Speaker 1: Bloomberg Audio Studios, podcasts, radio news. Welcome to the Daybreak
Speaker 1: Asia podcast. I'm Doug Prisner. Trading in global equities was
Speaker 1: muted on Monday given a number of holidays. We have
Speaker 1: lunar New Year festivities underway in the Asia, Pacific and
Speaker 1: in the States. Markets were closed on Monday to observe
Speaker 1: Presidents Day. Now in the week ahead, disruption from AI
Speaker 1: is likely to continue as a theme. Last week, nearly
Speaker 1: every part of the financial sector was hit, and strategist
Speaker 1: at JP Morgan Chase are urging caution on stocks at
Speaker 1: risk of AI driven cannibalization, and other Wall Street firms
Speaker 1: are creating tools to capitalize on this divergence. Goldman Sachs,
Speaker 1: as an example, has launched a new basket of software
Speaker 1: stocks that goes long firms that will benefit from AI
Speaker 1: adoption and short companies whose workflows could be REPLA placed
Speaker 1: for a closer look. I'm joined by Stephanie Leungshi is
Speaker 1: the CIO at Stashaway. Stephanie joins from our studios in
Speaker 1: Hong Kong. Thank you for being here. It seems clear
Speaker 1: that AI has ushered in disruption across a range of industries.
Speaker 1: How do you understand this moment in the evolution of
Speaker 1: artificial intelligence.
Speaker 2: I think two things.
Speaker 3: A number one is that Anthropic released its latest model
Speaker 3: together with open AI, and they are much more powerful
Speaker 3: models than previously. And what these new models enable kind
Speaker 3: of I guess us to do is actually to build
Speaker 3: even more powerful agents. So if you think about the
Speaker 3: latest kind of I guess development, which is something called
Speaker 3: open call, it's basically an agent that you can deploy
Speaker 3: on your home PC. And actually, I don't know if
Speaker 3: you've tried it. I've actually started deploying my own agents
Speaker 3: in my PC as well using open plot and it's
Speaker 3: quite easy, to be frank, and it can help you
Speaker 3: to do a lot of things right. It can help
Speaker 3: you to automate your daily routine, help you to automate
Speaker 3: any workflow that you specify. So I think the fear
Speaker 3: is that once these agents are kind of so easy
Speaker 3: to deploy, then you don't need to buy any software,
Speaker 3: right you can just ask an agent to build your
Speaker 3: software and it'll come back with what you require in
Speaker 3: a very very short time and do it on a
Speaker 3: very cost effective basis. So there's a question about, okay,
Speaker 3: whether or not all these software companies will actually face
Speaker 3: extential kind of crises. And I think we do have
Speaker 3: to separate these companies into two groups.
Speaker 2: The first group is.
Speaker 3: The ones that have mode, which I mean from my perspective,
Speaker 3: the modes are defined as for example, companies with a
Speaker 3: distribution strong distribution like Microsoft or SAPE. These are very
Speaker 3: very entrenched with the enterprises, and of course the software
Speaker 3: companies that have their own kind of understanding of the
Speaker 3: data layer.
Speaker 2: For example, if you think about the securities.
Speaker 3: Companies, right, they have a very deep understanding of how
Speaker 3: to kind of think about security and how to survey
Speaker 3: the data to to to to do their job. And
Speaker 3: these are not that easy to replace. You can't just
Speaker 3: tell an AI agent to build me a company with
Speaker 3: distribution or a company with with a deep understanding of cybersecurity.
Speaker 3: The second group of company are the ones that have
Speaker 3: not a lot of modes, uh, And I mean those
Speaker 3: are kind of I think companies that for example, serve
Speaker 3: single purposes. For example, if let's say a company has
Speaker 3: a SAA business that just focus on providing a scheduling tool,
Speaker 3: I think that is a mode that is easy much
Speaker 3: easier for agent to crack. I mean, you can just
Speaker 3: spin up agents and and and tell it to build that.
Speaker 3: So I do think that there are descriptions to the sector.
Speaker 3: And then also I think on a broader basis, if
Speaker 3: you think about simple kind of old school supply and demand,
Speaker 3: there's going to be a lot of supply of software
Speaker 3: that's coming right, and and the question is that is
Speaker 3: there a so much demand to have solve this supply?
Speaker 2: And I think given that the supply.
Speaker 3: Is also coming up at very very low cost, I
Speaker 3: mean that is a true disruption for the whole software industry.
Speaker 3: But I do think that the market has been selling
Speaker 3: down kind of quite in discriminately. And also these are
Speaker 3: are a bit overblown.
Speaker 1: So one of the things that I want to focus
Speaker 1: on the disruption from AI that has the potential to
Speaker 1: impact nearly every part of the financial services sector, whether
Speaker 1: you're talking wealth managers, insurance brokers, even some of the
Speaker 1: big banks, boutique advisors. Now I know your firm, stash Way,
Speaker 1: is a digital investment platform. Do you think that this
Speaker 1: is necessarily going to engender a lot more in the
Speaker 1: way of competition?
Speaker 3: Yeah, And I think, I mean, believe me, this is
Speaker 3: like the daily conversation we have in the c suite
Speaker 3: as well, right, And I think there are two things
Speaker 3: that I think that we need to think about in
Speaker 3: terms of whether or not these would disrupt the financial industry.
Speaker 2: The first thing is, of course.
Speaker 3: The regulations, because I mean all of us are are
Speaker 3: tightly regulated. For example, I mean we have we have
Speaker 3: licenses with regulators in all the markets that we operate,
Speaker 3: and I mean these are very very strict kind of
Speaker 3: I guess regulatory kind of restrictions that we have that
Speaker 3: we need to operate within.
Speaker 2: The question is I mean if even if somebody.
Speaker 3: Builds an AI agent, right, I mean you need you
Speaker 3: still need to comply with other regulations. You need to
Speaker 3: file a license with the regulator. So I mean there
Speaker 3: is a level of expertise that is required to do
Speaker 3: all these And I think from a regulator's perspective, I mean,
Speaker 3: regulators are typically slower to react to these kind of innovations, right,
Speaker 3: They don't have a framework to approve, for example.
Speaker 2: An AI agent. Can an AI agent actually give advice
Speaker 2: or not?
Speaker 3: These are sort of still unanswered question because I mean
Speaker 3: the AI agent, of course is not licensed. So I
Speaker 3: think that's the first kind of big question mark as
Speaker 3: to whether or not AI can or AI agents kind
Speaker 3: of independently can disrupt the industry. Secondly, if you think
Speaker 3: all these kind of financial companies, I mean we're not
Speaker 3: standing still, right, We're not sitting around and and not
Speaker 3: doing anything.
Speaker 2: Uh.
Speaker 3: In fact, I think a lot of the bigger financial
Speaker 3: industries companies are innovating and and sort of investing a
Speaker 3: lot of money into the into building out the own
Speaker 3: A capabilities.
Speaker 1: Uh.
Speaker 3: If you look at the for example, in JP Morgan
Speaker 3: or Goldman or or I mean even in the stashway ourselves,
Speaker 3: we're investing a lot in building out our AA capabilities,
Speaker 3: and I mean those are I think the bottleneck still
Speaker 3: remains the sort of I guess, the the resources that
Speaker 3: you have in order to build these out, because I
Speaker 3: mean using tokens or or using these alblems, it's not cheap, right,
Speaker 3: it doesn't come at free cost. And therefore I do
Speaker 3: think that companies with platform, with resources and with regulatory
Speaker 3: kind of approvals will still remain the bigger players.
Speaker 1: So we've seen how the adoption of artificial intelligence has
Speaker 1: impacted the hardware market, particularly in the Asia Pacific region,
Speaker 1: so much so that we're talking now about a shortage
Speaker 1: of memory chips, particularly the high bandwidth memory that's necessary
Speaker 1: to work with these various AI platforms. Are you seeing
Speaker 1: opportunity here? How do you understand the shortage that we're
Speaker 1: seeing in memory?
Speaker 3: Yeah, I think the I mean, of course, a memory
Speaker 3: industry has gone through a lot of consolidation in the past,
Speaker 3: and now we have basically kind of three big companies
Speaker 3: supplying most of.
Speaker 2: The memory chips. I mean, memory historically has been a
Speaker 2: cyclical industry, and I mean.
Speaker 3: That is because basically it goes with the industry cycle,
Speaker 3: right when when I mean, when the economy is hot,
Speaker 3: there's a lot more demand for memory and these kind
Speaker 3: of chips. When the economy is not so hot, then
Speaker 3: basically there is oversupply. And typically, I think if you
Speaker 3: look at the AI development today, as we go from
Speaker 3: kind of model training to inference, there's a lot more
Speaker 3: demand for memory because because of the thingand kind of
Speaker 3: context windows.
Speaker 2: So when we do inference, when we ask.
Speaker 3: GPT a question and we carry on these conversations very very,
Speaker 3: very very long time, and therefore there's a lot more
Speaker 3: that needs to be stored in memory for the model
Speaker 3: to be able to have a prolonged conversation with the
Speaker 3: user and This is the main difference between inference and training.
Speaker 3: So in the past few years, when the industry or
Speaker 3: when token usage was actually mostly focused in training, the
Speaker 3: demand on memory was not that big. Right right now, actually,
Speaker 3: we're just starting to see the increase and ramp up
Speaker 3: and demand for memory, and I think as the AI
Speaker 3: kind of agent usage proliferates starting this year, we're actually
Speaker 3: going to see even more demand. And the problem is
Speaker 3: that because the memory supply is actually focused on these
Speaker 3: three companies, it's not that easy for them or it's
Speaker 3: not that fast for them to ramp up capacity, so
Speaker 3: I think at least for the next few months or
Speaker 3: the next year, So the visibility of a memory shortage
Speaker 3: is actually still remains quite large. And if look at
Speaker 3: some of these companies, they're still trading at pretty decent
Speaker 3: valuations because the market historically has viewed them as cyclical
Speaker 3: rather than structural. So I do think that there is
Speaker 3: more to go. Of course, in an air term, a
Speaker 3: lot of these companies are quite overbought, but I think
Speaker 3: these are companies that you add to if there is
Speaker 3: a correction.
Speaker 1: So when you consider how the technology has been evolving
Speaker 1: over time, and I'm wondering whether or not, there is
Speaker 1: a risk here that what we're talking about today is
Speaker 1: being cutting edge becomes obsolete six months from now, and
Speaker 1: that if you're investing in that, if you're making a
Speaker 1: capital expenditure in that type of technology, that you could
Speaker 1: be forced to remain competitive. You're going to be forced
Speaker 1: to have to upgrade.
Speaker 3: Yes, But also I think because the demand is so
Speaker 3: vague the UH I guess, the risk of UH investing
Speaker 3: in assets that they appreciate very very quickly is today lower.
Speaker 3: So if you look at, for example, the Nvidia chips,
Speaker 3: the older chips actually don't follow that much in value. Indeed,
Speaker 3: if you look at kind of the recent pricing, they've
Speaker 3: actually been going higher. And that's because the way it
Speaker 3: kind of I guess the whole AI stack works, the
Speaker 3: most cutting edge hardware is used for training. But today
Speaker 3: we have an addititional demand which is inference, right, which
Speaker 3: is basically I mean us talking to GBT and also
Speaker 3: using agents. These tasks actually don't require the cutting edge
Speaker 3: chips that are required as of training. So we have
Speaker 3: started another wave of of of of I guess AI
Speaker 3: application and these applications actually can make use of the
Speaker 3: older UH kind of uh, I guess older and less
Speaker 3: advanced chips. I think the analogy maybe we can draw
Speaker 3: the iPhone, right, I mean, iPhone basically has a new
Speaker 3: version every year, but the old iPhones actually still retain
Speaker 3: the value because you can actually resell these to other
Speaker 3: kind of I guess, other users that do not demand
Speaker 3: such a cutting h iPhone. So perhaps I mean this
Speaker 3: is kind of how I would categorize or characterize the
Speaker 3: the the AI hardware, because the demand is so big
Speaker 3: that needs to be satisfied. So and also the demand
Speaker 3: is a spectrum of high end, lower end, medium end.
Speaker 3: So I do think that the risk of kind of
Speaker 3: investing in technologies that depreciates or fates is it's lower.
Speaker 1: Stephanie, before I let you go, we are obviously in
Speaker 1: the midst of the lunar New Year holidays, celebrating the
Speaker 1: beginning of the Year of the Horse. Can I ask
Speaker 1: you to offer some wisdom?
Speaker 3: This year is the year of the fire horse, which
Speaker 3: tends to be I guess fast running and and I
Speaker 3: guess quite vivid. So I think in a sense, I
Speaker 3: mean it's sort of I guess shells with our that
Speaker 3: this year we're going to see a lot of upside
Speaker 3: downs and volatility and times. I mean, think about kind
Speaker 3: of what we've gone through already in the last month
Speaker 3: and a half. I think that's sort of a taste
Speaker 3: that was to come and problems. I mean, that's what
Speaker 3: the fire Horse is going to bring us.
Speaker 1: Okay, Stephanie, Happy New Year too. Thank you so very much.
Speaker 1: Stephanie Ljung is the CIO of Stashuay joining us here
Speaker 1: on the Daybreak Asia podcast. Thanks for listening to today's
Speaker 1: episode of the Bloomberg Daybreak Asia Edition podcast. Each weekday,
Speaker 1: we look at the story shaping markets, finance, and geopolitics
Speaker 1: in the Asia Pacific. You can find us on Apple, Spotify,
Speaker 1: the Bloomberg Podcast YouTube channel, or anywhere else you listen.
Speaker 1: Join us again tomorrow for insight on the market moves
Speaker 1: from Hong Kong to Singapore and Australia. I'm Doug Prisoner
Speaker 1: and this is Bloomberg