WEBVTT - Bonus: How will AI change investing?

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<v Speaker 1>Kyorda, Welcome to Shared Lunch. I'm Sonya. Today we have

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<v Speaker 1>a special episode we will talk about AI and investing.

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<v Speaker 1>We have a new feature coming to Chairs's called Search

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<v Speaker 1>AI and we'll talk more about it in this episode.

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<v Speaker 1>But before we get started, here's some important information.

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<v Speaker 2>Investing involves risk you might lose the money you start with.

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<v Speaker 2>We recommend talking to a licensed financial advisor. We also

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<v Speaker 2>recommend reading product disclosure documents before deciding to invest. Everything

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<v Speaker 2>you're about to see and here is current at the

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<v Speaker 2>time of recording.

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<v Speaker 3>Today.

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<v Speaker 1>We're joined by Richard Clark, who is one of the

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<v Speaker 1>co founders of chairs EA's, and Luke Petett, who is

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<v Speaker 1>the founder and CEO of Telescope. We are talking about AI,

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<v Speaker 1>which is a hot topic right now and something that's

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<v Speaker 1>really of interest to us as we are launching a

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<v Speaker 1>new tool here at chairs EA's called AI Search, and

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<v Speaker 1>so we want to talk about that, but also just

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<v Speaker 1>get a to to talk about AI with two people

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<v Speaker 1>who are deep in that world. So let's kick off

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<v Speaker 1>the conversation. So first, maybe we'll start with you, Richard.

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<v Speaker 1>Can you tell us a bit about.

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<v Speaker 3>You and how you come to be joining us combo today.

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<v Speaker 4>Yeah, sure. So I'm one of the technical co founders

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<v Speaker 4>of Chaza's and you know, came to Chazas after a

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<v Speaker 4>long time in the tech industry. And I think the

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<v Speaker 4>fascinating thing for me about AI has it has always

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<v Speaker 4>been really interesting, you know, the idea of computers doing everything,

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<v Speaker 4>you know, being just like people in some ways. You know,

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<v Speaker 4>it's science fiction everywhere, and so that was always a

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<v Speaker 4>thing that was fascinating to me. I tried to, you know,

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<v Speaker 4>make things ever since I started programming, you know, make

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<v Speaker 4>little companions or whatever, and they were all very good.

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<v Speaker 4>But you know, in the last few years, we've just

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<v Speaker 4>seen this incredible sea change, and it has been so

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<v Speaker 4>so interesting to see something that's gone from like nothing

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<v Speaker 4>to science fiction to to you know, really right there

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<v Speaker 4>on your computer, able to play with it. And you know,

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<v Speaker 4>my so my recent history with that has just been

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<v Speaker 4>really engaging with these new changes, building tools, chatting with

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<v Speaker 4>people like Louk here, and really trying to understand how

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<v Speaker 4>this is going to impact everything and how we can

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<v Speaker 4>use these things to do really really interesting stuff.

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<v Speaker 5>And how about you, Lot, Yeah, I mean, well, first

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<v Speaker 5>of all, you Sion founder of Telescope.

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<v Speaker 6>So I'm definitely trying to keep up with AI.

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<v Speaker 5>It's it's wild, as Richard pointed out, like it's it's

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<v Speaker 5>just moving so quick. It is even for someone well

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<v Speaker 5>and truly involved in the industry and as an engineer,

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<v Speaker 5>you know, it's sort of I feel like I understand

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<v Speaker 5>the fundamentals, but it is. It is crazy how quick

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<v Speaker 5>things are moving. And it certainly appeals. I mean, the

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<v Speaker 5>idea that you can just have a system make intelligent

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<v Speaker 5>choices with like kind of you know, unusual conditions.

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<v Speaker 6>It's so human.

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<v Speaker 5>It feels so human the way it works, and it

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<v Speaker 5>actually has shifted my thinking around how we see humans.

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<v Speaker 5>You know, I kind of feel like some sort of

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<v Speaker 5>finely tuned model that's had all these experiences through my

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<v Speaker 5>life as well.

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<v Speaker 3>You mentioned, you know, you're from Telescope.

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<v Speaker 1>For anyone who's never heard of telescope, do you want

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<v Speaker 1>to just give us the oneliner of what telescope is.

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<v Speaker 6>Yeah.

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<v Speaker 5>So we're generative aur company based in Australia, a small team,

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<v Speaker 5>but we're growing. We're working with brokers and publishers and

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<v Speaker 5>sort of FinTechs all across the world on solving problems

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<v Speaker 5>to do with capital markets and finance and most of

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<v Speaker 5>those problems we found to do with I guess, the

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<v Speaker 5>overwhelming nature of capital markets, of investing, of the choices

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<v Speaker 5>we have when it comes to investing, and just offering

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<v Speaker 5>co pilots or AI tools to help navigate that journey

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<v Speaker 5>when it comes to making a decision or thinking about

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<v Speaker 5>you know, a single stock or looking looking for something

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<v Speaker 5>maybe that matters to you, and just providing more information

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<v Speaker 5>to those investors.

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<v Speaker 3>Yeah.

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<v Speaker 1>So now if we want to kind of take a

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<v Speaker 1>step back and we're just talking about things that for

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<v Speaker 1>anyone who's not finger on the pulse or they're just

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<v Speaker 1>kind of reading the headlines hearing what's going on, if

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<v Speaker 1>we can help fill in a few of those gaps,

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<v Speaker 1>can you just tell us, like, maybe what's what's been

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<v Speaker 1>happening that's driving this real kick kind of into another.

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<v Speaker 4>Gear from where we've been sitting at Shares's And you know,

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<v Speaker 4>my own personal interest, I think probably three to four

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<v Speaker 4>years ago, largely with a technology called GPT two, which

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<v Speaker 4>was you know, the very you're probably aware of GPT four.

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<v Speaker 4>You might have heard that stuff on the headlines chat

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<v Speaker 4>GPT and stuff like that, but GPT two is an

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<v Speaker 4>earlier version of this and when it turned up. It

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<v Speaker 4>was very interesting. It's it was nothing like what we're

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<v Speaker 4>able to see today, but it had those hints. And

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<v Speaker 4>now with GPD four and some of the more recent

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<v Speaker 4>things both from Google and Anthropic and other companies involved

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<v Speaker 4>in this space, is actually it's doing really interesting stuff constantly,

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<v Speaker 4>consistently bringing new and interesting perspectives or creating new opportunities.

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<v Speaker 4>And I think this is the point where it's like,

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<v Speaker 4>you really have to be paying attention to this thing

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<v Speaker 4>as an organization because the potential is enormous and we

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<v Speaker 4>just don't know what we can and can't do with it.

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<v Speaker 4>We're still very much finding out.

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<v Speaker 6>I'd love to explain how it works as well.

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<v Speaker 3>Oh give there to go, don't hold back.

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<v Speaker 5>I was thinking about this morning, and it's very hard

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<v Speaker 5>to articulate sometimes these complex systems in simple ways. But

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<v Speaker 5>you know this, Just think about a simple sentence, like

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<v Speaker 5>this morning, I grabbed my surfboard and I caught an epic.

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<v Speaker 5>You know, I'm going to say most likely to say wave.

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<v Speaker 5>And we experienced this kind of auto predict on our phones.

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<v Speaker 5>And so if you think about a sentence and a conversation,

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<v Speaker 5>you know it follows a certain number of patterns, and

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<v Speaker 5>there's probability and mathematics involved in what's most likely to

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<v Speaker 5>be that final word, But maybe it was actually I

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<v Speaker 5>caught an epic fish, and so what's the probability of

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<v Speaker 5>me saying that?

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<v Speaker 6>And it's probably very low.

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<v Speaker 5>But then what open AI did in the beginning was

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<v Speaker 5>they actually just went, you know what, let's take auto

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<v Speaker 5>predict and let's put it through a large cluster of

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<v Speaker 5>computing units of GPUs, and let's look at all of

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<v Speaker 5>those relationships on a time series and almost think of

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<v Speaker 5>it like in three D space, so like visualize the

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<v Speaker 5>three D space of language, and so you think about

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<v Speaker 5>these kind of relationships between words, but then you start

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<v Speaker 5>to think of those in a multi dimensional manner, so

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<v Speaker 5>you start to think about relationships between paragraphs and the

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<v Speaker 5>question that's been asked, And that's really all that did,

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<v Speaker 5>and it's kind of fascinating that maybe this is all

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<v Speaker 5>intelligence really is.

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<v Speaker 1>Yeah, I've used it to try and you know, create

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<v Speaker 1>meal plans for my you know, one and a half

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<v Speaker 1>year old.

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<v Speaker 3>You know, when I was like and just like stuff

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<v Speaker 3>that I'm like.

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<v Speaker 1>Please help me, you know, and then when you think of,

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<v Speaker 1>you know, what, what mission we're on here, which is like,

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<v Speaker 1>we're trying to create more empowerment for people when it

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<v Speaker 1>comes to money and some of the things that you

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<v Speaker 1>guys have talked about about what is possible here?

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<v Speaker 3>Were AI or where we're at?

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<v Speaker 1>Where AI like, am really interested to, you know, get

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<v Speaker 1>your thoughts on how do you see this kind of

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<v Speaker 1>playing out for people when it comes to money or investing.

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<v Speaker 5>The irony is if this video ends up on YouTube,

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<v Speaker 5>it's probably going to be part of the future model

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<v Speaker 5>training that's going to help build the model. So maybe

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<v Speaker 5>maybe we have our moment to share our ideas personally,

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<v Speaker 5>I think if you think about the best investors of

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<v Speaker 5>all time, like Munger and Buffet and Howard, you know,

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<v Speaker 5>Howard markson all the long list of value investors, the

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<v Speaker 5>ones that actually study financial markets and history and a

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<v Speaker 5>multifaceted approach to investing.

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<v Speaker 6>They've spent a.

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<v Speaker 5>Good part of like thirty forty in Monger's case, maybe

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<v Speaker 5>seventy years of their life. I think Manger famously read

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<v Speaker 5>for thirty eight hours a week. He would allocate two

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<v Speaker 5>hours a week to meetings. So let's just presume he

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<v Speaker 5>spent seventy years of his investing career reading you know,

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<v Speaker 5>all sorts of literature, and not just annual filings and

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<v Speaker 5>SEC filings, but physics and you know, politics, history. I've

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<v Speaker 5>calculated that it's around about two billion words that Manger

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<v Speaker 5>has possibly consumed two to ten billion words in his

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<v Speaker 5>lifetime jet And then the question is that how much

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<v Speaker 5>does he recall? You know, that's forming his knowledge base,

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<v Speaker 5>but he's trained himself to know on that content. And

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<v Speaker 5>I think so there's no First of all, there's no

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<v Speaker 5>denying that the compound knowledge that AI models are going

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<v Speaker 5>through already, the intelligence that they're building on, is growing

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<v Speaker 5>at a large rate, and it will outperform somebody like

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<v Speaker 5>Manga or Buffett or whoever. And so I think the

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<v Speaker 5>opportunity in capital markets when it comes to an investing

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<v Speaker 5>is really interesting because maybe it levels the playing field.

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<v Speaker 6>If we have access to these models.

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<v Speaker 5>And they become super intelligent, and we just know how

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<v Speaker 5>to use them in the right way, and they're available publicly,

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<v Speaker 5>they're not privately run by Hedge Fund in New York,

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<v Speaker 5>maybe that kind of makes the markets more efficient and

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<v Speaker 5>makes them probably more fair. If a balance sheet comes

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<v Speaker 5>out on the stop market and you have something that

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<v Speaker 5>can read it and understand it because you can't, maybe

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<v Speaker 5>you know what to do, and so yeah, I think

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<v Speaker 5>maybe it creates more opportunity for abundance when it comes

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<v Speaker 5>to investing. That's my view on maybe where it could

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<v Speaker 5>go for ten ten years, and I'm an optimist. I'd

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<v Speaker 5>like to hope that it won't be held by you know,

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<v Speaker 5>concentrated powers and on Wall Street.

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<v Speaker 6>That's my view.

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<v Speaker 4>I would love to read thirty eight hours a week.

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<v Speaker 4>That's I love reading, and that sounds great, but you know, yeah,

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<v Speaker 4>but the other reflection that I have, and I think

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<v Speaker 4>is very key to us here at share These, is

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<v Speaker 4>that it's so much about wealth, is personal to you,

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<v Speaker 4>to your your family, your situation, all of these things.

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<v Speaker 4>And until a technology like AI turns up and is

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<v Speaker 4>able to take all of that context into account as

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<v Speaker 4>it provides you within science and take the information that's

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<v Speaker 4>been given. You know, maybe you're a particle physicist. You

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<v Speaker 4>don't need somebody to give you baby words on the

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<v Speaker 4>nature of particle physics, but you're trying to evaluate a

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<v Speaker 4>company that's working in that field, and you don't understand

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<v Speaker 4>the financial terms. You know, it's something that can understand

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<v Speaker 4>what you know, what you don't. The gaps that you

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<v Speaker 4>have in your understanding and what's relevant for you really

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<v Speaker 4>changes the picture in my view, because people can really

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<v Speaker 4>leverage the opportunities that they have personally and solve the

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<v Speaker 4>problems that they have personally and apply their personal values.

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<v Speaker 4>You know, get these tools. And I think we've talked

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<v Speaker 4>about this several times, the idea that AI at the

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<v Speaker 4>moment anyway, we're kind of anticipating and acting in a

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<v Speaker 4>copilot role. It's sitting there at like a friend, a buddy.

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<v Speaker 4>It's help us understand. It's not making decisions for us,

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<v Speaker 4>it's helping us understand the things that are important to us.

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<v Speaker 4>And when that happens, it just feels like it's really

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<v Speaker 4>really magical and really really useful. You know, Sonya's heard

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<v Speaker 4>me say this many many times. People need to be

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<v Speaker 4>building an intuition about these tools and how they work,

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<v Speaker 4>like you can't use them half as effectively. You know,

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<v Speaker 4>almost all of our listeners who've heard anything about this stuff,

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<v Speaker 4>will I have heard equal amounts of like, wow, this

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<v Speaker 4>is amazing at massive hype and also oh hallucinations, they lie,

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<v Speaker 4>they make stuff up, they do all that, and the

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<v Speaker 4>trekkers you get way better outcomes if you invest the

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<v Speaker 4>time to understand the strengths and weaknesses, you know, spend

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<v Speaker 4>some time playing with them. It doesn't really matter whether

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<v Speaker 4>you're doing serious work stuff or whether you're just toying around.

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<v Speaker 5>And if you tried the audio feature within chat GPT,

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<v Speaker 5>so you just have it on in your car and

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<v Speaker 5>talk to it.

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<v Speaker 6>That's fascinating. I've done it with my kids. So I

0:13:07.960 --> 0:13:08.880
<v Speaker 6>picked them up from school.

0:13:08.880 --> 0:13:12.720
<v Speaker 5>We've got three kids, and just take a moment to

0:13:12.760 --> 0:13:16.920
<v Speaker 5>explain my kids' ages, their names, and their interests. And

0:13:16.960 --> 0:13:20.079
<v Speaker 5>then I ask chat GPT to go around the car

0:13:20.559 --> 0:13:23.880
<v Speaker 5>one by one and ask them an interesting question that

0:13:23.880 --> 0:13:26.480
<v Speaker 5>they have to answer, and it becomes a homework task.

0:13:26.960 --> 0:13:29.880
<v Speaker 5>And I just drive and I let this AI agent

0:13:29.960 --> 0:13:32.719
<v Speaker 5>take over the conversation in the car and they love it.

0:13:32.720 --> 0:13:33.560
<v Speaker 6>It's fascinating.

0:13:34.320 --> 0:13:36.559
<v Speaker 3>I love it. It's like your own little family podcast.

0:13:36.960 --> 0:13:37.400
<v Speaker 6>Yeah.

0:13:37.559 --> 0:13:40.439
<v Speaker 5>Well, remember the feeling when we had an Encyclopedia Britannica

0:13:40.640 --> 0:13:44.120
<v Speaker 5>or those you know, or even a map of the world.

0:13:44.600 --> 0:13:46.880
<v Speaker 5>I mean it's the same experience for them, just at

0:13:46.880 --> 0:13:47.959
<v Speaker 5>a much deeper level.

0:13:48.240 --> 0:13:50.480
<v Speaker 3>Yeah. Great. Like so talking about getting stuck.

0:13:50.280 --> 0:13:53.400
<v Speaker 1>In you know, one thing I think of is like,

0:13:53.440 --> 0:13:56.040
<v Speaker 1>sometimes the problems don't change, it's just the solutions. All

0:13:56.040 --> 0:13:58.600
<v Speaker 1>the ways we might solve those problems become different as

0:13:58.800 --> 0:14:02.200
<v Speaker 1>different technologies and things like that. And you know, one

0:14:02.240 --> 0:14:05.840
<v Speaker 1>thing we are launching soon is and working with Telescope

0:14:05.840 --> 0:14:09.480
<v Speaker 1>to launch that is the AI search. And you know,

0:14:09.559 --> 0:14:11.800
<v Speaker 1>one of the problems that exists and one of the

0:14:11.880 --> 0:14:14.280
<v Speaker 1>you know, if people are investing, one of the top

0:14:14.360 --> 0:14:15.719
<v Speaker 1>questions is well, what should I.

0:14:15.679 --> 0:14:17.480
<v Speaker 3>Invest in or how should I think about this? And

0:14:17.480 --> 0:14:18.360
<v Speaker 3>what do I need to know?

0:14:18.520 --> 0:14:21.400
<v Speaker 1>And want to get more and build their knowledge around

0:14:21.440 --> 0:14:24.440
<v Speaker 1>investing and build their confidence in that space. So we're

0:14:24.480 --> 0:14:27.920
<v Speaker 1>launching this in New Zealand. But maybe Richard, do you

0:14:27.920 --> 0:14:30.520
<v Speaker 1>want to give us a bit of a insight into

0:14:30.520 --> 0:14:31.880
<v Speaker 1>what AI search is?

0:14:32.320 --> 0:14:32.520
<v Speaker 5>Yeah?

0:14:32.560 --> 0:14:36.720
<v Speaker 4>Yeah, yeah, So AI search for us is, you know,

0:14:36.760 --> 0:14:40.480
<v Speaker 4>we call the problem discovery. How do you find the

0:14:40.480 --> 0:14:44.560
<v Speaker 4>thing that you're actually interested in, whether you know it

0:14:45.080 --> 0:14:50.600
<v Speaker 4>you know well or not. And AI search is a

0:14:50.600 --> 0:14:53.080
<v Speaker 4>new take on this. It's the idea that you can

0:14:53.200 --> 0:14:56.040
<v Speaker 4>kind of give us a concept. You can search on

0:14:56.080 --> 0:14:59.680
<v Speaker 4>a concept or an idea what change in the world

0:15:00.680 --> 0:15:05.520
<v Speaker 4>might be true and then how would that impact what

0:15:05.560 --> 0:15:08.320
<v Speaker 4>you might look to invest in or what you might

0:15:08.440 --> 0:15:09.720
<v Speaker 4>look to think about.

0:15:09.920 --> 0:15:13.800
<v Speaker 5>I think the initial problem we were solving at Telescope,

0:15:13.800 --> 0:15:18.240
<v Speaker 5>even prior to working with Chasi's, was that scenario playing

0:15:18.480 --> 0:15:21.640
<v Speaker 5>a situation, a theme, or an event or something that

0:15:21.720 --> 0:15:26.600
<v Speaker 5>was personal that mattered to you, and you know, stepping

0:15:26.640 --> 0:15:29.800
<v Speaker 5>through I guess the butterfly effect if you've heard that

0:15:29.880 --> 0:15:32.080
<v Speaker 5>term before, but the cause and effects, and then the

0:15:32.080 --> 0:15:35.479
<v Speaker 5>cause and effect. So going through this process of conducting

0:15:36.200 --> 0:15:39.960
<v Speaker 5>second third order thinking, which is an interesting topic, but

0:15:40.040 --> 0:15:44.840
<v Speaker 5>actually just relying on the one point seven trillion parameters

0:15:44.880 --> 0:15:49.560
<v Speaker 5>that open ai has in their knowledge base to rationally

0:15:49.640 --> 0:15:52.600
<v Speaker 5>just search for stocks based on that theme, so not

0:15:52.680 --> 0:15:56.280
<v Speaker 5>only unpack the idea that you had, but also just

0:15:56.320 --> 0:16:01.640
<v Speaker 5>to rationally pick the stocks that match the idea. And

0:16:01.680 --> 0:16:04.760
<v Speaker 5>so I think, like then, just what's really interesting about

0:16:04.760 --> 0:16:07.160
<v Speaker 5>it is that it changes the way you then interact

0:16:07.280 --> 0:16:09.960
<v Speaker 5>in the real world because you suddenly realize you have

0:16:10.040 --> 0:16:13.720
<v Speaker 5>this tool available. So, for example, we had a user

0:16:13.800 --> 0:16:20.800
<v Speaker 5>go to take their EV to the mechanic and they said, oh,

0:16:20.920 --> 0:16:22.640
<v Speaker 5>the tires have a bit of wear on them, but

0:16:22.680 --> 0:16:27.119
<v Speaker 5>nothing unusual due to it being an EV and having evware,

0:16:27.840 --> 0:16:30.560
<v Speaker 5>And it's like, what do you mean by evwear? And

0:16:31.040 --> 0:16:33.960
<v Speaker 5>you put that into Chasi's ai search and it actually

0:16:34.000 --> 0:16:39.160
<v Speaker 5>explains like, well, they get twenty to twenty five percent

0:16:39.280 --> 0:16:42.120
<v Speaker 5>more wear on their tires because the car's heavier and

0:16:42.160 --> 0:16:44.840
<v Speaker 5>they have more talqu and so actually, like what's an

0:16:44.880 --> 0:16:48.960
<v Speaker 5>interesting side effect of the electrification of vehicles is that

0:16:49.480 --> 0:16:52.720
<v Speaker 5>now there's the tire companies that are starting to benefit

0:16:52.760 --> 0:16:56.000
<v Speaker 5>in their revenues growing. So that's an interesting idea that

0:16:56.040 --> 0:16:59.440
<v Speaker 5>you probably wouldn't have traditionally been able to just search.

0:16:59.520 --> 0:17:02.680
<v Speaker 5>And so I think going into the Chasias app and

0:17:02.760 --> 0:17:06.159
<v Speaker 5>just throwing in these ideas throughout the experience you have

0:17:06.280 --> 0:17:09.199
<v Speaker 5>in your career or you know, at home, and just

0:17:09.240 --> 0:17:12.600
<v Speaker 5>thinking about the products you use and the services you

0:17:12.720 --> 0:17:15.760
<v Speaker 5>use in a different way and being able to search

0:17:15.800 --> 0:17:17.840
<v Speaker 5>for those like you can you can put in you know,

0:17:18.320 --> 0:17:22.439
<v Speaker 5>maybe just something that an ETF doesn't already cover, Like

0:17:22.440 --> 0:17:25.679
<v Speaker 5>what about just female CEOs like that. Unfortunately it's not

0:17:25.720 --> 0:17:28.920
<v Speaker 5>an ETF, it's not an investment product, but perhaps that's

0:17:28.920 --> 0:17:32.840
<v Speaker 5>just something you want to invest in or search for

0:17:33.119 --> 0:17:36.479
<v Speaker 5>and then choose the single stocks that you actually believe in.

0:17:37.320 --> 0:17:40.520
<v Speaker 1>It's so like the opportunity I think and just helping

0:17:40.520 --> 0:17:44.520
<v Speaker 1>people build context, you know. Unfortunately, there's not a use

0:17:44.640 --> 0:17:48.440
<v Speaker 1>or no answer, you know, to any of the world

0:17:48.480 --> 0:17:51.840
<v Speaker 1>of investing. But I think what is really fascinating as

0:17:52.640 --> 0:17:55.240
<v Speaker 1>the more you can kind of understand the richness around

0:17:55.240 --> 0:17:59.800
<v Speaker 1>your decisions or the assumptions or the context, and so

0:18:00.080 --> 0:18:03.359
<v Speaker 1>can kind of maybe recognize some connections that you hadn't

0:18:03.480 --> 0:18:08.159
<v Speaker 1>more like most immediately made. Being able to explore and

0:18:08.400 --> 0:18:10.520
<v Speaker 1>you know, build that knowledge base or show some of

0:18:10.560 --> 0:18:14.800
<v Speaker 1>that logic or flesh those things out is super exciting

0:18:14.840 --> 0:18:17.199
<v Speaker 1>and I think can't wait for this to launch and

0:18:17.240 --> 0:18:21.399
<v Speaker 1>also to get feedback from our investors or around you know,

0:18:21.400 --> 0:18:24.720
<v Speaker 1>how this is helping them searching and get more interested

0:18:25.040 --> 0:18:28.080
<v Speaker 1>and building a portfolio that's right for them. I think

0:18:28.119 --> 0:18:30.480
<v Speaker 1>we've come to the end of our time now, and

0:18:30.520 --> 0:18:32.440
<v Speaker 1>I think just so grateful to have you guys here

0:18:32.480 --> 0:18:35.679
<v Speaker 1>and sharing your insights and what's been a really fascinating

0:18:35.800 --> 0:18:39.960
<v Speaker 1>conversation and shared some cool tools that people can use

0:18:40.040 --> 0:18:43.760
<v Speaker 1>to get stuck in that. Yeah, just did just want

0:18:43.760 --> 0:18:46.840
<v Speaker 1>to say thanks heats for sharing.

0:18:47.520 --> 0:18:47.879
<v Speaker 5>Thank you