WEBVTT - Lunar New Year, Artificial Intelligence Scare Trade

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<v Speaker 1>Bloomberg Audio Studios, podcasts, radio news. Welcome to the Daybreak

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<v Speaker 1>Asia podcast. I'm Doug Prisner. Trading in global equities was

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<v Speaker 1>muted on Monday given a number of holidays. We have

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<v Speaker 1>lunar New Year festivities underway in the Asia, Pacific and

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<v Speaker 1>in the States. Markets were closed on Monday to observe

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<v Speaker 1>Presidents Day. Now in the week ahead, disruption from AI

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<v Speaker 1>is likely to continue as a theme. Last week, nearly

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<v Speaker 1>every part of the financial sector was hit, and strategist

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<v Speaker 1>at JP Morgan Chase are urging caution on stocks at

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<v Speaker 1>risk of AI driven cannibalization, and other Wall Street firms

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<v Speaker 1>are creating tools to capitalize on this divergence. Goldman Sachs,

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<v Speaker 1>as an example, has launched a new basket of software

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<v Speaker 1>stocks that goes long firms that will benefit from AI

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<v Speaker 1>adoption and short companies whose workflows could be REPLA placed

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<v Speaker 1>for a closer look. I'm joined by Stephanie Leungshi is

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<v Speaker 1>the CIO at Stashaway. Stephanie joins from our studios in

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<v Speaker 1>Hong Kong. Thank you for being here. It seems clear

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<v Speaker 1>that AI has ushered in disruption across a range of industries.

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<v Speaker 1>How do you understand this moment in the evolution of

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<v Speaker 1>artificial intelligence.

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<v Speaker 2>I think two things.

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<v Speaker 3>A number one is that Anthropic released its latest model

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<v Speaker 3>together with open AI, and they are much more powerful

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<v Speaker 3>models than previously. And what these new models enable kind

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<v Speaker 3>of I guess us to do is actually to build

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<v Speaker 3>even more powerful agents. So if you think about the

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<v Speaker 3>latest kind of I guess development, which is something called

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<v Speaker 3>open call, it's basically an agent that you can deploy

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<v Speaker 3>on your home PC. And actually, I don't know if

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<v Speaker 3>you've tried it. I've actually started deploying my own agents

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<v Speaker 3>in my PC as well using open plot and it's

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<v Speaker 3>quite easy, to be frank, and it can help you

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<v Speaker 3>to do a lot of things right. It can help

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<v Speaker 3>you to automate your daily routine, help you to automate

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<v Speaker 3>any workflow that you specify. So I think the fear

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<v Speaker 3>is that once these agents are kind of so easy

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<v Speaker 3>to deploy, then you don't need to buy any software,

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<v Speaker 3>right you can just ask an agent to build your

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<v Speaker 3>software and it'll come back with what you require in

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<v Speaker 3>a very very short time and do it on a

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<v Speaker 3>very cost effective basis. So there's a question about, okay,

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<v Speaker 3>whether or not all these software companies will actually face

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<v Speaker 3>extential kind of crises. And I think we do have

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<v Speaker 3>to separate these companies into two groups.

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<v Speaker 2>The first group is.

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<v Speaker 3>The ones that have mode, which I mean from my perspective,

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<v Speaker 3>the modes are defined as for example, companies with a

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<v Speaker 3>distribution strong distribution like Microsoft or SAPE. These are very

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<v Speaker 3>very entrenched with the enterprises, and of course the software

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<v Speaker 3>companies that have their own kind of understanding of the

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<v Speaker 3>data layer.

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<v Speaker 2>For example, if you think about the securities.

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<v Speaker 3>Companies, right, they have a very deep understanding of how

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<v Speaker 3>to kind of think about security and how to survey

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<v Speaker 3>the data to to to to do their job. And

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<v Speaker 3>these are not that easy to replace. You can't just

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<v Speaker 3>tell an AI agent to build me a company with

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<v Speaker 3>distribution or a company with with a deep understanding of cybersecurity.

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<v Speaker 3>The second group of company are the ones that have

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<v Speaker 3>not a lot of modes, uh, And I mean those

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<v Speaker 3>are kind of I think companies that for example, serve

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<v Speaker 3>single purposes. For example, if let's say a company has

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<v Speaker 3>a SAA business that just focus on providing a scheduling tool,

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<v Speaker 3>I think that is a mode that is easy much

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<v Speaker 3>easier for agent to crack. I mean, you can just

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<v Speaker 3>spin up agents and and and tell it to build that.

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<v Speaker 3>So I do think that there are descriptions to the sector.

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<v Speaker 3>And then also I think on a broader basis, if

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<v Speaker 3>you think about simple kind of old school supply and demand,

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<v Speaker 3>there's going to be a lot of supply of software

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<v Speaker 3>that's coming right, and and the question is that is

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<v Speaker 3>there a so much demand to have solve this supply?

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<v Speaker 2>And I think given that the supply.

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<v Speaker 3>Is also coming up at very very low cost, I

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<v Speaker 3>mean that is a true disruption for the whole software industry.

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<v Speaker 3>But I do think that the market has been selling

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<v Speaker 3>down kind of quite in discriminately. And also these are

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<v Speaker 3>are a bit overblown.

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<v Speaker 1>So one of the things that I want to focus

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<v Speaker 1>on the disruption from AI that has the potential to

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<v Speaker 1>impact nearly every part of the financial services sector, whether

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<v Speaker 1>you're talking wealth managers, insurance brokers, even some of the

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<v Speaker 1>big banks, boutique advisors. Now I know your firm, stash Way,

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<v Speaker 1>is a digital investment platform. Do you think that this

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<v Speaker 1>is necessarily going to engender a lot more in the

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<v Speaker 1>way of competition?

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<v Speaker 3>Yeah, And I think, I mean, believe me, this is

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<v Speaker 3>like the daily conversation we have in the c suite

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<v Speaker 3>as well, right, And I think there are two things

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<v Speaker 3>that I think that we need to think about in

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<v Speaker 3>terms of whether or not these would disrupt the financial industry.

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<v Speaker 2>The first thing is, of course.

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<v Speaker 3>The regulations, because I mean all of us are are

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<v Speaker 3>tightly regulated. For example, I mean we have we have

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<v Speaker 3>licenses with regulators in all the markets that we operate,

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<v Speaker 3>and I mean these are very very strict kind of

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<v Speaker 3>I guess regulatory kind of restrictions that we have that

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<v Speaker 3>we need to operate within.

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<v Speaker 2>The question is I mean if even if somebody.

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<v Speaker 3>Builds an AI agent, right, I mean you need you

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<v Speaker 3>still need to comply with other regulations. You need to

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<v Speaker 3>file a license with the regulator. So I mean there

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<v Speaker 3>is a level of expertise that is required to do

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<v Speaker 3>all these And I think from a regulator's perspective, I mean,

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<v Speaker 3>regulators are typically slower to react to these kind of innovations, right,

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<v Speaker 3>They don't have a framework to approve, for example.

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<v Speaker 2>An AI agent. Can an AI agent actually give advice

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<v Speaker 2>or not?

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<v Speaker 3>These are sort of still unanswered question because I mean

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<v Speaker 3>the AI agent, of course is not licensed. So I

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<v Speaker 3>think that's the first kind of big question mark as

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<v Speaker 3>to whether or not AI can or AI agents kind

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<v Speaker 3>of independently can disrupt the industry. Secondly, if you think

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<v Speaker 3>all these kind of financial companies, I mean we're not

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<v Speaker 3>standing still, right, We're not sitting around and and not

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<v Speaker 3>doing anything.

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<v Speaker 2>Uh.

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<v Speaker 3>In fact, I think a lot of the bigger financial

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<v Speaker 3>industries companies are innovating and and sort of investing a

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<v Speaker 3>lot of money into the into building out the own

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<v Speaker 3>A capabilities.

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<v Speaker 1>Uh.

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<v Speaker 3>If you look at the for example, in JP Morgan

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<v Speaker 3>or Goldman or or I mean even in the stashway ourselves,

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<v Speaker 3>we're investing a lot in building out our AA capabilities,

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<v Speaker 3>and I mean those are I think the bottleneck still

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<v Speaker 3>remains the sort of I guess, the the resources that

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<v Speaker 3>you have in order to build these out, because I

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<v Speaker 3>mean using tokens or or using these alblems, it's not cheap, right,

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<v Speaker 3>it doesn't come at free cost. And therefore I do

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<v Speaker 3>think that companies with platform, with resources and with regulatory

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<v Speaker 3>kind of approvals will still remain the bigger players.

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<v Speaker 1>So we've seen how the adoption of artificial intelligence has

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<v Speaker 1>impacted the hardware market, particularly in the Asia Pacific region,

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<v Speaker 1>so much so that we're talking now about a shortage

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<v Speaker 1>of memory chips, particularly the high bandwidth memory that's necessary

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<v Speaker 1>to work with these various AI platforms. Are you seeing

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<v Speaker 1>opportunity here? How do you understand the shortage that we're

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<v Speaker 1>seeing in memory?

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<v Speaker 3>Yeah, I think the I mean, of course, a memory

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<v Speaker 3>industry has gone through a lot of consolidation in the past,

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<v Speaker 3>and now we have basically kind of three big companies

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<v Speaker 3>supplying most of.

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<v Speaker 2>The memory chips. I mean, memory historically has been a

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<v Speaker 2>cyclical industry, and I mean.

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<v Speaker 3>That is because basically it goes with the industry cycle,

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<v Speaker 3>right when when I mean, when the economy is hot,

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<v Speaker 3>there's a lot more demand for memory and these kind

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<v Speaker 3>of chips. When the economy is not so hot, then

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<v Speaker 3>basically there is oversupply. And typically, I think if you

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<v Speaker 3>look at the AI development today, as we go from

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<v Speaker 3>kind of model training to inference, there's a lot more

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<v Speaker 3>demand for memory because because of the thingand kind of

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<v Speaker 3>context windows.

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<v Speaker 2>So when we do inference, when we ask.

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<v Speaker 3>GPT a question and we carry on these conversations very very,

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<v Speaker 3>very very long time, and therefore there's a lot more

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<v Speaker 3>that needs to be stored in memory for the model

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<v Speaker 3>to be able to have a prolonged conversation with the

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<v Speaker 3>user and This is the main difference between inference and training.

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<v Speaker 3>So in the past few years, when the industry or

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<v Speaker 3>when token usage was actually mostly focused in training, the

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<v Speaker 3>demand on memory was not that big. Right right now, actually,

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<v Speaker 3>we're just starting to see the increase and ramp up

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<v Speaker 3>and demand for memory, and I think as the AI

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<v Speaker 3>kind of agent usage proliferates starting this year, we're actually

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<v Speaker 3>going to see even more demand. And the problem is

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<v Speaker 3>that because the memory supply is actually focused on these

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<v Speaker 3>three companies, it's not that easy for them or it's

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<v Speaker 3>not that fast for them to ramp up capacity, so

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<v Speaker 3>I think at least for the next few months or

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<v Speaker 3>the next year, So the visibility of a memory shortage

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<v Speaker 3>is actually still remains quite large. And if look at

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<v Speaker 3>some of these companies, they're still trading at pretty decent

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<v Speaker 3>valuations because the market historically has viewed them as cyclical

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<v Speaker 3>rather than structural. So I do think that there is

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<v Speaker 3>more to go. Of course, in an air term, a

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<v Speaker 3>lot of these companies are quite overbought, but I think

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<v Speaker 3>these are companies that you add to if there is

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<v Speaker 3>a correction.

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<v Speaker 1>So when you consider how the technology has been evolving

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<v Speaker 1>over time, and I'm wondering whether or not, there is

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<v Speaker 1>a risk here that what we're talking about today is

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<v Speaker 1>being cutting edge becomes obsolete six months from now, and

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<v Speaker 1>that if you're investing in that, if you're making a

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<v Speaker 1>capital expenditure in that type of technology, that you could

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<v Speaker 1>be forced to remain competitive. You're going to be forced

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<v Speaker 1>to have to upgrade.

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<v Speaker 3>Yes, But also I think because the demand is so

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<v Speaker 3>vague the UH I guess, the risk of UH investing

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<v Speaker 3>in assets that they appreciate very very quickly is today lower.

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<v Speaker 3>So if you look at, for example, the Nvidia chips,

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<v Speaker 3>the older chips actually don't follow that much in value. Indeed,

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<v Speaker 3>if you look at kind of the recent pricing, they've

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<v Speaker 3>actually been going higher. And that's because the way it

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<v Speaker 3>kind of I guess the whole AI stack works, the

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<v Speaker 3>most cutting edge hardware is used for training. But today

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<v Speaker 3>we have an addititional demand which is inference, right, which

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<v Speaker 3>is basically I mean us talking to GBT and also

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<v Speaker 3>using agents. These tasks actually don't require the cutting edge

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<v Speaker 3>chips that are required as of training. So we have

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<v Speaker 3>started another wave of of of of I guess AI

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<v Speaker 3>application and these applications actually can make use of the

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<v Speaker 3>older UH kind of uh, I guess older and less

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<v Speaker 3>advanced chips. I think the analogy maybe we can draw

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<v Speaker 3>the iPhone, right, I mean, iPhone basically has a new

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<v Speaker 3>version every year, but the old iPhones actually still retain

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<v Speaker 3>the value because you can actually resell these to other

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<v Speaker 3>kind of I guess, other users that do not demand

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<v Speaker 3>such a cutting h iPhone. So perhaps I mean this

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<v Speaker 3>is kind of how I would categorize or characterize the

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<v Speaker 3>the the AI hardware, because the demand is so big

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<v Speaker 3>that needs to be satisfied. So and also the demand

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<v Speaker 3>is a spectrum of high end, lower end, medium end.

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<v Speaker 3>So I do think that the risk of kind of

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<v Speaker 3>investing in technologies that depreciates or fates is it's lower.

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<v Speaker 1>Stephanie, before I let you go, we are obviously in

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<v Speaker 1>the midst of the lunar New Year holidays, celebrating the

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<v Speaker 1>beginning of the Year of the Horse. Can I ask

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<v Speaker 1>you to offer some wisdom?

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<v Speaker 3>This year is the year of the fire horse, which

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<v Speaker 3>tends to be I guess fast running and and I

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<v Speaker 3>guess quite vivid. So I think in a sense, I

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<v Speaker 3>mean it's sort of I guess shells with our that

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<v Speaker 3>this year we're going to see a lot of upside

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<v Speaker 3>downs and volatility and times. I mean, think about kind

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<v Speaker 3>of what we've gone through already in the last month

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<v Speaker 3>and a half. I think that's sort of a taste

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<v Speaker 3>that was to come and problems. I mean, that's what

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<v Speaker 3>the fire Horse is going to bring us.

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<v Speaker 1>Okay, Stephanie, Happy New Year too. Thank you so very much.

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<v Speaker 1>Stephanie Ljung is the CIO of Stashuay joining us here

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<v Speaker 1>on the Daybreak Asia podcast. Thanks for listening to today's

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<v Speaker 1>episode of the Bloomberg Daybreak Asia Edition podcast. Each weekday,

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<v Speaker 1>we look at the story shaping markets, finance, and geopolitics

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<v Speaker 1>in the Asia Pacific. You can find us on Apple, Spotify,

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<v Speaker 1>the Bloomberg Podcast YouTube channel, or anywhere else you listen.

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<v Speaker 1>Join us again tomorrow for insight on the market moves

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<v Speaker 1>from Hong Kong to Singapore and Australia. I'm Doug Prisoner

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<v Speaker 1>and this is Bloomberg