,160 Speaker 2: So, Kathy, in the slightest chance there is a viewer 38 00:01:57,280 --> 00:02:00,560 Speaker 2: or listener out there that is not from familiar with 39 00:02:00,600 --> 00:02:02,280 Speaker 2: what you guys do, could you just give us a 40 00:02:02,400 --> 00:02:05,279 Speaker 2: very brief overview of how. 41 00:02:05,160 --> 00:02:06,720 Speaker 3: Many strategies do you guys run? 42 00:02:06,760 --> 00:02:06,920 Speaker 4: Now? 43 00:02:07,680 --> 00:02:10,200 Speaker 2: How much is AUM at the moment? Essentially, what are 44 00:02:10,240 --> 00:02:11,079 Speaker 2: you guys busy with? 45 00:02:12,120 --> 00:02:16,480 Speaker 5: Right? Our invest I founded it in twenty fourteen, so 46 00:02:16,480 --> 00:02:20,640 Speaker 5: we're ten years old. We are approaching twenty five billion 47 00:02:20,720 --> 00:02:25,280 Speaker 5: dollars in assets under management in the world of ETFs. 48 00:02:25,400 --> 00:02:28,760 Speaker 5: We have eight ETFs here in the United States, but 49 00:02:28,800 --> 00:02:33,320 Speaker 5: we also subadvise for Nico Asset Management a number of 50 00:02:33,400 --> 00:02:38,840 Speaker 5: mutual funds in Japan. So we've actually gotten some interesting 51 00:02:39,120 --> 00:02:42,639 Speaker 5: ETF ideas here in the United States from our partner 52 00:02:43,000 --> 00:02:46,639 Speaker 5: in Japan. In two thousand I think it was twenty sixteen, 53 00:02:47,320 --> 00:02:51,400 Speaker 5: Japan saw or Nico saw fintech on our roadmap for 54 00:02:51,480 --> 00:02:55,200 Speaker 5: two thousand nineteen, I think, and they wanted it right away, 55 00:02:55,200 --> 00:02:58,639 Speaker 5: and they were right because of course, back then China 56 00:02:58,760 --> 00:03:02,040 Speaker 5: with we chat pay was teaching all of us how 57 00:03:02,120 --> 00:03:04,160 Speaker 5: the world was going to work, and so it was 58 00:03:04,200 --> 00:03:06,840 Speaker 5: a very good time to start that fund perfect. 59 00:03:07,000 --> 00:03:09,800 Speaker 2: I was hoping to just pick your brain and understand, 60 00:03:09,960 --> 00:03:13,760 Speaker 2: you know, this concept of disruptive technology, which underscores a 61 00:03:13,760 --> 00:03:18,639 Speaker 2: lot of your investment thesis. By virtue of being disruptive, 62 00:03:18,880 --> 00:03:21,480 Speaker 2: there's also a component of not knowing really what it 63 00:03:21,560 --> 00:03:23,680 Speaker 2: is until it becomes disruptive. So how do you predict 64 00:03:23,720 --> 00:03:25,720 Speaker 2: into the future and how do you know what the 65 00:03:25,760 --> 00:03:26,440 Speaker 2: future looks like? 66 00:03:26,480 --> 00:03:27,840 Speaker 3: I guess it's the starting point there. 67 00:03:28,560 --> 00:03:33,080 Speaker 5: Well, we are focused on all new technology that is evolving, 68 00:03:33,760 --> 00:03:36,680 Speaker 5: and we are focused, as Rebecca said, on the five 69 00:03:36,920 --> 00:03:41,480 Speaker 5: major innovation platforms that have been evolving believe it or 70 00:03:41,520 --> 00:03:45,920 Speaker 5: not since the late nineties. So the tech and telecom 71 00:03:46,000 --> 00:03:50,560 Speaker 5: bubble was the dream. Today is the reality that bubble 72 00:03:50,640 --> 00:03:55,920 Speaker 5: happened for many companies. You know, ten, fifteen, twenty, twenty 73 00:03:55,920 --> 00:03:59,880 Speaker 5: five years too early. Now we're in the middle of 74 00:04:00,000 --> 00:04:03,720 Speaker 5: time time for a lot of these themes. Certainly energy 75 00:04:03,760 --> 00:04:09,560 Speaker 5: storage evs. EV's are taking massive share, right, but so 76 00:04:09,720 --> 00:04:12,600 Speaker 5: far they account for a little more than ten percent 77 00:04:12,640 --> 00:04:15,119 Speaker 5: of total vehicle sales around the world. We think that's 78 00:04:15,160 --> 00:04:18,960 Speaker 5: going to seventy five percent in the next five years. Now, 79 00:04:19,120 --> 00:04:24,040 Speaker 5: why do we say that the centerpiece of our research 80 00:04:25,000 --> 00:04:30,960 Speaker 5: is Right's law. Right's law is a relative of Moore's law. 81 00:04:31,200 --> 00:04:35,920 Speaker 5: So many people know Moore's law in the semiconductor space, right, 82 00:04:36,440 --> 00:04:39,400 Speaker 5: and it basically says, you know, you get twice the 83 00:04:39,520 --> 00:04:42,640 Speaker 5: power for the same cost every eighteen months to two 84 00:04:42,760 --> 00:04:46,799 Speaker 5: years in the world of semiconductors. Right, that's a function 85 00:04:46,880 --> 00:04:51,560 Speaker 5: of time. Rights law, however, is a function of units, 86 00:04:52,360 --> 00:04:56,680 Speaker 5: and it says for every cumulative doubling in the number 87 00:04:56,720 --> 00:05:01,000 Speaker 5: of units produced of a given technology, cost decline at 88 00:05:01,040 --> 00:05:06,000 Speaker 5: a consistent percentage rate. So this is very important because 89 00:05:06,839 --> 00:05:11,040 Speaker 5: when costs fall and those are passed through into prices, 90 00:05:11,760 --> 00:05:15,000 Speaker 5: then access to new technologies is opened up to more 91 00:05:15,000 --> 00:05:19,920 Speaker 5: and more sectors. And we're looking for very big opportunities 92 00:05:19,920 --> 00:05:23,920 Speaker 5: that are going to scale across sectors, and we're also 93 00:05:24,000 --> 00:05:27,600 Speaker 5: looking for platforms that are going to become launching pads 94 00:05:27,640 --> 00:05:31,040 Speaker 5: for other technologies. So to give you an example of 95 00:05:31,080 --> 00:05:36,680 Speaker 5: that in the world of multiomics that sounds complicated, it's 96 00:05:36,720 --> 00:05:42,839 Speaker 5: life sciences, But until very recently, it has been prohibitively 97 00:05:42,960 --> 00:05:47,800 Speaker 5: expensive to sequence the human genome. Now we can for 98 00:05:48,400 --> 00:05:52,320 Speaker 5: around two hundred to four hundred dollars. Twenty years ago 99 00:05:52,360 --> 00:05:55,600 Speaker 5: that was two point seven billion dollars. We've come a 100 00:05:55,640 --> 00:05:59,679 Speaker 5: long way now that we can sequence the six billion 101 00:05:59,720 --> 00:06:05,760 Speaker 5: bits of code inside our genome. We're able to isolate mutations, 102 00:06:06,279 --> 00:06:10,919 Speaker 5: and so what has happened because of that sequencing technology 103 00:06:10,960 --> 00:06:15,440 Speaker 5: has been a launching pad for crisper gene editing. So 104 00:06:16,040 --> 00:06:21,280 Speaker 5: these technologies didn't exist, and they're truly disruptive in the 105 00:06:21,320 --> 00:06:26,080 Speaker 5: sense that with gene editing we can now cure disease right, 106 00:06:26,520 --> 00:06:29,760 Speaker 5: we have not been able to cure diseases before. That's 107 00:06:29,839 --> 00:06:33,040 Speaker 5: one of the ways we describe truly disruptive innovation. 108 00:06:33,680 --> 00:06:37,520 Speaker 2: Your investment horizon, by virtue of what you just described, 109 00:06:37,560 --> 00:06:40,160 Speaker 2: seems to be that you need to look at returns 110 00:06:40,200 --> 00:06:43,640 Speaker 2: over a much longer period of time. And yes, seeming 111 00:06:43,839 --> 00:06:48,240 Speaker 2: paradox that you're operating in this market that judges you 112 00:06:48,320 --> 00:06:51,880 Speaker 2: based on a yearly, if not even quarterly basis in 113 00:06:51,960 --> 00:06:55,279 Speaker 2: terms of returns. I have to note, of course, the 114 00:06:55,279 --> 00:06:56,960 Speaker 2: market's not extremely well this year. 115 00:06:57,040 --> 00:06:58,960 Speaker 3: You guys have underperformed the market. 116 00:06:59,680 --> 00:07:02,800 Speaker 2: Over the longer term, maybe we can make a different assessment, 117 00:07:02,839 --> 00:07:05,400 Speaker 2: of course, but just give us a sense of how 118 00:07:05,440 --> 00:07:09,240 Speaker 2: you navigate that space on keeping investors patient when this 119 00:07:09,360 --> 00:07:11,920 Speaker 2: is really not a business of patients. 120 00:07:12,240 --> 00:07:15,040 Speaker 5: It isn't a business of patients. And I'll take you 121 00:07:15,120 --> 00:07:18,520 Speaker 5: back to twenty twenty and twenty one when we could 122 00:07:18,560 --> 00:07:23,480 Speaker 5: do no wrong during COVID because technology innovation was solving 123 00:07:23,520 --> 00:07:27,280 Speaker 5: big problems back then, so our kind of strategy was 124 00:07:27,440 --> 00:07:32,520 Speaker 5: really in vogue and the inflows were incredible to the 125 00:07:32,560 --> 00:07:35,560 Speaker 5: point where I said, please keep some powder dry, especially 126 00:07:35,600 --> 00:07:37,760 Speaker 5: towards the end of twenty twenty and early twenty one, 127 00:07:38,560 --> 00:07:41,280 Speaker 5: and yet we couldn't fire our clients and we couldn't 128 00:07:41,320 --> 00:07:45,120 Speaker 5: prevent the inflows in the ETF world, right, and so 129 00:07:45,800 --> 00:07:49,000 Speaker 5: we ended up in twenty one and twenty two when 130 00:07:49,080 --> 00:07:51,800 Speaker 5: first the whiff of interest rates going up and then 131 00:07:51,840 --> 00:07:56,560 Speaker 5: the reality of interest rates going up crucified long duration assets. 132 00:07:57,120 --> 00:08:01,200 Speaker 5: Long bonds had their worst performance since the seventeen hundreds 133 00:08:01,720 --> 00:08:05,280 Speaker 5: if we can measure back that far, and our strategy 134 00:08:05,640 --> 00:08:07,960 Speaker 5: was going to have a very difficult time in that 135 00:08:08,080 --> 00:08:10,960 Speaker 5: kind of an environment. And of course it's because interest 136 00:08:11,040 --> 00:08:14,000 Speaker 5: rates went up twenty two fold, record breaking. We had 137 00:08:14,080 --> 00:08:17,640 Speaker 5: never seen that in not much more than a year's time, 138 00:08:18,360 --> 00:08:22,560 Speaker 5: so those two years were extremely difficult. Twenty three, on 139 00:08:22,600 --> 00:08:26,720 Speaker 5: the other hand, the width of falling interest rates, we 140 00:08:26,800 --> 00:08:30,160 Speaker 5: had a wonderful year. It was quite lumpy, but we 141 00:08:30,320 --> 00:08:36,240 Speaker 5: significantly outperformed the broad based benchmarks this year. As those 142 00:08:36,559 --> 00:08:40,040 Speaker 5: six interest rate cuts which were expected at the end 143 00:08:40,720 --> 00:08:44,360 Speaker 5: of December for twenty four, as those were taken off 144 00:08:44,360 --> 00:08:48,079 Speaker 5: the table. Again, as you say, very short term oriented 145 00:08:48,160 --> 00:08:51,720 Speaker 5: shareholders just you know, they took their profits from last 146 00:08:51,760 --> 00:08:56,080 Speaker 5: year and they redeployed them elsewhere. So, yes, we've been 147 00:08:56,120 --> 00:08:59,600 Speaker 5: seeing outflows, and I think this is the flip side 148 00:08:59,600 --> 00:09:03,520 Speaker 5: of where we were in late twenty and early twenty one. 149 00:09:04,160 --> 00:09:08,120 Speaker 5: Obviously we disagree with outflows, just like I said, keep 150 00:09:08,160 --> 00:09:10,960 Speaker 5: your powder dry at the top, and that was the 151 00:09:11,080 --> 00:09:16,959 Speaker 5: right advice. Now we're saying leaving our strategy now right before. 152 00:09:17,160 --> 00:09:20,000 Speaker 5: We believe interest rates are going to come down and 153 00:09:20,080 --> 00:09:23,640 Speaker 5: going to come down more dramatically than most people think, 154 00:09:23,679 --> 00:09:27,600 Speaker 5: because our economy is much weaker and inflation we think 155 00:09:27,679 --> 00:09:30,800 Speaker 5: is going to be much lower than expected. Now would 156 00:09:30,840 --> 00:09:33,840 Speaker 5: be the wrong time to sell our strategy. What we 157 00:09:33,920 --> 00:09:36,880 Speaker 5: do say to clients is truth will win out. If 158 00:09:36,880 --> 00:09:41,880 Speaker 5: we are right, then we believe that truly disruptive innovation 159 00:09:42,000 --> 00:09:46,480 Speaker 5: will scale from roughly fifteen trillion dollars in the global 160 00:09:46,520 --> 00:09:49,320 Speaker 5: equity markets, which is roughly where it is right now 161 00:09:49,360 --> 00:09:53,080 Speaker 5: a little more than ten percent of global equity markets 162 00:09:53,679 --> 00:09:58,079 Speaker 5: to two hundred and twenty trillion by twenty thirty. If 163 00:09:58,080 --> 00:10:01,520 Speaker 5: we're right, that will be leaving a lot of appreciation 164 00:10:01,679 --> 00:10:05,280 Speaker 5: on the table. Now, we do need the macros to 165 00:10:05,400 --> 00:10:09,040 Speaker 5: come our way, and we think they will so. 166 00:10:09,400 --> 00:10:13,000 Speaker 4: ARC has a very different definition of what disruptive technology is. 167 00:10:13,360 --> 00:10:16,880 Speaker 4: Tesla is your largest holding in your flagship ARC Innovation 168 00:10:17,000 --> 00:10:19,800 Speaker 4: Fund at sixteen percent, followed by Roku at nine percent, 169 00:10:19,840 --> 00:10:23,560 Speaker 4: Coinbase eight percent, in Roadblock six percent. So what's your 170 00:10:23,640 --> 00:10:27,320 Speaker 4: veal on scaling up and the monetization of this business 171 00:10:27,360 --> 00:10:28,800 Speaker 4: and what are some of the hurdles. 172 00:10:29,520 --> 00:10:35,960 Speaker 5: So we believe Tesla is the largest AI project, and 173 00:10:36,040 --> 00:10:39,880 Speaker 5: I say that believing they will complete the project on Earth. 174 00:10:40,160 --> 00:10:47,360 Speaker 5: Autonomous taxi platforms are the biggest AI project evolving today, 175 00:10:48,040 --> 00:10:51,320 Speaker 5: and we believe this will be an eight to ten 176 00:10:51,400 --> 00:10:57,280 Speaker 5: trillion dollar global revenue opportunity for the entire ecosystem, with 177 00:10:57,480 --> 00:11:03,000 Speaker 5: the taxi network providers the autonomous network providers taking half 178 00:11:03,080 --> 00:11:07,040 Speaker 5: of that. We believe this is a winner take most project, 179 00:11:07,600 --> 00:11:11,439 Speaker 5: meaning the company getting people from point A to point 180 00:11:11,440 --> 00:11:15,880 Speaker 5: B the safest and the quickest, that company is probably 181 00:11:15,880 --> 00:11:17,760 Speaker 5: going to win the lion's share of the market. We 182 00:11:17,840 --> 00:11:22,199 Speaker 5: believe in the US that is Tesla. Interestingly, we've been 183 00:11:22,240 --> 00:11:26,920 Speaker 5: focused on China thinking Tesla would not be granted much 184 00:11:26,920 --> 00:11:30,600 Speaker 5: of an opportunity for autonomous in China, and as you 185 00:11:30,720 --> 00:11:34,720 Speaker 5: probably know, they have been allowed in China. And what 186 00:11:34,800 --> 00:11:40,239 Speaker 5: we've learned from Baidu recently Apollo it's autonomous driving platform, 187 00:11:40,880 --> 00:11:47,400 Speaker 5: it has shifted its strategy to Tesla's strategy exactly, which 188 00:11:47,440 --> 00:11:51,520 Speaker 5: is very interesting to us. It seems that Tesla could 189 00:11:51,559 --> 00:11:55,640 Speaker 5: get more of the opportunity in China than we once thought. 190 00:11:56,120 --> 00:11:58,920 Speaker 5: So if you look at the way Tesla is valued, 191 00:11:59,320 --> 00:12:03,120 Speaker 5: it is no longer valued just an ev manufacturer. So 192 00:12:03,360 --> 00:12:07,440 Speaker 5: some of this autonomous opportunity is getting into the stock. 193 00:12:07,880 --> 00:12:12,160 Speaker 5: And yet if we are right, the stock has miles 194 00:12:12,200 --> 00:12:16,040 Speaker 5: to go, and we've been valuing primarily the autonomous opportunity 195 00:12:16,120 --> 00:12:19,160 Speaker 5: and we see a tenfold increase. Why is that It 196 00:12:19,280 --> 00:12:24,320 Speaker 5: is because Tesla will shift from an auto manufacturer, so 197 00:12:24,400 --> 00:12:27,920 Speaker 5: that's one and done. You sell the car, and with 198 00:12:28,000 --> 00:12:32,479 Speaker 5: the full self driving software, that's pretty much the commercial opportunity. 199 00:12:33,160 --> 00:12:36,560 Speaker 5: With autonomous it will shift from one and done to 200 00:12:36,679 --> 00:12:41,240 Speaker 5: a recurring revenue. SaaS like model Tesla will take a 201 00:12:41,360 --> 00:12:47,280 Speaker 5: piece of the action from every fleet owner using its network, 202 00:12:47,360 --> 00:12:51,080 Speaker 5: deploying on its network, and we think they could take 203 00:12:51,160 --> 00:12:56,400 Speaker 5: anywhere from thirty to fifty percent of the economics of 204 00:12:56,440 --> 00:12:59,920 Speaker 5: the top line from the fleet owners, and the margins 205 00:13:00,040 --> 00:13:04,880 Speaker 5: associated with that will be more like north of fifty percent. 206 00:13:05,040 --> 00:13:08,840 Speaker 5: I mean SaaS margins are in the eighties and nineties. 207 00:13:09,200 --> 00:13:12,560 Speaker 5: You combine that with their electric vehicle right now, those 208 00:13:12,600 --> 00:13:16,360 Speaker 5: gross margins are sixteen percent. The blended margin is going 209 00:13:16,440 --> 00:13:19,200 Speaker 5: to be well above fifty percent. That is what we 210 00:13:19,280 --> 00:13:22,040 Speaker 5: think people are missing, the size of the opportunity, how 211 00:13:22,120 --> 00:13:24,400 Speaker 5: quickly it's going to scale, and how profitable it's going 212 00:13:24,440 --> 00:13:24,640 Speaker 5: to be. 213 00:13:25,520 --> 00:13:28,440 Speaker 2: Right, you have a sixteen percent based on what Rebecca said, 214 00:13:28,440 --> 00:13:31,280 Speaker 2: sixteen percent of the flagship ark innovation is Tesla. 215 00:13:31,320 --> 00:13:34,200 Speaker 3: I mean, are you looking to increase that weighting? 216 00:13:34,360 --> 00:13:37,240 Speaker 2: Is there a maximum cap within the strategy for a 217 00:13:37,280 --> 00:13:39,320 Speaker 2: single stock and then let's take it from there. 218 00:13:39,840 --> 00:13:43,160 Speaker 5: Sure, so we can buy any stock up to ten 219 00:13:43,240 --> 00:13:46,280 Speaker 5: percent of the portfolio. We cannot buy beyond, but we 220 00:13:46,360 --> 00:13:50,880 Speaker 5: can let a stock ride. Now normally we will start 221 00:13:50,920 --> 00:13:56,520 Speaker 5: taking profits well before sixteen percent. But the reason we 222 00:13:56,640 --> 00:14:00,120 Speaker 5: have let Tesla run. We've taken some profits, but let 223 00:14:00,200 --> 00:14:02,640 Speaker 5: it run to a higher percentage than normal of the 224 00:14:02,679 --> 00:14:07,600 Speaker 5: portfolio is because that Tesla is about to give us 225 00:14:07,600 --> 00:14:12,600 Speaker 5: a lot more information about the robotaxi opportunity. Now. Originally 226 00:14:13,200 --> 00:14:15,680 Speaker 5: August eighth, so eight eight and I know in China 227 00:14:15,720 --> 00:14:17,319 Speaker 5: that's a very lucky number. 228 00:14:18,000 --> 00:14:18,880 Speaker 3: Double infinity. 229 00:14:19,640 --> 00:14:22,280 Speaker 5: Yeah, there you go, there you go. And we thought 230 00:14:22,360 --> 00:14:25,080 Speaker 5: that's why Elon chose the date at first it was 231 00:14:25,400 --> 00:14:30,640 Speaker 5: August eighth. Today they pushed it to October, and that 232 00:14:30,880 --> 00:14:35,080 Speaker 5: just tells me that we're probably getting closer to this 233 00:14:35,240 --> 00:14:40,200 Speaker 5: robo taxi opportunity. He wants to show us something more 234 00:14:40,960 --> 00:14:44,400 Speaker 5: I think awe inspiring than we might have seen on 235 00:14:44,600 --> 00:14:48,120 Speaker 5: August eighth, and he believes as possible by October. And 236 00:14:48,160 --> 00:14:51,040 Speaker 5: what does that mean. It means that analysts will have 237 00:14:51,160 --> 00:14:53,760 Speaker 5: to build into their models. They'll have to think about this. 238 00:14:53,840 --> 00:14:56,840 Speaker 5: Wait a minute, this is no longer a one and done. 239 00:14:57,000 --> 00:15:01,760 Speaker 5: This is a recurring revenue model with explosive cash flows. Oh. 240 00:15:01,800 --> 00:15:07,760 Speaker 5: By the way, ARCS model suggests there's significant upside if 241 00:15:08,000 --> 00:15:11,720 Speaker 5: Elon and team deliver on this opportunity. And this is 242 00:15:11,720 --> 00:15:17,240 Speaker 5: truly disruptive, meaning transportation will go autonomous, and not just 243 00:15:17,640 --> 00:15:23,360 Speaker 5: electric passenger vehicles, but trucks and drones. This is going 244 00:15:23,440 --> 00:15:27,880 Speaker 5: to create a completely new world for transportation. And even 245 00:15:27,920 --> 00:15:31,520 Speaker 5: in the case of rails, I know Warren Buffett has 246 00:15:31,800 --> 00:15:35,200 Speaker 5: had a constant exposure to rails because they've been so 247 00:15:35,320 --> 00:15:39,960 Speaker 5: dependable as investments over the years. But autonomous trucks, we 248 00:15:40,080 --> 00:15:44,800 Speaker 5: believe could undercut rail pricing and go point to point, 249 00:15:45,160 --> 00:15:50,040 Speaker 5: which rail cannot do. And so rails. Ultimately out there 250 00:15:50,080 --> 00:15:53,520 Speaker 5: somewhere could be stuck with stranded assets unless they turn 251 00:15:53,600 --> 00:15:56,040 Speaker 5: them into high speed passenger. 252 00:15:56,600 --> 00:15:58,800 Speaker 2: Yeah, it looks like we'll need to build more highways 253 00:15:58,840 --> 00:16:01,560 Speaker 2: than if that comes out to be true. I know 254 00:16:01,680 --> 00:16:04,120 Speaker 2: you speak with Elon. I know he's a friend. If 255 00:16:04,120 --> 00:16:06,960 Speaker 2: I'm not mistaken, I'm wondering how you look at this. 256 00:16:07,560 --> 00:16:09,120 Speaker 2: I'm wondering if you could give us an insight on 257 00:16:09,160 --> 00:16:11,840 Speaker 2: how Elon might look at this to key Man Risk, right, 258 00:16:11,920 --> 00:16:15,160 Speaker 2: if something were to happen to Elon Musk, what happens 259 00:16:15,160 --> 00:16:16,480 Speaker 2: with the project and how do you look at that 260 00:16:16,520 --> 00:16:17,960 Speaker 2: from an investment point of view. 261 00:16:18,640 --> 00:16:21,600 Speaker 5: I think many people would be surprised at how little 262 00:16:22,080 --> 00:16:24,800 Speaker 5: Elon and I speak, and that's kind of by design. 263 00:16:24,960 --> 00:16:28,480 Speaker 5: We have great relationship with the broader team, the financial 264 00:16:28,520 --> 00:16:31,320 Speaker 5: people and so forth. We speak to him occasionally. 265 00:16:31,320 --> 00:16:31,720 Speaker 2: We did a. 266 00:16:31,680 --> 00:16:36,320 Speaker 5: Podcast with him if Autonomous, We're not as far along 267 00:16:36,600 --> 00:16:39,360 Speaker 5: as it is. What I'm telling you is three years 268 00:16:39,400 --> 00:16:43,080 Speaker 5: ago key Man Risk, in terms of where we think 269 00:16:43,160 --> 00:16:46,480 Speaker 5: the stock is going, might have been more significant. But 270 00:16:46,880 --> 00:16:52,800 Speaker 5: the talent that Elon has attracted to Tesla. This is 271 00:16:52,840 --> 00:16:56,240 Speaker 5: a very hard AI project and the best engineers in 272 00:16:56,240 --> 00:17:00,680 Speaker 5: the world want to work on very high are difficult projects. 273 00:17:01,240 --> 00:17:05,800 Speaker 5: So the talent he has surrounded himself with is phenomenal, 274 00:17:06,200 --> 00:17:09,520 Speaker 5: and we think we're almost there. In terms of autonomous 275 00:17:09,800 --> 00:17:13,960 Speaker 5: Elon is a visionary. He is our renaissance man. I 276 00:17:14,000 --> 00:17:17,040 Speaker 5: don't think there are many other executives out there who 277 00:17:17,160 --> 00:17:22,119 Speaker 5: are able to deal with the convergences that are taking 278 00:17:22,160 --> 00:17:26,400 Speaker 5: place between and among technologies. This is why we never 279 00:17:26,520 --> 00:17:30,600 Speaker 5: called Tesla an auto company. We called it a robot company, 280 00:17:30,840 --> 00:17:35,600 Speaker 5: an energy storage company, an artificial intelligence company. Tesla has 281 00:17:35,720 --> 00:17:39,399 Speaker 5: never been an auto company. It is one of the 282 00:17:39,440 --> 00:17:45,919 Speaker 5: most profound examples of how the convergences among technologies are 283 00:17:45,960 --> 00:17:50,959 Speaker 5: going to manifest into super exponential growth long term. 284 00:17:51,440 --> 00:17:54,600 Speaker 2: You also mentioned earlier on this is almost winner takes most. 285 00:17:54,640 --> 00:17:56,840 Speaker 2: If I'm not mistaken, how you phrased that winter takes most. 286 00:17:57,000 --> 00:17:59,600 Speaker 2: Conversation Tesla and China. I need to ask you about 287 00:17:59,640 --> 00:18:02,520 Speaker 2: b White Tesla or BYD. Do I have to pick 288 00:18:02,520 --> 00:18:04,480 Speaker 2: between it? Is that a binary choice? How would you 289 00:18:04,520 --> 00:18:04,960 Speaker 2: approach that? 290 00:18:05,359 --> 00:18:07,440 Speaker 5: No, I don't think so. I don't think so. What's 291 00:18:07,480 --> 00:18:13,120 Speaker 5: been great about BYD and its success is very important 292 00:18:13,359 --> 00:18:19,360 Speaker 5: to our analysis of cost declines of technologies including electric 293 00:18:19,480 --> 00:18:25,240 Speaker 5: drive trains is units. As I mentioned, Right's law, BYD 294 00:18:26,000 --> 00:18:31,240 Speaker 5: has been very helpful in scaling the units in evs 295 00:18:31,280 --> 00:18:35,800 Speaker 5: to help costs come down. So BYD actually has helped 296 00:18:36,000 --> 00:18:41,600 Speaker 5: Tesla and all electric vehicle manufacturers because electric drive train 297 00:18:41,760 --> 00:18:45,920 Speaker 5: costs continue to come down as units scale. So it's 298 00:18:45,960 --> 00:18:48,439 Speaker 5: not either or, and in fact we own both. We 299 00:18:48,520 --> 00:18:51,359 Speaker 5: own both in one of our ETFs. Of course, in 300 00:18:51,400 --> 00:18:54,520 Speaker 5: the flagship ETF we own Tesla. Our flagship has to 301 00:18:54,560 --> 00:18:57,679 Speaker 5: be exposed to all technologies, so we're more careful there 302 00:18:57,880 --> 00:18:58,880 Speaker 5: in terms of selection. 303 00:19:00,560 --> 00:19:05,240 Speaker 1: This podcast is brought to you by HKX Asia's ETF marketplace, 304 00:19:05,680 --> 00:19:08,560 Speaker 1: where you can get exposure to themes ranging from AI 305 00:19:08,720 --> 00:19:12,840 Speaker 1: to virtual assets, small cap to large cap Greater China 306 00:19:13,040 --> 00:19:18,040 Speaker 1: to global search. HKX to learn more, let's. 307 00:19:17,880 --> 00:19:21,560 Speaker 4: Shift geared to crypto. Your spot Bitcoin ETF is now 308 00:19:21,600 --> 00:19:24,680 Speaker 4: the fourth largest by assets under management with almost three 309 00:19:24,720 --> 00:19:28,440 Speaker 4: billion in assets, and a lot of your best performing 310 00:19:28,520 --> 00:19:31,440 Speaker 4: funds this year are in the crypto space, whether it's blockchain, 311 00:19:31,560 --> 00:19:35,400 Speaker 4: digital economy, ethereum, and I think when we last spoke 312 00:19:35,400 --> 00:19:37,720 Speaker 4: about a year ago, you mentioned that bitcoin could reach 313 00:19:37,720 --> 00:19:41,719 Speaker 4: as high as one point five million dollars by twenty thirty. 314 00:19:42,200 --> 00:19:44,479 Speaker 4: So what are you seeing in the crypto space? Can 315 00:19:44,560 --> 00:19:46,639 Speaker 4: just share any insights into the crypto space and what 316 00:19:46,680 --> 00:19:47,480 Speaker 4: you want people to know. 317 00:19:48,880 --> 00:19:51,920 Speaker 5: Sure, it's a little bit of a David Goliath story. 318 00:19:52,119 --> 00:19:57,000 Speaker 5: Great scale was the BMTH and it donated a lot 319 00:19:57,040 --> 00:20:02,440 Speaker 5: of share to black Rock and and us we're number 320 00:20:02,480 --> 00:20:07,960 Speaker 5: three of the recipients of that share, so we're very 321 00:20:08,000 --> 00:20:11,200 Speaker 5: proud of that. As far as where we think bitcoin 322 00:20:11,280 --> 00:20:14,439 Speaker 5: is going to go, yes, that one point five million 323 00:20:14,720 --> 00:20:18,880 Speaker 5: is our bowl case. Our base case is in the 324 00:20:19,000 --> 00:20:23,280 Speaker 5: six hundred and fifty thousand dollars range. With the approval 325 00:20:23,480 --> 00:20:27,160 Speaker 5: of the spot Bitcoin ETFs, the probability of our bowl 326 00:20:27,240 --> 00:20:30,919 Speaker 5: case went up dramatically. This is a new asset class. 327 00:20:30,960 --> 00:20:35,800 Speaker 5: So not only is bitcoin a global rules based digital 328 00:20:35,920 --> 00:20:40,040 Speaker 5: private monetary system first time in history, a very big idea. 329 00:20:40,720 --> 00:20:44,280 Speaker 5: Not is it only a technology, but it is also 330 00:20:44,320 --> 00:20:50,440 Speaker 5: a new asset class that institutions must consider. Why because 331 00:20:50,600 --> 00:20:55,239 Speaker 5: new asset classes mean the correlation of returns with this 332 00:20:55,359 --> 00:21:00,240 Speaker 5: new asset class are low relative to other asset class, 333 00:21:00,800 --> 00:21:05,119 Speaker 5: and so institutions and others have a fiduciary responsibility to 334 00:21:05,320 --> 00:21:11,160 Speaker 5: consider Bitcoin and other crypto assets because the low correlations 335 00:21:11,200 --> 00:21:15,119 Speaker 5: mean that over the long term, the returns per unit 336 00:21:15,160 --> 00:21:18,680 Speaker 5: of risk will be higher, and so I think institutions 337 00:21:18,760 --> 00:21:22,040 Speaker 5: are thinking very carefully about this. They have to, They 338 00:21:22,040 --> 00:21:24,399 Speaker 5: have a fiduciary responsibility to do so. 339 00:21:25,160 --> 00:21:27,000 Speaker 3: Is the unbridled bullishness? 340 00:21:27,320 --> 00:21:30,840 Speaker 2: Correct me if I'm mischaracterizing your views on bitcoin specifically. 341 00:21:30,960 --> 00:21:33,639 Speaker 2: Is that limited to bitcoin or like? At some point 342 00:21:33,720 --> 00:21:35,720 Speaker 2: is Ether going to be part of the conversation. Why 343 00:21:35,760 --> 00:21:38,119 Speaker 2: did you guys file for an Ether spot ETF, for example, 344 00:21:38,240 --> 00:21:38,840 Speaker 2: or do you have plan? 345 00:21:38,880 --> 00:21:42,960 Speaker 5: Do we actually have private funds? One is one we 346 00:21:43,040 --> 00:21:47,240 Speaker 5: call the Crypto Asset Revolution Fund, and so that has 347 00:21:47,359 --> 00:21:51,320 Speaker 5: about ten assets in it. We're still very selective. I 348 00:21:51,320 --> 00:21:55,160 Speaker 5: think there are thousands of crypto assets out there, so 349 00:21:55,320 --> 00:22:00,240 Speaker 5: ten suggests that we think very few are going to 350 00:22:00,400 --> 00:22:04,280 Speaker 5: scale very dramatically in the years ahead. In terms of Ether, 351 00:22:04,800 --> 00:22:07,400 Speaker 5: we believe in it. We think it's the leader in 352 00:22:07,600 --> 00:22:13,280 Speaker 5: the financial services revolution, and we're looking for better ways 353 00:22:13,600 --> 00:22:17,000 Speaker 5: to exploit it to unlock its full benefits. As you 354 00:22:17,160 --> 00:22:22,520 Speaker 5: probably notice that SEC is not going to allow staking, 355 00:22:23,240 --> 00:22:27,680 Speaker 5: so that is not really providing investors with the full 356 00:22:27,800 --> 00:22:31,040 Speaker 5: benefits that Ether will offer over time. So we're trying 357 00:22:31,080 --> 00:22:33,760 Speaker 5: to figure out how to pick our spots there, right. 358 00:22:33,840 --> 00:22:36,120 Speaker 2: You know, I need to ask you to interesting how 359 00:22:36,160 --> 00:22:39,240 Speaker 2: you split up the Mag seven into you know you 360 00:22:39,320 --> 00:22:42,000 Speaker 2: have I would imagine Tesla and then the Mag six, 361 00:22:42,560 --> 00:22:43,199 Speaker 2: and you mentioned it. 362 00:22:43,240 --> 00:22:45,320 Speaker 3: I need to ask you about Nvidia. And I don't 363 00:22:45,359 --> 00:22:47,240 Speaker 3: mean this. It's a gotcha question, right. 364 00:22:47,560 --> 00:22:50,000 Speaker 5: Believe me. I'm used to answering this question. 365 00:22:51,240 --> 00:22:53,920 Speaker 2: Yeah, Well, you know what wasn't obvious at that point 366 00:22:53,960 --> 00:22:56,240 Speaker 2: that we do know now that if we had a 367 00:22:56,320 --> 00:22:58,760 Speaker 2: little bit more information at that point, you wouldn't have sold. 368 00:22:58,800 --> 00:23:00,760 Speaker 2: I guess that is also part of what some of 369 00:23:00,800 --> 00:23:03,800 Speaker 2: your investors are asking and talking about concentration risk. 370 00:23:03,920 --> 00:23:05,560 Speaker 3: Where is our Nvidia in your strategy? 371 00:23:06,440 --> 00:23:11,040 Speaker 5: Well, Nvidia already has been to answer your question directly 372 00:23:11,160 --> 00:23:15,840 Speaker 5: about we sold and it has tripled since we were 373 00:23:15,920 --> 00:23:20,280 Speaker 5: in for the first one hundredfold increase, so we wrote 374 00:23:20,280 --> 00:23:24,560 Speaker 5: it up one hundredfold. Would we have liked another triple? Sure, 375 00:23:25,200 --> 00:23:28,520 Speaker 5: and if we had known it was going to triple, absolutely, 376 00:23:28,600 --> 00:23:32,800 Speaker 5: But here is the most important question for Nvidia to 377 00:23:33,000 --> 00:23:36,880 Speaker 5: deserve where it is right now. From a valuation point 378 00:23:36,920 --> 00:23:43,840 Speaker 5: of view, other companies out there must be having phenomenal results, 379 00:23:44,760 --> 00:23:49,840 Speaker 5: provocative results that are turbo charging them and moving forward 380 00:23:50,200 --> 00:23:52,520 Speaker 5: We think we have a lot of companies in our 381 00:23:52,560 --> 00:23:58,679 Speaker 5: portfolio that will enjoy the AI swish, but it is 382 00:23:58,720 --> 00:24:01,200 Speaker 5: going to take time, and I think that is what 383 00:24:01,720 --> 00:24:04,080 Speaker 5: a lot of people are missing. And this is what 384 00:24:04,119 --> 00:24:08,400 Speaker 5: we were thinking as we were selling in Vidia and 385 00:24:08,960 --> 00:24:13,240 Speaker 5: moving more into Tesla, right, I mean, or in the 386 00:24:13,480 --> 00:24:16,600 Speaker 5: early days when we sold in video, we bought Coinbase 387 00:24:16,720 --> 00:24:21,040 Speaker 5: because it was being destroyed by the SEC. Coinbase last 388 00:24:21,119 --> 00:24:24,040 Speaker 5: year did better than in Vidia. That doesn't mean we 389 00:24:24,880 --> 00:24:28,480 Speaker 5: wouldn't have loved to have owned Nvidia as well. I'm 390 00:24:28,520 --> 00:24:30,920 Speaker 5: not saying that. I'm just saying you have to look 391 00:24:31,200 --> 00:24:33,720 Speaker 5: to the other side of a cell. What did you 392 00:24:33,800 --> 00:24:37,920 Speaker 5: buy now? Why do I say it's going to take time? Well, 393 00:24:38,040 --> 00:24:42,159 Speaker 5: Palanteer itself, which is in our top ten, is telling 394 00:24:42,280 --> 00:24:47,000 Speaker 5: us that for large enterprises to be able to harness 395 00:24:47,200 --> 00:24:52,760 Speaker 5: AI in a profound way, meaning to transform their own business, 396 00:24:53,440 --> 00:24:57,040 Speaker 5: is going to take time. These are big ships and 397 00:24:57,119 --> 00:24:59,639 Speaker 5: what do they have to do. They have to gather 398 00:24:59,800 --> 00:25:03,800 Speaker 5: day from all around the world. These multinationals data that 399 00:25:03,840 --> 00:25:07,639 Speaker 5: they don't even know exists, are having trouble finding it, 400 00:25:07,760 --> 00:25:11,480 Speaker 5: centralize it, clean it up, integrate it. They're going to 401 00:25:11,480 --> 00:25:16,040 Speaker 5: have to map out their workflows throughout the enterprise, these 402 00:25:16,160 --> 00:25:21,119 Speaker 5: huge enterprises in excruciating detail, and then maybe they're ready 403 00:25:21,359 --> 00:25:24,080 Speaker 5: to harness AI. Sure, right now we can all write 404 00:25:24,119 --> 00:25:28,200 Speaker 5: emails and poems if that's what we do much more 405 00:25:28,320 --> 00:25:32,359 Speaker 5: quickly and effectively. But those aren't the profound uses of 406 00:25:32,400 --> 00:25:36,600 Speaker 5: AI to transform business models. Those are very useful from 407 00:25:36,640 --> 00:25:39,960 Speaker 5: productivity point of view. We're not dismissing them. But even 408 00:25:40,000 --> 00:25:43,400 Speaker 5: a company like UiPath, which is in our top ten, 409 00:25:44,040 --> 00:25:47,760 Speaker 5: its fourth quarter was up thirty one percent revenue growth, 410 00:25:48,160 --> 00:25:50,080 Speaker 5: and then all of a sudden, here is one of 411 00:25:50,400 --> 00:25:55,399 Speaker 5: a software provider which is using AI a federated model, 412 00:25:55,520 --> 00:25:59,199 Speaker 5: using all the foundation models out there, and building specialized 413 00:25:59,240 --> 00:26:03,200 Speaker 5: models to help companies with their workflows, which is the 414 00:26:03,240 --> 00:26:06,480 Speaker 5: first thing that's probably going to happen. And what happens. 415 00:26:07,040 --> 00:26:10,600 Speaker 5: It's growth rates slowed down from thirty one percent to 416 00:26:10,640 --> 00:26:12,800 Speaker 5: I think it was eighteen percent in the first quarter, 417 00:26:13,520 --> 00:26:16,560 Speaker 5: and the stock drop thirty percent? What was that all about? 418 00:26:16,720 --> 00:26:19,320 Speaker 5: That is what I just told you. Wait a minute, 419 00:26:19,480 --> 00:26:24,480 Speaker 5: there's a little bit of a paralysis here as strategic 420 00:26:24,560 --> 00:26:28,159 Speaker 5: decision makers say, wait a minute, this is important and 421 00:26:28,200 --> 00:26:31,000 Speaker 5: we need to figure this out, and we agree it's 422 00:26:31,040 --> 00:26:35,480 Speaker 5: going to be transformational, but it is going to take time, 423 00:26:35,920 --> 00:26:40,040 Speaker 5: and I think that a lot of Nvidia's move has 424 00:26:40,520 --> 00:26:45,240 Speaker 5: not incorporated the time it is going to take. All 425 00:26:45,280 --> 00:26:47,720 Speaker 5: I know is that Nvidia is one of the most 426 00:26:47,760 --> 00:26:52,359 Speaker 5: cyclical semiconductor companies that I have witnessed in my career. 427 00:26:52,720 --> 00:26:57,320 Speaker 5: So again, call it experience, call it stupidity, call it 428 00:26:57,400 --> 00:27:01,000 Speaker 5: whatever you want. There was a lot behind that decision. 429 00:27:02,400 --> 00:27:05,359 Speaker 4: So for our listeners who may not know, Kathy actually 430 00:27:05,359 --> 00:27:08,679 Speaker 4: bought in Video when it was just four dollars back 431 00:27:08,840 --> 00:27:11,560 Speaker 4: in twenty fourteen, and so you were definitely one of 432 00:27:11,600 --> 00:27:14,400 Speaker 4: the early investors into this. 433 00:27:15,040 --> 00:27:19,080 Speaker 5: Thank you, No, it was forty cents, Rebecca. We wrote 434 00:27:19,160 --> 00:27:23,280 Speaker 5: that pre ten for one split, so we bought it 435 00:27:23,560 --> 00:27:29,120 Speaker 5: at forty cents and we wrote it to almost forty dollars, 436 00:27:29,160 --> 00:27:32,080 Speaker 5: and then of course it tripled from there. But that's 437 00:27:32,119 --> 00:27:35,720 Speaker 5: one hundredfold increase and it is the number four contributor 438 00:27:36,080 --> 00:27:39,960 Speaker 5: from inception to date to our flagship funds performance. 439 00:27:40,920 --> 00:27:44,560 Speaker 4: So if we go beyond AI, keeping with the innovation theme, 440 00:27:45,000 --> 00:27:48,320 Speaker 4: what are some of your most compelling adjacent industry that 441 00:27:48,359 --> 00:27:51,240 Speaker 4: you're focused on. I know you guys have gene editing, 442 00:27:51,600 --> 00:27:56,000 Speaker 4: DNA sequencing, how do you find the speed of development changing, 443 00:27:56,040 --> 00:27:59,480 Speaker 4: and what's your expectation or forecast in these To David's point, 444 00:27:59,520 --> 00:28:01,639 Speaker 4: a lot of these companies may not even exist right 445 00:28:01,680 --> 00:28:04,480 Speaker 4: now or are only in its infancy state. 446 00:28:05,240 --> 00:28:10,800 Speaker 5: Sure, AI is going to impact every industry, every sector. 447 00:28:10,840 --> 00:28:16,040 Speaker 5: Every company we're now calling the multiomix revolution is probably 448 00:28:16,080 --> 00:28:20,200 Speaker 5: going to show some of the most profound results because 449 00:28:20,320 --> 00:28:24,040 Speaker 5: of artificial intelligence. We have six billion bits of code 450 00:28:24,520 --> 00:28:25,600 Speaker 5: in our genomes. 451 00:28:26,040 --> 00:28:26,240 Speaker 2: Right. 452 00:28:26,960 --> 00:28:33,080 Speaker 5: It was impossible until recently to understand what was going 453 00:28:33,240 --> 00:28:37,239 Speaker 5: wrong in those six billion bits of code because it 454 00:28:37,320 --> 00:28:42,800 Speaker 5: was too expensive to sequence our genomes, and so God 455 00:28:42,840 --> 00:28:46,320 Speaker 5: bless them, doctors didn't have the science or the knowledge 456 00:28:46,480 --> 00:28:49,160 Speaker 5: just it wasn't there. It was too expensive to get. 457 00:28:49,960 --> 00:28:54,000 Speaker 5: Now we have that information, six billion bits of code. 458 00:28:54,080 --> 00:29:01,360 Speaker 5: We've got all kinds of things going on in the body, DNA, RNA, proteins, epigenetics, metabolics. 459 00:29:01,880 --> 00:29:05,960 Speaker 5: It's the most complicated organism out there. Right. The only 460 00:29:06,000 --> 00:29:08,480 Speaker 5: way we're really going to understand it is with the 461 00:29:08,520 --> 00:29:13,640 Speaker 5: convergence between sequencing and artificial intelligence. And now that we 462 00:29:13,760 --> 00:29:18,480 Speaker 5: are seeing that we're able to diagnose cancer. And one 463 00:29:18,520 --> 00:29:22,160 Speaker 5: of the names in our venture fund, Freenome, is able 464 00:29:22,240 --> 00:29:26,480 Speaker 5: to diagnose colorectal cancer. It seems this is early days, 465 00:29:26,520 --> 00:29:30,040 Speaker 5: still in trials in stage one, and one of the 466 00:29:30,040 --> 00:29:34,000 Speaker 5: co founders until recently an advisor to ARC now having 467 00:29:34,120 --> 00:29:38,719 Speaker 5: joined ARC, Charlie Roberts leading a venture fund, now believes 468 00:29:38,760 --> 00:29:43,040 Speaker 5: we'll be able to diagnose cancer before stage one. Think 469 00:29:43,120 --> 00:29:48,080 Speaker 5: polyps in cholarectal cancer right, Those shed two into the bloodstream. 470 00:29:48,520 --> 00:29:49,120 Speaker 2: So with a. 471 00:29:49,080 --> 00:29:53,160 Speaker 5: Blood test, diagnosing cancer in stage one or before stage 472 00:29:53,200 --> 00:29:58,320 Speaker 5: one couldn't happen without artificial intelligence and sequencing. So in 473 00:29:58,360 --> 00:30:03,600 Speaker 5: the sequencing space, we own specific biosciences in the molecular 474 00:30:03,760 --> 00:30:08,680 Speaker 5: diagnostic space Vericite, which uses artificial intelligence when it comes 475 00:30:08,680 --> 00:30:12,840 Speaker 5: to prostate cancer, thyroid cancer, and so forth. We have 476 00:30:13,280 --> 00:30:19,080 Speaker 5: names like Recursion Therapeutics, and Nvidia invested in Recursion Therapeutics. 477 00:30:19,640 --> 00:30:24,640 Speaker 5: Recursion is in the drug discovery area, and it is collapsing. 478 00:30:24,720 --> 00:30:29,200 Speaker 5: The time and the cost to discover new targets for 479 00:30:29,680 --> 00:30:34,120 Speaker 5: therapies to address. The numbers are astonishing, like AI itself is. 480 00:30:34,240 --> 00:30:37,200 Speaker 5: But what's so interesting the reason you're not hearing a 481 00:30:37,200 --> 00:30:41,640 Speaker 5: lot about this and the reason we're investing aggressively in it, 482 00:30:41,720 --> 00:30:45,480 Speaker 5: is AI is going to create these cures if we 483 00:30:45,560 --> 00:30:51,120 Speaker 5: didn't have that convergence of sequencing technologies AI and now 484 00:30:51,240 --> 00:30:54,400 Speaker 5: Crisper gene editing, we would not be able to cure disease. 485 00:30:55,440 --> 00:30:59,000 Speaker 2: Interesting, so catching it early seems to be for all 486 00:30:59,040 --> 00:31:02,840 Speaker 2: things cancers has to be the secret sauce and for investing. 487 00:31:02,840 --> 00:31:05,160 Speaker 2: I'm curious to get your take. It seems like for 488 00:31:05,240 --> 00:31:08,280 Speaker 2: you to catch these early winners, do you have to 489 00:31:08,320 --> 00:31:10,960 Speaker 2: go early into the public market space or do you 490 00:31:11,000 --> 00:31:14,440 Speaker 2: have to even go further before they become public companies? 491 00:31:14,480 --> 00:31:16,840 Speaker 2: As an investor that picks and chooses where to play 492 00:31:16,840 --> 00:31:19,560 Speaker 2: your game, is it easier to go into the private 493 00:31:19,600 --> 00:31:20,880 Speaker 2: markets at better valuations? 494 00:31:20,880 --> 00:31:22,080 Speaker 3: Where are the better deals right now? 495 00:31:22,120 --> 00:31:24,760 Speaker 5: So I'm trying to go no, no, no, the better valuations, 496 00:31:24,880 --> 00:31:28,200 Speaker 5: by far are in the public markets. Now. Remember the 497 00:31:28,240 --> 00:31:32,760 Speaker 5: private markets lag the public markets, So the best valuations 498 00:31:32,800 --> 00:31:36,400 Speaker 5: in the multiomic space are in the public sector. These 499 00:31:36,440 --> 00:31:41,720 Speaker 5: stocks have been destroyed by algorithms that have been looking 500 00:31:41,720 --> 00:31:46,520 Speaker 5: at just a couple of things one cash cushion, two 501 00:31:46,840 --> 00:31:51,840 Speaker 5: cash flow, and three revenue growth. Today they don't care 502 00:31:51,880 --> 00:31:54,840 Speaker 5: about five years, they don't care about one year or 503 00:31:54,840 --> 00:31:58,840 Speaker 5: two years. But these are the gems out there that 504 00:31:59,080 --> 00:32:04,560 Speaker 5: are right for the picking because the markets have destroyed them. 505 00:32:04,720 --> 00:32:07,120 Speaker 2: Okay, Kathy, I can't let you go without asking you 506 00:32:07,160 --> 00:32:08,120 Speaker 2: about China. 507 00:32:08,160 --> 00:32:09,840 Speaker 3: You've been doing this for forty years. 508 00:32:10,200 --> 00:32:14,360 Speaker 2: There seems to be up until this point, a massive 509 00:32:14,480 --> 00:32:18,520 Speaker 2: risk premium baked into this entire equity market. It's trading 510 00:32:18,560 --> 00:32:21,400 Speaker 2: at half the SMP, It's trading at thirty forty percent 511 00:32:21,400 --> 00:32:24,200 Speaker 2: of India. I can see some of the reasons why 512 00:32:24,240 --> 00:32:27,440 Speaker 2: that's so. Some of the money's gone out hasn't come 513 00:32:27,480 --> 00:32:30,040 Speaker 2: back right now, What do you make of this premium? 514 00:32:30,080 --> 00:32:31,840 Speaker 2: Do you think it's justified or do you think this 515 00:32:31,920 --> 00:32:32,800 Speaker 2: market is too cheap? 516 00:32:33,920 --> 00:32:37,480 Speaker 5: I think it's a great market for value investors. For 517 00:32:37,680 --> 00:32:42,400 Speaker 5: growth investors like us, we can see a big positive 518 00:32:42,440 --> 00:32:47,520 Speaker 5: and a big negative. The big positive is new productive forces, 519 00:32:47,840 --> 00:32:53,040 Speaker 5: so not just common prosperity, but new productive forces means 520 00:32:53,280 --> 00:32:56,880 Speaker 5: China wants to be number one in innovation, which we love. 521 00:32:57,240 --> 00:33:02,160 Speaker 5: That's a huge positive. The problem is, given all of 522 00:33:02,200 --> 00:33:07,400 Speaker 5: the policy moves that started in late twenty until very 523 00:33:07,440 --> 00:33:11,760 Speaker 5: recently when they started loosening up more, there were a 524 00:33:11,800 --> 00:33:15,560 Speaker 5: lot of reasons to see that the innovators in the 525 00:33:15,680 --> 00:33:20,600 Speaker 5: Chinese market were not being incentivized. In fact, all of 526 00:33:20,640 --> 00:33:23,760 Speaker 5: them had to go and do charitable work. So what 527 00:33:23,840 --> 00:33:27,560 Speaker 5: kind of message does that send to those people who 528 00:33:27,920 --> 00:33:31,200 Speaker 5: have great ideas and want to innovate. Maybe some of 529 00:33:31,240 --> 00:33:34,600 Speaker 5: them won't or they won't do it in China, So 530 00:33:35,240 --> 00:33:38,640 Speaker 5: that is the negative. And I do think China has 531 00:33:38,720 --> 00:33:43,400 Speaker 5: to earn its way back into growth investor portfolios with 532 00:33:43,480 --> 00:33:48,040 Speaker 5: more incentives for those who have great ideas and want 533 00:33:48,080 --> 00:33:51,240 Speaker 5: to deploy them in China as opposed to elsewhere in 534 00:33:51,320 --> 00:33:54,920 Speaker 5: the world. You know, I think China will get it right, 535 00:33:55,520 --> 00:33:59,000 Speaker 5: but it has been very discouraging from an innovator point 536 00:33:59,000 --> 00:34:01,880 Speaker 5: of view. One of the recent things that China has 537 00:34:01,920 --> 00:34:05,280 Speaker 5: done was allowed Tesla to come in and participate in 538 00:34:05,320 --> 00:34:08,560 Speaker 5: the autonomous taxi network world. We didn't think that was 539 00:34:08,600 --> 00:34:12,560 Speaker 5: going to happen. Okay, now we're talking. That's a plus. 540 00:34:12,840 --> 00:34:16,560 Speaker 5: Let's see what other pluses are out there and definitely 541 00:34:16,600 --> 00:34:19,120 Speaker 5: could earn its way back into our portfolio. 542 00:34:20,200 --> 00:34:23,520 Speaker 4: Okay, we like to end our podcast with some fun questions. 543 00:34:23,840 --> 00:34:26,200 Speaker 4: So the first one is you mentioned that you've had 544 00:34:26,200 --> 00:34:28,799 Speaker 4: Elon Musk on your podcast. Have you ever had lunch 545 00:34:28,880 --> 00:34:30,239 Speaker 4: or dinner with him? What's he like? 546 00:34:31,360 --> 00:34:34,440 Speaker 5: No, I haven't had lunch or dinner with him. Around 547 00:34:34,520 --> 00:34:37,319 Speaker 5: the podcast, the people who surround him. I think they 548 00:34:37,360 --> 00:34:39,920 Speaker 5: were giving us forty five minutes for the podcast and 549 00:34:40,000 --> 00:34:43,319 Speaker 5: that was it. He spent more than two hours with us, 550 00:34:43,800 --> 00:34:47,080 Speaker 5: and so we had a wonderful opportunity to get into 551 00:34:47,080 --> 00:34:49,719 Speaker 5: his head a little bit more understand who he is. 552 00:34:49,920 --> 00:34:53,200 Speaker 5: And one of the wonderful things he said was he 553 00:34:53,320 --> 00:34:57,480 Speaker 5: said one of the things that I ask people I respect, 554 00:34:57,600 --> 00:35:01,120 Speaker 5: and he was saying he respected our and our research 555 00:35:01,239 --> 00:35:05,560 Speaker 5: and how we go about investing. He said, when I 556 00:35:05,600 --> 00:35:10,120 Speaker 5: respect a person or a team, my one request is 557 00:35:10,560 --> 00:35:13,960 Speaker 5: that they come back at me when they disagree with me. 558 00:35:14,320 --> 00:35:17,319 Speaker 5: If they disagree with something that I'm doing, and they 559 00:35:17,400 --> 00:35:21,319 Speaker 5: let me know, then that will make an impact on 560 00:35:21,400 --> 00:35:25,160 Speaker 5: my decision making. And it is interesting that I don't 561 00:35:25,200 --> 00:35:29,200 Speaker 5: know if you kept track of the trial around his 562 00:35:29,440 --> 00:35:32,960 Speaker 5: Twitter post when he said funding secured when he was 563 00:35:33,000 --> 00:35:36,000 Speaker 5: thinking about going private and the sec it went after him. 564 00:35:36,239 --> 00:35:40,879 Speaker 5: Well during that trial, he used our research and our conversation, 565 00:35:41,440 --> 00:35:46,640 Speaker 5: especially around well, the podcast hadn't taken place at that point, 566 00:35:46,960 --> 00:35:49,880 Speaker 5: but I had written a shareholder letter by that point 567 00:35:50,160 --> 00:35:52,799 Speaker 5: to him and to the board of directors, and it 568 00:35:52,840 --> 00:35:55,719 Speaker 5: was an open letter and said don't go private and 569 00:35:55,800 --> 00:36:00,040 Speaker 5: so he said that had an impact on him and 570 00:36:00,080 --> 00:36:03,520 Speaker 5: his board of directors in not going private. Now I 571 00:36:03,520 --> 00:36:06,960 Speaker 5: don't know if he still agrees that staying public was 572 00:36:07,000 --> 00:36:11,360 Speaker 5: a good thing, although especially given all the hullabaloo and 573 00:36:11,440 --> 00:36:13,120 Speaker 5: drama around his pay package. 574 00:36:13,200 --> 00:36:15,400 Speaker 2: But well, do you think he's worth his pay package? 575 00:36:15,400 --> 00:36:20,120 Speaker 5: Definitely, no question. When they struck the pay package at 576 00:36:20,160 --> 00:36:24,759 Speaker 5: that price, the package maxed out would have been worth 577 00:36:24,760 --> 00:36:27,839 Speaker 5: two point three billion dollars. The only reason it got 578 00:36:27,920 --> 00:36:32,560 Speaker 5: to fifty six billion is it was such a spectacular success, 579 00:36:32,640 --> 00:36:36,960 Speaker 5: so much sooner than he expected and the board expected. 580 00:36:37,120 --> 00:36:39,799 Speaker 5: So think about what I just said about China. You 581 00:36:39,840 --> 00:36:44,040 Speaker 5: put in place the right incentives, and true visionaries can 582 00:36:44,120 --> 00:36:48,839 Speaker 5: accomplish anything or anything beyond one's imagination. 583 00:36:49,760 --> 00:36:52,879 Speaker 4: You've had a challenging year this year. How have these 584 00:36:52,960 --> 00:36:54,920 Speaker 4: challenges made you a better investor? 585 00:36:55,640 --> 00:36:59,080 Speaker 5: Well, we know we have mistakes in the portfolio. Everyone does. 586 00:36:59,120 --> 00:37:01,920 Speaker 5: By the way, ours get highlighted because we post our trades. 587 00:37:02,280 --> 00:37:05,520 Speaker 5: But if you were to see the trading record of 588 00:37:05,800 --> 00:37:09,600 Speaker 5: any portfolio team, you would see a lot of mistakes 589 00:37:09,680 --> 00:37:12,720 Speaker 5: being sold. But a down market gives us a chance. 590 00:37:12,760 --> 00:37:16,560 Speaker 5: Wait a minute, everything's down this one we weren't quite 591 00:37:16,600 --> 00:37:19,160 Speaker 5: right on this one, we feel even more confident about 592 00:37:19,239 --> 00:37:23,000 Speaker 5: let's just swap some of this into this. So it's 593 00:37:23,000 --> 00:37:26,360 Speaker 5: not that it's making us a better investor, but it 594 00:37:26,440 --> 00:37:31,200 Speaker 5: is giving us opportunities to concentrate towards our highest conviction names, 595 00:37:31,239 --> 00:37:33,880 Speaker 5: which is what we always do in a down market. 596 00:37:35,120 --> 00:37:37,120 Speaker 4: Okay, I think we're going to end there because you've 597 00:37:37,239 --> 00:37:40,600 Speaker 4: given us so much insights today. Thank you so much 598 00:37:40,640 --> 00:37:44,600 Speaker 4: for your time. We went from everything from Tesla to cancer, 599 00:37:44,719 --> 00:37:46,040 Speaker 4: so I think we've covered it all. 600 00:37:47,600 --> 00:37:50,239 Speaker 2: Yes, Seed, Kathy would thank you so much of course 601 00:37:50,280 --> 00:37:53,359 Speaker 2: for joining us today, and so all our listeners out there, 602 00:37:53,400 --> 00:37:55,760 Speaker 2: thank you for listening to Tig Your Money, your Boomberg 603 00:37:55,760 --> 00:38:00,160 Speaker 2: podcast about investing clients in financial markets in Asia and beyond. 604 00:38:00,200 --> 00:38:02,360 Speaker 2: If you like what you hear, please do not forget 605 00:38:02,520 --> 00:38:05,359 Speaker 2: to like, share, and subscribe until next time. Of course, 606 00:38:05,360 --> 00:38:08,280 Speaker 2: you can find us on the Bloomberg terminal or on LinkedIn. 607 00:38:08,800 --> 00:38:11,239 Speaker 2: We look forward, of course to hearing from all of you. 608 00:38:11,800 --> 00:38:14,480 Speaker 3: This podcast was produced by Clara Chan