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Speaker 1: Bloomberg Audio Studios, podcasts, radio news. Welcome to the Daybreak

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Speaker 1: Asia podcast. I'm Doug Prisner. Trading in global equities was

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Speaker 1: muted on Monday given a number of holidays. We have

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Speaker 1: lunar New Year festivities underway in the Asia, Pacific and

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Speaker 1: in the States. Markets were closed on Monday to observe

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Speaker 1: Presidents Day. Now in the week ahead, disruption from AI

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Speaker 1: is likely to continue as a theme. Last week, nearly

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Speaker 1: every part of the financial sector was hit, and strategist

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Speaker 1: at JP Morgan Chase are urging caution on stocks at

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Speaker 1: risk of AI driven cannibalization, and other Wall Street firms

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Speaker 1: are creating tools to capitalize on this divergence. Goldman Sachs,

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Speaker 1: as an example, has launched a new basket of software

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Speaker 1: stocks that goes long firms that will benefit from AI

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Speaker 1: adoption and short companies whose workflows could be REPLA placed

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Speaker 1: for a closer look. I'm joined by Stephanie Leungshi is

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Speaker 1: the CIO at Stashaway. Stephanie joins from our studios in

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Speaker 1: Hong Kong. Thank you for being here. It seems clear

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Speaker 1: that AI has ushered in disruption across a range of industries.

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Speaker 1: How do you understand this moment in the evolution of

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Speaker 1: artificial intelligence.

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Speaker 2: I think two things.

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Speaker 3: A number one is that Anthropic released its latest model

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Speaker 3: together with open AI, and they are much more powerful

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Speaker 3: models than previously. And what these new models enable kind

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Speaker 3: of I guess us to do is actually to build

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Speaker 3: even more powerful agents. So if you think about the

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Speaker 3: latest kind of I guess development, which is something called

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Speaker 3: open call, it's basically an agent that you can deploy

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

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Speaker 3: you've tried it. I've actually started deploying my own agents

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Speaker 3: in my PC as well using open plot and it's

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Speaker 3: quite easy, to be frank, and it can help you

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Speaker 3: to do a lot of things right. It can help

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Speaker 3: you to automate your daily routine, help you to automate

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Speaker 3: any workflow that you specify. So I think the fear

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Speaker 3: is that once these agents are kind of so easy

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Speaker 3: to deploy, then you don't need to buy any software,

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Speaker 3: right you can just ask an agent to build your

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Speaker 3: software and it'll come back with what you require in

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Speaker 3: a very very short time and do it on a

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

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Speaker 3: whether or not all these software companies will actually face

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Speaker 3: extential kind of crises. And I think we do have

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Speaker 3: to separate these companies into two groups.

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Speaker 2: The first group is.

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Speaker 3: The ones that have mode, which I mean from my perspective,

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Speaker 3: the modes are defined as for example, companies with a

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Speaker 3: distribution strong distribution like Microsoft or SAPE. These are very

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Speaker 3: very entrenched with the enterprises, and of course the software

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Speaker 3: companies that have their own kind of understanding of the

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Speaker 3: data layer.

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Speaker 2: For example, if you think about the securities.

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Speaker 3: Companies, right, they have a very deep understanding of how

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Speaker 3: to kind of think about security and how to survey

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Speaker 3: the data to to to to do their job. And

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Speaker 3: these are not that easy to replace. You can't just

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Speaker 3: tell an AI agent to build me a company with

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Speaker 3: distribution or a company with with a deep understanding of cybersecurity.

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Speaker 3: The second group of company are the ones that have

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Speaker 3: not a lot of modes, uh, And I mean those

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Speaker 3: are kind of I think companies that for example, serve

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

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Speaker 3: a SAA business that just focus on providing a scheduling tool,

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Speaker 3: I think that is a mode that is easy much

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Speaker 3: easier for agent to crack. I mean, you can just

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Speaker 3: spin up agents and and and tell it to build that.

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Speaker 3: So I do think that there are descriptions to the sector.

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Speaker 3: And then also I think on a broader basis, if

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Speaker 3: you think about simple kind of old school supply and demand,

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Speaker 3: there's going to be a lot of supply of software

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Speaker 3: that's coming right, and and the question is that is

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Speaker 3: there a so much demand to have solve this supply?

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Speaker 2: And I think given that the supply.

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Speaker 3: Is also coming up at very very low cost, I

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Speaker 3: mean that is a true disruption for the whole software industry.

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Speaker 3: But I do think that the market has been selling

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Speaker 3: down kind of quite in discriminately. And also these are

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Speaker 3: are a bit overblown.

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Speaker 1: So one of the things that I want to focus

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Speaker 1: on the disruption from AI that has the potential to

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Speaker 1: impact nearly every part of the financial services sector, whether

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Speaker 1: you're talking wealth managers, insurance brokers, even some of the

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Speaker 1: big banks, boutique advisors. Now I know your firm, stash Way,

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Speaker 1: is a digital investment platform. Do you think that this

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Speaker 1: is necessarily going to engender a lot more in the

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Speaker 1: way of competition?

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Speaker 3: Yeah, And I think, I mean, believe me, this is

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Speaker 3: like the daily conversation we have in the c suite

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Speaker 3: as well, right, And I think there are two things

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Speaker 3: that I think that we need to think about in

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Speaker 3: terms of whether or not these would disrupt the financial industry.

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Speaker 2: The first thing is, of course.

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Speaker 3: The regulations, because I mean all of us are are

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Speaker 3: tightly regulated. For example, I mean we have we have

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Speaker 3: licenses with regulators in all the markets that we operate,

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Speaker 3: and I mean these are very very strict kind of

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Speaker 3: I guess regulatory kind of restrictions that we have that

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Speaker 3: we need to operate within.

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Speaker 2: The question is I mean if even if somebody.

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Speaker 3: Builds an AI agent, right, I mean you need you

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Speaker 3: still need to comply with other regulations. You need to

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Speaker 3: file a license with the regulator. So I mean there

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Speaker 3: is a level of expertise that is required to do

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Speaker 3: all these And I think from a regulator's perspective, I mean,

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Speaker 3: regulators are typically slower to react to these kind of innovations, right,

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Speaker 3: They don't have a framework to approve, for example.

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Speaker 2: An AI agent. Can an AI agent actually give advice

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Speaker 2: or not?

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Speaker 3: These are sort of still unanswered question because I mean

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Speaker 3: the AI agent, of course is not licensed. So I

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Speaker 3: think that's the first kind of big question mark as

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Speaker 3: to whether or not AI can or AI agents kind

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Speaker 3: of independently can disrupt the industry. Secondly, if you think

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Speaker 3: all these kind of financial companies, I mean we're not

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Speaker 3: standing still, right, We're not sitting around and and not

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Speaker 3: doing anything.

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Speaker 2: Uh.

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Speaker 3: In fact, I think a lot of the bigger financial

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Speaker 3: industries companies are innovating and and sort of investing a

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Speaker 3: lot of money into the into building out the own

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Speaker 3: A capabilities.

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Speaker 1: Uh.

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Speaker 3: If you look at the for example, in JP Morgan

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Speaker 3: or Goldman or or I mean even in the stashway ourselves,

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Speaker 3: we're investing a lot in building out our AA capabilities,

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Speaker 3: and I mean those are I think the bottleneck still

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Speaker 3: remains the sort of I guess, the the resources that

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Speaker 3: you have in order to build these out, because I

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Speaker 3: mean using tokens or or using these alblems, it's not cheap, right,

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Speaker 3: it doesn't come at free cost. And therefore I do

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Speaker 3: think that companies with platform, with resources and with regulatory

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Speaker 3: kind of approvals will still remain the bigger players.

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Speaker 1: So we've seen how the adoption of artificial intelligence has

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Speaker 1: impacted the hardware market, particularly in the Asia Pacific region,

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Speaker 1: so much so that we're talking now about a shortage

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Speaker 1: of memory chips, particularly the high bandwidth memory that's necessary

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Speaker 1: to work with these various AI platforms. Are you seeing

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Speaker 1: opportunity here? How do you understand the shortage that we're

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Speaker 1: seeing in memory?

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Speaker 3: Yeah, I think the I mean, of course, a memory

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Speaker 3: industry has gone through a lot of consolidation in the past,

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Speaker 3: and now we have basically kind of three big companies

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Speaker 3: supplying most of.

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Speaker 2: The memory chips. I mean, memory historically has been a

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Speaker 2: cyclical industry, and I mean.

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Speaker 3: That is because basically it goes with the industry cycle,

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Speaker 3: right when when I mean, when the economy is hot,

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Speaker 3: there's a lot more demand for memory and these kind

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Speaker 3: of chips. When the economy is not so hot, then

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Speaker 3: basically there is oversupply. And typically, I think if you

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Speaker 3: look at the AI development today, as we go from

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Speaker 3: kind of model training to inference, there's a lot more

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Speaker 3: demand for memory because because of the thingand kind of

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Speaker 3: context windows.

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Speaker 2: So when we do inference, when we ask.

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Speaker 3: GPT a question and we carry on these conversations very very,

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Speaker 3: very very long time, and therefore there's a lot more

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Speaker 3: that needs to be stored in memory for the model

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Speaker 3: to be able to have a prolonged conversation with the

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Speaker 3: user and This is the main difference between inference and training.

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Speaker 3: So in the past few years, when the industry or

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Speaker 3: when token usage was actually mostly focused in training, the

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Speaker 3: demand on memory was not that big. Right right now, actually,

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Speaker 3: we're just starting to see the increase and ramp up

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Speaker 3: and demand for memory, and I think as the AI

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Speaker 3: kind of agent usage proliferates starting this year, we're actually

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Speaker 3: going to see even more demand. And the problem is

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Speaker 3: that because the memory supply is actually focused on these

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Speaker 3: three companies, it's not that easy for them or it's

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Speaker 3: not that fast for them to ramp up capacity, so

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Speaker 3: I think at least for the next few months or

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Speaker 3: the next year, So the visibility of a memory shortage

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Speaker 3: is actually still remains quite large. And if look at

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Speaker 3: some of these companies, they're still trading at pretty decent

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Speaker 3: valuations because the market historically has viewed them as cyclical

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Speaker 3: rather than structural. So I do think that there is

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Speaker 3: more to go. Of course, in an air term, a

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Speaker 3: lot of these companies are quite overbought, but I think

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Speaker 3: these are companies that you add to if there is

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Speaker 3: a correction.

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Speaker 1: So when you consider how the technology has been evolving

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Speaker 1: over time, and I'm wondering whether or not, there is

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Speaker 1: a risk here that what we're talking about today is

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Speaker 1: being cutting edge becomes obsolete six months from now, and

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Speaker 1: that if you're investing in that, if you're making a

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Speaker 1: capital expenditure in that type of technology, that you could

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Speaker 1: be forced to remain competitive. You're going to be forced

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Speaker 1: to have to upgrade.

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Speaker 3: Yes, But also I think because the demand is so

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Speaker 3: vague the UH I guess, the risk of UH investing

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Speaker 3: in assets that they appreciate very very quickly is today lower.

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Speaker 3: So if you look at, for example, the Nvidia chips,

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

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Speaker 3: if you look at kind of the recent pricing, they've

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Speaker 3: actually been going higher. And that's because the way it

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Speaker 3: kind of I guess the whole AI stack works, the

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Speaker 3: most cutting edge hardware is used for training. But today

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Speaker 3: we have an addititional demand which is inference, right, which

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Speaker 3: is basically I mean us talking to GBT and also

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Speaker 3: using agents. These tasks actually don't require the cutting edge

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Speaker 3: chips that are required as of training. So we have

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Speaker 3: started another wave of of of of I guess AI

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Speaker 3: application and these applications actually can make use of the

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Speaker 3: older UH kind of uh, I guess older and less

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Speaker 3: advanced chips. I think the analogy maybe we can draw

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Speaker 3: the iPhone, right, I mean, iPhone basically has a new

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Speaker 3: version every year, but the old iPhones actually still retain

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Speaker 3: the value because you can actually resell these to other

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Speaker 3: kind of I guess, other users that do not demand

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Speaker 3: such a cutting h iPhone. So perhaps I mean this

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Speaker 3: is kind of how I would categorize or characterize the

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Speaker 3: the the AI hardware, because the demand is so big

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Speaker 3: that needs to be satisfied. So and also the demand

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Speaker 3: is a spectrum of high end, lower end, medium end.

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Speaker 3: So I do think that the risk of kind of

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Speaker 3: investing in technologies that depreciates or fates is it's lower.

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Speaker 1: Stephanie, before I let you go, we are obviously in

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Speaker 1: the midst of the lunar New Year holidays, celebrating the

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Speaker 1: beginning of the Year of the Horse. Can I ask

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Speaker 1: you to offer some wisdom?

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Speaker 3: This year is the year of the fire horse, which

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Speaker 3: tends to be I guess fast running and and I

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Speaker 3: guess quite vivid. So I think in a sense, I

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Speaker 3: mean it's sort of I guess shells with our that

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Speaker 3: this year we're going to see a lot of upside

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Speaker 3: downs and volatility and times. I mean, think about kind

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Speaker 3: of what we've gone through already in the last month

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Speaker 3: and a half. I think that's sort of a taste

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Speaker 3: that was to come and problems. I mean, that's what

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Speaker 3: the fire Horse is going to bring us.

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Speaker 1: Okay, Stephanie, Happy New Year too. Thank you so very much.

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Speaker 1: Stephanie Ljung is the CIO of Stashuay joining us here

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Speaker 1: on the Daybreak Asia podcast. Thanks for listening to today's

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Speaker 1: episode of the Bloomberg Daybreak Asia Edition podcast. Each weekday,

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Speaker 1: we look at the story shaping markets, finance, and geopolitics

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Speaker 1: in the Asia Pacific. You can find us on Apple, Spotify,

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Speaker 1: the Bloomberg Podcast YouTube channel, or anywhere else you listen.

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Speaker 1: Join us again tomorrow for insight on the market moves

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Speaker 1: from Hong Kong to Singapore and Australia. I'm Doug Prisoner

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Speaker 1: and this is Bloomberg