1 00:00:00,560 --> 00:00:03,280 Speaker 1: We'd like to interview you about how we can make 2 00:00:03,400 --> 00:00:06,600 Speaker 1: shared lunch better. We've got a survey happening right now 3 00:00:06,680 --> 00:00:09,360 Speaker 1: and we'd love your feedback. It just takes a few minutes, 4 00:00:09,560 --> 00:00:11,879 Speaker 1: and if you're a New Zealand resident, you can go 5 00:00:11,920 --> 00:00:15,200 Speaker 1: on the drawer to one of six fifty dollars Shares's gifts. 6 00:00:15,480 --> 00:00:17,280 Speaker 1: The link is in our show notes. 7 00:00:17,440 --> 00:00:24,880 Speaker 2: Right now you're listening to a Chasi's podcast, just getting 8 00:00:24,920 --> 00:00:30,760 Speaker 2: back to manufacturing these pharmaceuticals. Has AI had much of 9 00:00:30,800 --> 00:00:33,720 Speaker 2: a place here in terms of speeding things. 10 00:00:33,600 --> 00:00:37,279 Speaker 3: Up, So this AI kind of in farmer companies and 11 00:00:37,280 --> 00:00:40,360 Speaker 3: in drug discovery. It's been a topic that investors are 12 00:00:40,440 --> 00:00:44,239 Speaker 3: kind of increasingly focused on and increasingly excited about. And 13 00:00:44,280 --> 00:00:47,239 Speaker 3: one of the key kind of themes or takeaways I 14 00:00:47,280 --> 00:00:49,400 Speaker 3: would say that I really noticed when I was in 15 00:00:49,440 --> 00:00:52,479 Speaker 3: the US is just how excited companies are about the 16 00:00:52,600 --> 00:00:55,360 Speaker 3: use of AI and drug discovery. Because we've already talked 17 00:00:55,400 --> 00:00:59,320 Speaker 3: about how time consuming and expensive it is to develop 18 00:00:59,400 --> 00:01:02,720 Speaker 3: new medicine, and what we've seen is that the developments 19 00:01:02,760 --> 00:01:07,600 Speaker 3: in AI mean that you can develop new molecules around 20 00:01:07,760 --> 00:01:10,240 Speaker 3: ten times as fast, or so you can design new 21 00:01:10,240 --> 00:01:13,520 Speaker 3: molecules around ten times as fast. You can run simulations 22 00:01:13,520 --> 00:01:17,280 Speaker 3: on those molecules around one hundred times as fast. And 23 00:01:17,319 --> 00:01:19,760 Speaker 3: then there's this new concept, which I think is pretty interesting. 24 00:01:19,760 --> 00:01:21,759 Speaker 3: You've heard of the concept of a self driving car. 25 00:01:22,080 --> 00:01:25,319 Speaker 3: There's this new concept of a self driving lab. And 26 00:01:25,360 --> 00:01:28,640 Speaker 3: so what this means is that you have AI and 27 00:01:28,680 --> 00:01:31,840 Speaker 3: then you have robotics, and so you use AI and 28 00:01:31,959 --> 00:01:35,080 Speaker 3: robotics together and they drive the cycle of kind of prediction, 29 00:01:35,280 --> 00:01:40,240 Speaker 3: experimentation and analysis, which means that you can iteratively identify 30 00:01:40,319 --> 00:01:43,680 Speaker 3: new compounds which you then run experiments on, which then 31 00:01:43,760 --> 00:01:46,200 Speaker 3: mean that the whole process is much faster and then 32 00:01:46,240 --> 00:01:48,840 Speaker 3: importantly more cost effective for companies. 33 00:01:49,240 --> 00:01:51,800 Speaker 2: Gosh, So do we have any idea how much that 34 00:01:51,800 --> 00:01:54,520 Speaker 2: would speed things up? We talked about ten years before 35 00:01:55,000 --> 00:01:58,280 Speaker 2: being the time that it can take, I think for exclusivity, 36 00:01:58,320 --> 00:02:00,000 Speaker 2: but I think you know, when you've got a meticine 37 00:02:00,560 --> 00:02:02,840 Speaker 2: in the wings, that can take even longer, can't it. 38 00:02:03,040 --> 00:02:06,040 Speaker 3: Yeah, And I think it's probably too early yet to 39 00:02:06,120 --> 00:02:09,240 Speaker 3: put an actual number on how much it could speed 40 00:02:09,320 --> 00:02:11,400 Speaker 3: things up. But I think what we're seeing is more 41 00:02:11,440 --> 00:02:13,639 Speaker 3: and more companies are talking about it, more and more 42 00:02:13,639 --> 00:02:17,080 Speaker 3: companies are using it in their drug discovery process. So 43 00:02:17,200 --> 00:02:19,880 Speaker 3: I think over time we'll be able to get a 44 00:02:19,880 --> 00:02:23,640 Speaker 3: better sense of how much time it actually is taking 45 00:02:23,680 --> 00:02:26,480 Speaker 3: off the drug discovery process. But to the extent that 46 00:02:26,520 --> 00:02:30,760 Speaker 3: it means we can develop medicines that are cheaper, I 47 00:02:30,760 --> 00:02:32,520 Speaker 3: feel like that could only be a good thing given 48 00:02:32,560 --> 00:02:34,960 Speaker 3: the high costs of healthcare that we're seeing around the 49 00:02:34,960 --> 00:02:35,959 Speaker 3: world now. 50 00:02:36,000 --> 00:02:39,799 Speaker 2: Just thinking about investors looking to invest in the sector, 51 00:02:40,440 --> 00:02:43,360 Speaker 2: I mean, there are those direct stocks, they are probably 52 00:02:43,440 --> 00:02:46,320 Speaker 2: quite expensive at the moment for some people. But also 53 00:02:46,520 --> 00:02:49,560 Speaker 2: what about ETFs. I know on cheeseys, for instance, we've 54 00:02:49,639 --> 00:02:53,359 Speaker 2: got about five or at least there's more actually pharmaceutical 55 00:02:53,400 --> 00:02:56,720 Speaker 2: ETFs that people can look to. They are mainly in 56 00:02:56,760 --> 00:02:59,600 Speaker 2: the US. But also I did see that there was 57 00:02:59,760 --> 00:03:05,640 Speaker 2: a couple of applications for more themed weight loss ETFs 58 00:03:05,680 --> 00:03:09,440 Speaker 2: by I think Amplify and round til investments. We're two 59 00:03:09,560 --> 00:03:12,519 Speaker 2: though I don't think they've actually come to fruition yet. 60 00:03:12,800 --> 00:03:15,720 Speaker 3: So I guess when you're thinking about healthcare, healthcare itself 61 00:03:15,880 --> 00:03:19,760 Speaker 3: is super broad, and then within that there's all different 62 00:03:19,840 --> 00:03:24,640 Speaker 3: kinds of companies, so hospitals, medical device companies, companies that 63 00:03:24,720 --> 00:03:27,400 Speaker 3: sell chemicals that are used in farmer companies that sell 64 00:03:27,480 --> 00:03:29,960 Speaker 3: tools that are used in farmer, so all of that 65 00:03:30,080 --> 00:03:32,600 Speaker 3: kind of sets in the broader healthcare space. And I 66 00:03:32,639 --> 00:03:35,200 Speaker 3: think if you're thinking about getting a broad kind of 67 00:03:35,240 --> 00:03:38,600 Speaker 3: ETF exposure, maybe one way to get a sense of 68 00:03:39,120 --> 00:03:41,720 Speaker 3: what could be better is looking back at what's happened 69 00:03:41,720 --> 00:03:44,520 Speaker 3: over time. And if you look back over a significant 70 00:03:44,520 --> 00:03:47,160 Speaker 3: period of time, say twenty years, the S and P 71 00:03:47,400 --> 00:03:50,800 Speaker 3: five hundred Index and the S and P Healthcare Index 72 00:03:51,080 --> 00:03:54,720 Speaker 3: have actually interestingly performed basically neck on neck, so they 73 00:03:54,760 --> 00:03:57,760 Speaker 3: both generated around a ten percent return per year over 74 00:03:57,800 --> 00:04:01,960 Speaker 3: a twenty year time horizon. If you look at farmer itself, 75 00:04:02,280 --> 00:04:06,560 Speaker 3: that generated around half a percent lower return, so around 76 00:04:06,680 --> 00:04:09,800 Speaker 3: nine and a half percent over that twenty year period 77 00:04:09,800 --> 00:04:12,840 Speaker 3: of time. But if you had managed to pick farmer 78 00:04:12,880 --> 00:04:17,359 Speaker 3: winner Novo or farmer winner Eli Lilly over that period 79 00:04:17,400 --> 00:04:20,440 Speaker 3: of time, you would have generated I think from memory 80 00:04:20,480 --> 00:04:23,200 Speaker 3: it's around a sixteen percent return per ANIM and then 81 00:04:23,240 --> 00:04:25,839 Speaker 3: a twenty five percent return per ANIM. Picking a winner 82 00:04:26,240 --> 00:04:28,160 Speaker 3: can be great. But then if you're looking at a 83 00:04:28,200 --> 00:04:32,119 Speaker 3: diversified exposure, as she would suggest that rather than looking 84 00:04:32,200 --> 00:04:36,920 Speaker 3: for a narrow farmer exposure, a broader health care exposure 85 00:04:36,960 --> 00:04:40,000 Speaker 3: would be better. And then I think especially worth keeping 86 00:04:40,040 --> 00:04:45,159 Speaker 3: in mind. With a diversified easier for Farmer, You're likely 87 00:04:45,240 --> 00:04:48,400 Speaker 3: to have a higher exposure to the largest companies and 88 00:04:48,480 --> 00:04:52,000 Speaker 3: they may have a higher proportion of medicines that are 89 00:04:52,000 --> 00:04:55,400 Speaker 3: coming up to that loss of exclusivity and revenue and 90 00:04:55,440 --> 00:04:57,680 Speaker 3: revenue dropping off. So that's a really important thing to 91 00:04:57,760 --> 00:05:01,040 Speaker 3: keep in mind with Farmer itself, is what is that 92 00:05:01,080 --> 00:05:04,800 Speaker 3: loss of exclusivity burden and how does that sit across 93 00:05:04,839 --> 00:05:06,640 Speaker 3: the companies that are in that ETA. 94 00:05:06,760 --> 00:05:10,480 Speaker 2: Investing involves risk you might lose the money you start with. 95 00:05:10,960 --> 00:05:14,760 Speaker 2: We recommend talking to a licensed financial advisor. We also 96 00:05:14,800 --> 00:05:18,679 Speaker 2: recommend reading product disclosure documents before deciding to invest.