WEBVTT - Investing in Life Sciences

0:00:02.520 --> 0:00:07.040
<v Speaker 1>Bloomberg Audio Studios, Podcasts, radio News.

0:00:07.960 --> 0:00:11.680
<v Speaker 2>You're listening to Bloomberg Business Week with Carol Masser and

0:00:11.760 --> 0:00:14.920
<v Speaker 2>Tim Steneveek on Bloomberg Radio Matter.

0:00:15.000 --> 0:00:16.960
<v Speaker 3>You're thinking a lot about biotech.

0:00:17.120 --> 0:00:19.760
<v Speaker 1>I love the biotech sector. I think I still think

0:00:19.840 --> 0:00:22.680
<v Speaker 1>that there's so much we have yet to learn about

0:00:23.000 --> 0:00:26.360
<v Speaker 1>the body and how things work. Yeah, and I think

0:00:26.400 --> 0:00:28.800
<v Speaker 1>with AI there is this potential.

0:00:28.800 --> 0:00:30.600
<v Speaker 3>I just want cancer to be cured.

0:00:30.760 --> 0:00:33.440
<v Speaker 1>I just want cancer to be truly I know, I know.

0:00:33.920 --> 0:00:36.680
<v Speaker 1>I feel like it's the next big thing. We've figured

0:00:36.680 --> 0:00:37.360
<v Speaker 1>out some things.

0:00:37.720 --> 0:00:40.680
<v Speaker 2>There's idea of cancer moonshots, and it's just I don't know,

0:00:40.720 --> 0:00:42.960
<v Speaker 2>it's just like it's very frustrating.

0:00:43.200 --> 0:00:45.800
<v Speaker 1>Yeah, I know, I know, I know. So we talk

0:00:45.840 --> 0:00:47.879
<v Speaker 1>about it a lot. We do. There's a lot of

0:00:47.880 --> 0:00:51.600
<v Speaker 1>money going though at the world of pharmaceuticals and biotech,

0:00:51.680 --> 0:00:54.760
<v Speaker 1>and one story we wanted to point out our Jerry

0:00:54.800 --> 0:00:58.000
<v Speaker 1>Smith wrote this of Bloomberg that Blackstone is investing two

0:00:58.040 --> 0:01:01.240
<v Speaker 1>hundred and fifty million in a biotech startup, Xanogramd Therapeutics.

0:01:01.240 --> 0:01:04.480
<v Speaker 1>It's part of the private equity giant strategy to own

0:01:04.520 --> 0:01:07.880
<v Speaker 1>more cutting edge drug makers. I mean, it's called Blackstone

0:01:07.920 --> 0:01:11.039
<v Speaker 1>Life sciences. They have some seventeen billion in au M,

0:01:11.160 --> 0:01:15.080
<v Speaker 1>typically investing in late stage medicines invented by biotech and

0:01:15.120 --> 0:01:17.480
<v Speaker 1>pharma companies in exchange for a royalty and future sales.

0:01:17.520 --> 0:01:19.480
<v Speaker 1>I gets into the model. I think what's interesting to

0:01:19.560 --> 0:01:23.120
<v Speaker 1>me is their latest life science is Fun has six

0:01:23.160 --> 0:01:26.640
<v Speaker 1>point three billion of commitments. It's the firm's biggest haul

0:01:26.680 --> 0:01:30.039
<v Speaker 1>for backing clinical trials of medicines and technologies. I like

0:01:30.160 --> 0:01:32.360
<v Speaker 1>to see where the money flows. I also think money

0:01:32.360 --> 0:01:35.280
<v Speaker 1>can help make things happen and hopefully have a cure

0:01:35.319 --> 0:01:37.080
<v Speaker 1>for cancer. We put it in one bucket, but there's

0:01:37.240 --> 0:01:38.240
<v Speaker 1>hopefully lots of cures.

0:01:38.319 --> 0:01:41.000
<v Speaker 2>Yeah, these are things that Jenny Rook thinks a lot

0:01:41.040 --> 0:01:44.959
<v Speaker 2>about because this is an area that invests in. She's

0:01:45.000 --> 0:01:47.480
<v Speaker 2>the founder and managing director of Geno Adventures. She's back

0:01:47.480 --> 0:01:50.600
<v Speaker 2>here in our Bloomberg Interactive Brokers studio. Your portfolio has

0:01:50.720 --> 0:01:56.440
<v Speaker 2>investments in new culture, reducts, biofellow bond, pet foods.

0:01:55.920 --> 0:01:56.400
<v Speaker 3>And more.

0:01:57.040 --> 0:01:59.080
<v Speaker 2>Good to have you back with us. It's been gosh

0:01:59.160 --> 0:02:01.760
<v Speaker 2>since January when we last spoke. How are you doing.

0:02:01.800 --> 0:02:04.200
<v Speaker 1>Well, nothing's happened, right, It's.

0:02:04.040 --> 0:02:04.880
<v Speaker 4>Been pretty chill.

0:02:05.480 --> 0:02:10.080
<v Speaker 2>Yeah, well, well, you know what's the connection between the

0:02:10.120 --> 0:02:14.120
<v Speaker 2>public markets and and sort of what you see in

0:02:14.160 --> 0:02:18.040
<v Speaker 2>your world of venture capital and VC investing. Is there

0:02:18.480 --> 0:02:21.960
<v Speaker 2>any connection to volatility that we saw in March, increase

0:02:22.040 --> 0:02:25.639
<v Speaker 2>in sentiment, positive sentiment we saw in April.

0:02:25.720 --> 0:02:26.919
<v Speaker 3>Does that affect your world at all?

0:02:27.080 --> 0:02:29.919
<v Speaker 4>It does, and maybe even more than I probably originally

0:02:30.000 --> 0:02:33.200
<v Speaker 4>realized getting into venture. But it's exactly as you're just saying, Carol,

0:02:33.320 --> 0:02:36.200
<v Speaker 4>is that when you have in the public markets the interest,

0:02:36.320 --> 0:02:41.400
<v Speaker 4>the capital, the emphasis on driving these later stage businesses forward.

0:02:41.440 --> 0:02:45.480
<v Speaker 4>Getting medicines out into the through the clinic and into

0:02:45.480 --> 0:02:48.720
<v Speaker 4>the market. That drives a lot of the underlying layer

0:02:48.800 --> 0:02:51.680
<v Speaker 4>of demand for tools, the application of AI to the

0:02:51.720 --> 0:02:55.240
<v Speaker 4>discovery of biology, making you less frustrated about what we

0:02:55.320 --> 0:02:58.840
<v Speaker 4>don't know in medicine. So it really is driving I

0:02:58.840 --> 0:03:01.840
<v Speaker 4>think both the right atten and the right investment, Jenny.

0:03:01.840 --> 0:03:05.120
<v Speaker 1>When there's a big player like Blackstone, is that good

0:03:05.120 --> 0:03:07.680
<v Speaker 1>for the market? Is it more competitive than for you?

0:03:07.720 --> 0:03:10.200
<v Speaker 1>These are late stage I think you're earlier, right.

0:03:10.160 --> 0:03:13.400
<v Speaker 4>That's right. So at General Adventures we exist to lead

0:03:13.480 --> 0:03:16.280
<v Speaker 4>first rounds and companies that are hopefully going to be

0:03:16.320 --> 0:03:18.560
<v Speaker 4>public in seven to ten years. Being part of the

0:03:18.639 --> 0:03:21.919
<v Speaker 4>venture asset class. But it's very important to have these

0:03:22.000 --> 0:03:26.120
<v Speaker 4>later stage funds validating that the segment itself is an

0:03:26.160 --> 0:03:28.679
<v Speaker 4>important place to put capital and is driving returns later

0:03:28.720 --> 0:03:29.080
<v Speaker 4>as well.

0:03:29.160 --> 0:03:34.639
<v Speaker 3>So let's get to curing cancer, okay, please, Why is

0:03:34.680 --> 0:03:35.920
<v Speaker 3>it so difficult?

0:03:36.120 --> 0:03:38.280
<v Speaker 2>And our company is still working on this given that

0:03:38.480 --> 0:03:40.880
<v Speaker 2>we've and we've talked about this with you in the past.

0:03:41.120 --> 0:03:43.600
<v Speaker 2>I mean, you know, in the past eighteen months or

0:03:43.640 --> 0:03:46.280
<v Speaker 2>so about research funding drying up at some universities. But

0:03:46.680 --> 0:03:49.640
<v Speaker 2>our company is still putting money, oh very much.

0:03:49.680 --> 0:03:52.440
<v Speaker 4>So I would say if you look at the therapeutic

0:03:52.440 --> 0:03:55.240
<v Speaker 4>space generally and where investment is going, that is still

0:03:55.280 --> 0:03:57.880
<v Speaker 4>the oncology and still one of the leading segments for

0:03:57.960 --> 0:04:02.560
<v Speaker 4>investment and innovation. Yes, there's work still to be done,

0:04:02.600 --> 0:04:04.920
<v Speaker 4>but it is no longer a death sentence today like.

0:04:04.960 --> 0:04:05.440
<v Speaker 3>It is not.

0:04:05.800 --> 0:04:08.280
<v Speaker 2>But one scary thing about the reason why I think

0:04:08.360 --> 0:04:13.400
<v Speaker 2>so much money is going into oncology is because it's

0:04:13.400 --> 0:04:18.160
<v Speaker 2>becoming increasingly common for people as they live longer. That's right,

0:04:18.520 --> 0:04:20.480
<v Speaker 2>So more people are getting it because more people are

0:04:20.480 --> 0:04:22.520
<v Speaker 2>living longer. And also there are other types of cancers

0:04:22.520 --> 0:04:23.760
<v Speaker 2>that younger people are now getting.

0:04:24.040 --> 0:04:27.320
<v Speaker 4>Right, the color rectal cancer exactly rise. Yeah, that's exactly right.

0:04:27.400 --> 0:04:30.599
<v Speaker 4>So in a sense, it is the flip side of

0:04:30.600 --> 0:04:33.640
<v Speaker 4>the coin of living longer, healthier lives some have called

0:04:33.640 --> 0:04:37.120
<v Speaker 4>cancer disease of aging, so we are now living long

0:04:37.200 --> 0:04:39.839
<v Speaker 4>enough to encounter it. It's also very complex. It's not

0:04:39.960 --> 0:04:43.039
<v Speaker 4>one thing, it's not even just one thing in a

0:04:43.080 --> 0:04:45.880
<v Speaker 4>given organ. It's many different kinds of molecular changes.

0:04:46.160 --> 0:04:47.960
<v Speaker 1>So talk to us about what's interesting that's going on

0:04:48.000 --> 0:04:50.440
<v Speaker 1>in terms of biology. What are you seeing right?

0:04:50.920 --> 0:04:54.400
<v Speaker 4>Well, something that's been really exciting in this past quarters.

0:04:54.520 --> 0:04:57.480
<v Speaker 4>You've seen we've started to see the public markets opening up.

0:04:57.480 --> 0:04:59.720
<v Speaker 4>We've had a couple of IPOs in the biotech space,

0:05:00.320 --> 0:05:03.840
<v Speaker 4>surprising and delightful to us as investors in the infrastructure

0:05:03.839 --> 0:05:06.360
<v Speaker 4>and the tool chain. One of those was not therapeutics

0:05:06.360 --> 0:05:09.800
<v Speaker 4>at all, but a proteomics company, so alamar Bio came

0:05:09.839 --> 0:05:13.200
<v Speaker 4>out on NASDAK oversubscribed, has traded up since then and

0:05:13.240 --> 0:05:15.279
<v Speaker 4>the months since then. And what they do is they

0:05:15.360 --> 0:05:19.279
<v Speaker 4>make it possible to measure more proteins in, for example,

0:05:19.279 --> 0:05:23.040
<v Speaker 4>a patient sample, at a greater level of precision and

0:05:23.120 --> 0:05:26.560
<v Speaker 4>sensitivity than ever possible. And the hunger for being able

0:05:26.600 --> 0:05:29.359
<v Speaker 4>to measure what's happening in the protein layer, which is

0:05:29.520 --> 0:05:33.320
<v Speaker 4>in some sense the business end of biology, is necessary

0:05:33.480 --> 0:05:38.280
<v Speaker 4>for the next phase of understanding cancer biology, diagnostics, and

0:05:38.320 --> 0:05:40.799
<v Speaker 4>even kind of engineering of drugs in all of those spaces.

0:05:40.839 --> 0:05:43.720
<v Speaker 1>Why is that like understanding that layer now?

0:05:44.600 --> 0:05:46.680
<v Speaker 4>I think you probably know you've probably heard the dogmas

0:05:46.680 --> 0:05:49.440
<v Speaker 4>and they say genes, DNA make RNA, make proteins, and

0:05:49.480 --> 0:05:52.560
<v Speaker 4>it's proteins that circulate, interact with each other. It's kind

0:05:52.600 --> 0:05:55.400
<v Speaker 4>of the network of what's happening in the body and

0:05:55.440 --> 0:05:58.919
<v Speaker 4>between cells, and it's often proteins. Something going on with

0:05:58.960 --> 0:06:02.239
<v Speaker 4>the proteins that go goes wrong when we're talking about

0:06:02.240 --> 0:06:04.640
<v Speaker 4>disease pathology, and so we need to be able to

0:06:04.640 --> 0:06:07.000
<v Speaker 4>measure that, got it? And you know, we've made bets

0:06:07.000 --> 0:06:07.719
<v Speaker 4>in that space too.

0:06:07.800 --> 0:06:09.560
<v Speaker 1>I think before we got going, I still feel like

0:06:10.000 --> 0:06:12.600
<v Speaker 1>I just that we still don't know so much about Yeah,

0:06:12.640 --> 0:06:15.680
<v Speaker 1>that's our body and what the body can be used.

0:06:15.680 --> 0:06:18.240
<v Speaker 1>I think we're doing. We're learning more with therapy, right,

0:06:18.279 --> 0:06:20.839
<v Speaker 1>and how to use the body to help cure what

0:06:21.240 --> 0:06:21.800
<v Speaker 1>ails us.

0:06:21.880 --> 0:06:24.040
<v Speaker 4>That's right, But all of this depends on and actually

0:06:24.080 --> 0:06:28.320
<v Speaker 4>that AI revolution is fueling hunger for more and better data. Yeah,

0:06:28.560 --> 0:06:31.599
<v Speaker 4>probably know that AI is only as powerful as the

0:06:31.720 --> 0:06:34.520
<v Speaker 4>quality and the amount of data that you put into it. Right,

0:06:34.560 --> 0:06:36.920
<v Speaker 4>and so we've known, as you say, Carol, there's so

0:06:37.000 --> 0:06:40.520
<v Speaker 4>much we still don't know. But now there's reasons to

0:06:40.560 --> 0:06:43.520
<v Speaker 4>get that complexity of DNA because we can interact with

0:06:43.560 --> 0:06:44.520
<v Speaker 4>it with AIO.

0:06:44.600 --> 0:06:46.400
<v Speaker 2>To your point, Carol, I think what surprises me so

0:06:46.480 --> 0:06:48.080
<v Speaker 2>much and talking to with friends who are doctors, is

0:06:48.080 --> 0:06:49.560
<v Speaker 2>how much out there is idiopathic.

0:06:49.680 --> 0:06:50.560
<v Speaker 3>It's the idea that.

0:06:50.560 --> 0:06:54.000
<v Speaker 2>Okay, we recognize that the symptoms exist. We see your symptoms,

0:06:54.040 --> 0:06:56.680
<v Speaker 2>we just have no idea what causes them. Well, like

0:06:56.720 --> 0:07:01.200
<v Speaker 2>that's in twenty twenty six with imaging, with blood testing,

0:07:02.040 --> 0:07:03.760
<v Speaker 2>with all of the diagnostic testing that we have, there

0:07:03.800 --> 0:07:04.960
<v Speaker 2>are still so many more questions.

0:07:05.000 --> 0:07:07.600
<v Speaker 1>So how do we get to those really great data

0:07:07.680 --> 0:07:11.800
<v Speaker 1>sets because I mean our health facilities competing for the

0:07:11.880 --> 0:07:13.960
<v Speaker 1>data because that gives them more value, or is there

0:07:14.000 --> 0:07:16.840
<v Speaker 1>going to be some comprehensive data set so that we

0:07:16.880 --> 0:07:17.720
<v Speaker 1>all benefit off of.

0:07:18.400 --> 0:07:20.800
<v Speaker 4>I think what we've seen so far with AI. Generally

0:07:20.840 --> 0:07:23.760
<v Speaker 4>this is outside of healthcare, but AI is most powerful

0:07:24.080 --> 0:07:28.080
<v Speaker 4>when one can close the loop between generation of exactly

0:07:28.120 --> 0:07:30.160
<v Speaker 4>the right data set at a high quality to answer

0:07:30.240 --> 0:07:33.520
<v Speaker 4>the question you want to answer. So in the healthcare space,

0:07:33.640 --> 0:07:37.960
<v Speaker 4>closing the loop between turning biology into data, feeding that

0:07:38.160 --> 0:07:41.720
<v Speaker 4>into better and better AI models, generating insights, and then

0:07:41.800 --> 0:07:44.760
<v Speaker 4>doing it again, and so that pace of learning with

0:07:45.040 --> 0:07:46.800
<v Speaker 4>the AI support is only accelerating.

0:07:46.880 --> 0:07:49.040
<v Speaker 3>Have we seen any of that proven out yet?

0:07:49.760 --> 0:07:51.680
<v Speaker 4>Yeah? I think you see it in. One of the

0:07:51.720 --> 0:07:54.960
<v Speaker 4>other kind of big news in the space is Roch's

0:07:54.960 --> 0:07:57.800
<v Speaker 4>acquisition of pathai, right, and I think from people who

0:07:57.840 --> 0:08:00.160
<v Speaker 4>are interested in the space and trying to understand how

0:08:00.200 --> 0:08:03.760
<v Speaker 4>AI is affecting healthcare, that's a pretty great one stop

0:08:03.800 --> 0:08:08.120
<v Speaker 4>example or case study because pathai solutions include generation of

0:08:08.200 --> 0:08:11.800
<v Speaker 4>data on samples, visualization like you were saying, and then

0:08:11.840 --> 0:08:15.200
<v Speaker 4>the AI tools to understand it, as well as workflow

0:08:15.200 --> 0:08:18.040
<v Speaker 4>improvements because it all matters. What matters is can you

0:08:18.080 --> 0:08:19.960
<v Speaker 4>deliver it to patients in the healthcare system.

0:08:20.120 --> 0:08:22.880
<v Speaker 1>But you know what that's going back to the data,

0:08:23.000 --> 0:08:25.800
<v Speaker 1>like who is going to be the accumulator like I do.

0:08:25.880 --> 0:08:28.400
<v Speaker 1>We've had a guest on somebody I had talked to

0:08:28.480 --> 0:08:31.560
<v Speaker 1>at Bloomberg and Fast about this whole idea of agentic

0:08:31.600 --> 0:08:36.200
<v Speaker 1>AI but talking about data sets that are very specific

0:08:36.280 --> 0:08:39.319
<v Speaker 1>to a different industry, and her point really was that

0:08:39.760 --> 0:08:41.920
<v Speaker 1>you know, you're not going to need all these lms

0:08:42.240 --> 0:08:43.360
<v Speaker 1>accumulating everything.

0:08:43.400 --> 0:08:46.200
<v Speaker 3>You're going to need these very focused right.

0:08:46.520 --> 0:08:50.679
<v Speaker 1>So who, like, let's take I don't know, colon cancer, Like,

0:08:50.960 --> 0:08:56.600
<v Speaker 1>will there be an accumulator taking in data from global

0:08:56.720 --> 0:09:00.640
<v Speaker 1>so that you really get a great data set or

0:09:00.679 --> 0:09:02.040
<v Speaker 1>does everybody have their own?

0:09:02.320 --> 0:09:04.600
<v Speaker 4>The answer it ends up being both, so you end

0:09:04.720 --> 0:09:08.240
<v Speaker 4>up with large comprehensive data sets. For example, like the

0:09:08.320 --> 0:09:11.000
<v Speaker 4>UK Biobank is a very rich data set with a

0:09:11.000 --> 0:09:14.800
<v Speaker 4>lot of longitudinal information about patients and their healthcare, and

0:09:14.840 --> 0:09:17.800
<v Speaker 4>then the ability to access that and add to it

0:09:17.960 --> 0:09:20.960
<v Speaker 4>the layers of specific data sets that, for example, a

0:09:21.000 --> 0:09:24.240
<v Speaker 4>company trying to develop a diagnostic in a particular cancer

0:09:24.679 --> 0:09:28.199
<v Speaker 4>might add a proteo mix layer or a glycoproteomics layer

0:09:28.240 --> 0:09:29.839
<v Speaker 4>around a particular test.

0:09:30.040 --> 0:09:33.239
<v Speaker 1>Is the US still accumulating great data? With the administration

0:09:33.480 --> 0:09:35.240
<v Speaker 1>and some of the cuts and so on and so forth,

0:09:35.240 --> 0:09:37.200
<v Speaker 1>Are we still accumulating smart data?

0:09:38.080 --> 0:09:40.320
<v Speaker 4>This is a really important question. A lot of the

0:09:40.360 --> 0:09:43.320
<v Speaker 4>conversation this year that I've been in has been what

0:09:43.480 --> 0:09:48.880
<v Speaker 4>is our relative investment in generating more data at high quality?

0:09:48.880 --> 0:09:51.760
<v Speaker 4>And how does that compare to other geographies like China?

0:09:51.840 --> 0:09:53.000
<v Speaker 4>We have to talk about China.

0:09:54.640 --> 0:09:56.760
<v Speaker 3>I want to go in a different direction than China.

0:09:56.880 --> 0:09:59.960
<v Speaker 4>Okay, but because we haven't talked about China, an.

0:10:01.840 --> 0:10:07.720
<v Speaker 2>Are our incentives aligned for in our healthcare system to

0:10:07.800 --> 0:10:09.280
<v Speaker 2>make people healthier?

0:10:09.440 --> 0:10:12.200
<v Speaker 4>It's honestly easier to talk about China than our healthcare system.

0:10:12.240 --> 0:10:17.360
<v Speaker 2>But because our healthcare system makes money when people are sick. Yeah,

0:10:17.440 --> 0:10:20.240
<v Speaker 2>if you think about some of the biggest hospitals, insurance

0:10:20.360 --> 0:10:21.199
<v Speaker 2>and insurance companies.

0:10:21.200 --> 0:10:23.000
<v Speaker 3>Some people argue insurance companies serve as a.

0:10:23.000 --> 0:10:28.240
<v Speaker 2>Check on some healthcare facilities because they're only going to

0:10:28.240 --> 0:10:30.000
<v Speaker 2>pay for X so, and you know, and they kick

0:10:30.040 --> 0:10:31.360
<v Speaker 2>you out after a certain number of days.

0:10:31.720 --> 0:10:33.679
<v Speaker 3>Are the incentives misaligned in our society?

0:10:33.679 --> 0:10:35.679
<v Speaker 4>And you're quite right to talk about the incentives. And

0:10:36.400 --> 0:10:38.520
<v Speaker 4>you know this is well above my pay grade as

0:10:38.520 --> 0:10:39.880
<v Speaker 4>a venture investor, So.

0:10:40.040 --> 0:10:42.000
<v Speaker 2>That must cross your mind when you're doing the calculation.

0:10:42.320 --> 0:10:43.439
<v Speaker 2>Have a spreadsheet out.

0:10:43.320 --> 0:10:45.679
<v Speaker 4>So you have to think about, is a solution that

0:10:46.040 --> 0:10:48.559
<v Speaker 4>is based on innovation coming to market in a way

0:10:48.600 --> 0:10:51.000
<v Speaker 4>that can at least align enough of the incentives to

0:10:51.040 --> 0:10:52.040
<v Speaker 4>get there to be used.

0:10:52.160 --> 0:10:54.640
<v Speaker 3>Yeah, that's the question. Okay, next time, China.

0:10:54.679 --> 0:10:56.120
<v Speaker 4>Next time, China, time China.

0:10:56.160 --> 0:10:59.520
<v Speaker 1>No, I really do want to talk because we're talking

0:10:59.520 --> 0:11:01.600
<v Speaker 1>about China solar early like yeah, I.

0:11:01.600 --> 0:11:03.480
<v Speaker 4>Have to well, yeah, and we have to also talk

0:11:03.520 --> 0:11:07.600
<v Speaker 4>about how that intersects with biotechnology beyond healthcare as a

0:11:07.960 --> 0:11:13.000
<v Speaker 4>technology layer or general purpose technology for security, for defense,

0:11:13.280 --> 0:11:16.480
<v Speaker 4>for agriculture and food for industrials. You were talking earlier

0:11:16.520 --> 0:11:19.000
<v Speaker 4>about critical minerals. It's a space where we're looking where

0:11:19.000 --> 0:11:20.320
<v Speaker 4>bio can have an impact as well.

0:11:20.559 --> 0:11:23.680
<v Speaker 1>See, you always leave us won anymore, Jenny Rook, my

0:11:23.760 --> 0:11:26.800
<v Speaker 1>pleasure be well, be well, have a good weekend. Thanks

0:11:26.800 --> 0:11:29.880
<v Speaker 1>for thanks for bringing by Jenny Rook. She's founder imaging

0:11:29.960 --> 0:11:31.199
<v Speaker 1>director of General Adventures