Speaker 1: You're listening to Asia Centric from Bloomberg Intelligence, the podcast
Speaker 1: that pulls back the curtain non global business so you
Speaker 1: can invest better across the Pacific rim. I'm Tom Corbett
Speaker 1: in Hong Kong, and.
Speaker 2: I'm John Lee. Artificial intelligence is generating a level of
Speaker 2: investor excitement not seen since the dot com era.
Speaker 1: Call it a feeding frenzy, call it a buying binge.
Speaker 1: The rush to board the AI bandwagon has powered America's
Speaker 1: Magnificent seven stocks to new heights, and giddy investors are
Speaker 1: rushing to get on board.
Speaker 2: Will artificial intelligence deliver on the high or investors at
Speaker 2: risk of being left empty handed? And what about Asian
Speaker 2: tech stocks? Can they catch up to their American counterparts.
Speaker 1: Let's bring in two Bloomberg Intelligence experts. Mandeep Seeing is
Speaker 1: Global head of Technology Research based in New York.
Speaker 2: And Robert Lee see Asia Tech Analysts based here in
Speaker 2: Hong Kong.
Speaker 1: Man Deep and Rob welcome to Asia Centric.
Speaker 3: Thanks for having us on and y'are excited to be
Speaker 3: on the podcast.
Speaker 4: Like wife Tom and John, Thanks for having us Mandy.
Speaker 2: Let's get straight into it. Are we in an AI
Speaker 2: driven tech bubble?
Speaker 3: Well? I think bubble is a term that gets used
Speaker 3: probably so frequently that in my lifetime I probably would
Speaker 3: have come across at least one hundred bubbles so far.
Speaker 3: So clearly, you know, there are times when things come
Speaker 3: across as fad and there isn't proper follow up in
Speaker 3: terms of monetizable opportunities. AI, especially generative AI, doesn't seem
Speaker 3: to be that type of fad. And the reason I
Speaker 3: say that is what we have seen so far is
Speaker 3: not only you know, in video really getting that pricing
Speaker 3: power in terms of the GPUs that are being used
Speaker 3: for AI training, but also follow up application, whether it's
Speaker 3: on the software side with chatbots that can streamline back
Speaker 3: end operations, contact center stuff, as well as other type
Speaker 3: of optimizations and copilot use for developers. So with Jenny
Speaker 3: and I, we are talking about workflows getting streamlined, and
Speaker 3: when we talk about workflows, they're across the board and
Speaker 3: we see a lot of potential over there.
Speaker 2: And you also mentioned these AI companies are monetizing artificial intelligence.
Speaker 2: Is that a key difference between say the tech bubble
Speaker 2: in nineteen ninety nine.
Speaker 3: I think so you are seeing a lot of upfront
Speaker 3: revenue for companies that are playing to this trend. And
Speaker 3: it is a secular tailwind in the sense that not
Speaker 3: only it's what Nvidia is doing right now, but think
Speaker 3: of the recurring revenue that these companies can generate through
Speaker 3: copilots or the subscription revenue streams that Microsoft is able
Speaker 3: to add on to its office bundle. I mean this
Speaker 3: is real. Companies are subscribing to this because they can
Speaker 3: see knowledge workers getting a productivity boost. So I would
Speaker 3: say whether it delivers on the promise that everyone feels
Speaker 3: this technology brings to the fore I mean we ourselves
Speaker 3: have done our forecast, a ten year forecast where we
Speaker 3: think it's going to drive one point three trillion in
Speaker 3: incremental revenue by twenty thirty two. So we think this
Speaker 3: is going to grow at a kegar of forty percent
Speaker 3: over the next ten years. But look, there will be
Speaker 3: ups and downs, as we have seen with any technology wave,
Speaker 3: and so far it seems to be delivering on the
Speaker 3: promise with revenue streams showing up in different companies that
Speaker 3: we follow.
Speaker 1: Robert Lee, I know that you've been through the dot
Speaker 1: com bubble as with the rest of us. What similarities
Speaker 1: and differences do you see between the AI craze now
Speaker 1: and the dot com bubble back then, Uh.
Speaker 4: Well, look at my reflection in the camera and a
Speaker 4: few more gray hairs, and I used to have I
Speaker 4: do remember the dot com bubble and I started my
Speaker 4: cell side career then. But I think the difference then
Speaker 4: was the sort of infrastructure was built out. The web
Speaker 4: was built out with little thought at that point as
Speaker 4: to how again, what's a business model, how is it
Speaker 4: going to be monetized? Whereas AI? You know, I guess
Speaker 4: to many people hadn't really thought or heard of AI
Speaker 4: until chat GPT came along about a year ago. But
Speaker 4: AI has been around for decades in a form of
Speaker 4: machine learning as it's referred to, and so there's been
Speaker 4: a lot of research at university level and at corporate
Speaker 4: level going into AI for many decades. What's changed and
Speaker 4: what's helped to accelerate it along the curve is obviously
Speaker 4: some fine tuning of the algorithms which were previously focused
Speaker 4: more on numerical data and now they can handle words
Speaker 4: and a language. And also the huge amounts of computing
Speaker 4: and storage power that is available today versus decades ago,
Speaker 4: and so that has enabled these sort of AI applications
Speaker 4: which have been there for many decades, but perhaps below
Speaker 4: the horizon, unknown and unseen so many people. It's allowed
Speaker 4: a proliferation of those applications and opening up substantial new
Speaker 4: markets and opportunities. In arguably what's going to be not
Speaker 4: only a multi year secular trend, but a multi decade
Speaker 4: secular trend. So it's a very very exciting place to
Speaker 4: be and very very different to what things look like
Speaker 4: in the late nineteen nineties.
Speaker 1: Manleep seeing does it surprise you how quickly AI has
Speaker 1: erupted under the investment landscape.
Speaker 3: Not really if you follow the trajectory of evolution, and
Speaker 3: as Rob alluded to, you know, machine learning has been
Speaker 3: around for a number of years. The whole large anguage
Speaker 3: model training is about ingesting large amounts of data, using
Speaker 3: the compute capacity and really putting guardrails around the technology
Speaker 3: to the point that it's not only productive, but uh,
Speaker 3: you know, you're not at the risk of it being misused.
Speaker 3: I think that is one of the biggest concerns that
Speaker 3: I see, is the potential for this to be misused
Speaker 3: by bad actors, and I think AI always had that promise. Clearly,
Speaker 3: if we have made strides in terms of you know,
Speaker 3: deploying large anguage models at scale and to be used
Speaker 3: in productivity apps. But the key is how do you
Speaker 3: ensure safety and you make sure that the guardrails are
Speaker 3: there for all kinds of use cases. And to me,
Speaker 3: that still remains to be proven, Man Dave.
Speaker 2: Nvidia is probably the most important stock right now and
Speaker 2: everyone eagerly awaits when it reports its quarterly earnings. Is
Speaker 2: the bullmarket totally relying on Nvida continuing to beat estimates
Speaker 2: or is there any other metrics we should look at.
Speaker 3: When you look at you know, team stock like Nvidia
Speaker 3: is it's supposter child for generative AI and for a
Speaker 3: good reason. And I think when you look at how
Speaker 3: they kind of became the name to resemble this wave
Speaker 3: of gen AI, it's because they had these chips that
Speaker 3: they had developed over the last thirty years around gaming
Speaker 3: and parallel processing, which was a different type of architecture
Speaker 3: when you compare it to the traditional CPU architecture that
Speaker 3: is used for pretty much all kinds of processing up
Speaker 3: until now. So this concept of accelerators and parallel processing
Speaker 3: is new when you think about the data centers, and
Speaker 3: the reason why it's taken off in such a big
Speaker 3: way is because when you're dealing with large aguage models.
Speaker 3: We're talking about a scale that we haven't used for
Speaker 3: processing before. Right now, you're seeing supply constraints. I mean,
Speaker 3: think of how fab capacity increases over time. It normally
Speaker 3: increases you know, mid single digit, because that's how the
Speaker 3: KAPEX investments used to be. How we are talking about
Speaker 3: insatiable demand for video chips and that to leading note chips.
Speaker 3: We're talking about three to five nanometer fabs that are
Speaker 3: needed for producing these accelerator chips and we don't have
Speaker 3: that capacity. Combine that with you know, COOS packaging that's
Speaker 3: needed for these chips. So clearly the supply constraints are
Speaker 3: what's driving the asps. But I won't pay too much
Speaker 3: attention to asps even though that's what's driving the high
Speaker 3: growth rates, the fifty percent plus kegres that we've seen
Speaker 3: with Nvidia. It all comes down to what is the
Speaker 3: addressable market for data centers over the next ten years,
Speaker 3: and based on our one point three trillion number, half
Speaker 3: of that is hardware needed for training and inferencing. So
Speaker 3: clearly there is a long runway when it comes to hardware.
Speaker 1: Man leeps saying how sustainable is Navidio's competitive advantage.
Speaker 3: Do you think now, look at the biggest buyers of
Speaker 3: Nvidia's chips, it's your hyperscaler companies, the companies that deploy
Speaker 3: them on cloud to be used for widespread consumption. Anytime
Speaker 3: you see such big concentration where your customers can become
Speaker 3: potential competitors, which is the case with Nvidia's buyers. Here, Amazon, Google, Microsoft, Meta,
Speaker 3: all of them have said they are developing their own
Speaker 3: chips because they think vertical integration would give them an
Speaker 3: edge over time, and we've seen that with the Apple model.
Speaker 3: I mean, think of why Apple's smartphone is so successful.
Speaker 3: It's a vertical integration from chips all the way to
Speaker 3: software and operating system. So clearly there is a risk.
Speaker 3: But right now, by the time these companies develop a
Speaker 3: version of their accelerator, Nvidia would be at least four
Speaker 3: or five generations ahead in terms of the performance. So
Speaker 3: that's why you know every three to four months, these companies,
Speaker 3: especially Nvidia NAMD, they will keep progressing to the point
Speaker 3: their processor, their accelerator performance will be ahead of what
Speaker 3: the hyperscillers are developing. And I think that kind of
Speaker 3: edge will remain.
Speaker 1: For a while Rob Lee, did you want to weigh in?
Speaker 4: I agree with that. I mean, the semiconductor is at
Speaker 4: the lynchpin of the global supply constraints at the moment
Speaker 4: within the AI sector, you know, particularly coming from TISMC,
Speaker 4: given the supply bottleneck that exists within that business, because
Speaker 4: TSMC is the only semi conductor foundry globally with true
Speaker 4: deep experience in leading edge technology derived on production equipment
Speaker 4: from ASML. So it's those two key companies which are
Speaker 4: at the center of this supply bottleneck which is benefiting
Speaker 4: in video at the moment and obviously causing this constraining factor.
Speaker 4: Is clearly benefiting in video, but also to the detriment
Speaker 4: of the Chinese given the export constraints, can't access leading
Speaker 4: edge technologies, and also given the supply constraints on the
Speaker 4: semiconductor founder of fabrication side, they also can't acts leading
Speaker 4: edge semiconductor equipment in order to fabricate their own chips.
Speaker 4: So I think I just at those two points in
Speaker 4: there before.
Speaker 2: We get onto like Asia Tech. But Mande, but I
Speaker 2: wanted to just broaden the discussion to like the stocks
Speaker 2: outside in VideA, How did you grade the Magnificent seven, Like,
Speaker 2: what scorecard would you give them to be an ABAC
Speaker 2: in terms of the different companies.
Speaker 3: Well so, my lens right now in terms of evaluating
Speaker 3: Magnificent seven is around which out of the magnificent seven
Speaker 3: have their own foundational models and based on that, Google,
Speaker 3: Meta and Microsoft because of their early Open AI partnership,
Speaker 3: stand out to me as companies that have their own
Speaker 3: foundational models natively deployed on their cloud and that gives
Speaker 3: them an edge because we are talking about generator AI.
Speaker 3: Amazon is the largest cloud player. Clearly they have a
Speaker 3: lot of potential to bene fit from the trend, but
Speaker 3: what they haven't figured out so far is their foundational
Speaker 3: model and what are they going to standardize on. Same
Speaker 3: thing with Apple. I mean, clearly Apple has the install
Speaker 3: based when it comes to you know, on device AI
Speaker 3: and inferencing, but we don't know much about their GENI strategy.
Speaker 3: And Nvidia clearly is the biggest beneficiary of the trend,
Speaker 3: so they get very high probably a plus so far.
Speaker 3: So that leaves Tesla. Tesla, I think is an interesting
Speaker 3: play a derivative of their early strategy around the FSD
Speaker 3: software which wasn't built on generative AI per se, but
Speaker 3: they have changed the latest version of their software to
Speaker 3: leverage generative AI, so clearly they have a headstart when
Speaker 3: it comes to generative AI on the automotive side. But
Speaker 3: I think if you use that framework, companies with their
Speaker 3: own foundational models will have a persistent advantage than the
Speaker 3: ones that don't.
Speaker 1: Our guests are man Deep Sing and Rob Lee, both
Speaker 1: tech analysts with Bloomberg Intelligence. Mandeep and Rob Let's talk
Speaker 1: about Taiwan Semiconductor. Some in the West refer to it
Speaker 1: as the most important company, the most important manufacturer that
Speaker 1: they've never heard of. Sixty to seventy percent of the
Speaker 1: global chip market is what it commands, talk about its
Speaker 1: positioning in the broader tech AI matrix and why should
Speaker 1: investors care?
Speaker 3: I mean, look, and Rob probably has better visibility on
Speaker 3: what TSMC is doing in the region. But from a
Speaker 3: Western perspective, there is this reliance on TSMC when it
Speaker 3: comes to all the fabulous chip makers, including Nvidia that
Speaker 3: we wave about so much. At the end of the day,
Speaker 3: they rely on TSMC to make those GPUs that there's
Speaker 3: so much demand for. So clearly that is a choke
Speaker 3: point when you think about deploying generative AI. But I
Speaker 3: have no doubt, and you know, we just published two
Speaker 3: deep dives supply chain and semicap equipment here at BI
Speaker 3: and part of the conclusion was that the advanced chief
Speaker 3: manufacturing capacity will diversify over the next ten years, and
Speaker 3: that's where you will see companies like TSMC as well
Speaker 3: as other big fab makers actually invest their CAPEX dollars
Speaker 3: in terms of expanding their fabs outside the East Asia region.
Speaker 1: Robbie, you have some interesting perspectives about TSMC, including how
Speaker 1: it's affected Taiwan's exports over the past decades.
Speaker 4: Yes, well, the I wish I had a very precise
Speaker 4: state for you here, but at some point in the
Speaker 4: nineteen sixties, I believe Taiwan's number one export was actually sugarcane,
Speaker 4: so it was largely an agricultural based economy. And although
Speaker 4: that's sixty plus years ago, through significant government support, through
Speaker 4: cooperation with the universities in pushing people or encouraging people
Speaker 4: to study STEM subjects and engineering, the transformation of the
Speaker 4: Taiwanese economies phenomenal used to be referred to as one
Speaker 4: of the Asian tigers, if you remember. So, going back
Speaker 4: to what Mandeep was saying as well, and just a
Speaker 4: little bit of historical context. Again, it's twenty years or
Speaker 4: so ago. At the beginning of my investing career, I
Speaker 4: think the view within the semiconductor industry was the real
Speaker 4: value is in the design companies like Armholdings which were
Speaker 4: still around then, and companies designing their chips. That's where
Speaker 4: the value was added and was captured. The assembly. The
Speaker 4: fabrication was important, but to a lesser extent, but as
Speaker 4: a result of something referred to as Moore's law, as
Speaker 4: the complexity of cease chips has increased to the drum
Speaker 4: beat which defines to grow for it within the industry,
Speaker 4: with transistor densities doubling every eighteenth to twenty four months.
Speaker 4: Sem conductors these days are fabricated at the atomic scale,
Speaker 4: so they're incredibly technically complex and it's incredibly difficult to
Speaker 4: fabricate at such a high level of miniaturization. And TSMC
Speaker 4: really is the only company on the planet which has
Speaker 4: really kept a commanding lead in fact enhanced and built
Speaker 4: its commanding lead over the competition over that period. So
Speaker 4: we've seen a transfer of value from the design, which
Speaker 4: is still important that increasingly to the fabrication or manufacturing side,
Speaker 4: and that is why TSMC on a global scale is
Speaker 4: so important. Obviously with an interesting overlay of geopolitics and
Speaker 4: everything else, which makes the story both more interesting and
Speaker 4: more complex to analyze. But TSMC is really the key
Speaker 4: lynchpin of the global technology sector at the moment, I
Speaker 4: would say, and.
Speaker 2: Robert, if you look at the share prices of Asian
Speaker 2: tech like TSMC definitely appears to have benefited from the
Speaker 2: AI trend, but some other stocks in your coverage seems
Speaker 2: to have really missed the AI hyph. I'm talking about
Speaker 2: the big Chinese tech companies Ali and tenset. Now, prior
Speaker 2: to recording this podcast, I did look at the Bloomberg
Speaker 2: terminal and if you roll back the clock three years ago, Ali,
Speaker 2: Barber and Tencent in terms of market cap were actually
Speaker 2: comparable to the Magnificent Seven. If you look at the
Speaker 2: average market cap of the Magnificent seven now, it's almost
Speaker 2: ten times the size of Ali Barber. What went wrong
Speaker 2: with China tech, especially as it relates to AI, right, how.
Speaker 4: Long have we got?
Speaker 3: Is?
Speaker 4: You don't answer for that? Well, clearly the China tech
Speaker 4: sectors being caught up with a lot of concerns on
Speaker 4: the regulatory front, a lot of domestic regulation with Chinese
Speaker 4: government want to, you know, to level up the playing
Speaker 4: field and with a lot of I think very well
Speaker 4: fought through regulation to protect the more vulnerable in society
Speaker 4: from the overarching power of these two Internet giants. That's
Speaker 4: one issue that's impacted their earnings outlook and clearly their
Speaker 4: share price. But on the AI side, I think whilst
Speaker 4: China has some very interesting technology, on the software side,
Speaker 4: there are two big differences with the US market. The
Speaker 4: first is it's a far more competitive space than in
Speaker 4: the US. So for example, when Chat GPT was announced
Speaker 4: roughly a year ago, you hadn't heard of any Chinese
Speaker 4: large language models. At that point there were probably were
Speaker 4: a few in development behind closed doors. As of October
Speaker 4: last year, there are two hundred and thirty eight. So
Speaker 4: this is an immensely competitive field in China, and these
Speaker 4: companies have come from a relative standing start to develop
Speaker 4: a large number of models, which also tells you that
Speaker 4: the relative technical barriers to entry are quite low, certainly
Speaker 4: within China. So again that has implications for the longer
Speaker 4: term growth potential and margin potential of these companies, and
Speaker 4: again I think that's another thing that's factored in. But
Speaker 4: I think the most important thing which is going to
Speaker 4: increasingly forestall their growth curve is the access to the
Speaker 4: core infrastructure in which these large language models are both
Speaker 4: trained and then the inferencing happens, and that is largely
Speaker 4: US derived technology. So whilst the chips may be fabricated
Speaker 4: in Taiwan, they are in Nvidia design chips or AMD
Speaker 4: design chips, and due to the export restrictions, it's increasingly
Speaker 4: difficult for Chinese companies to get hold of those, which
Speaker 4: makes them evra reliant on lower generation chips which are
Speaker 4: slower or more energy intensive, you know, their inferior in
Speaker 4: many respects, or it makes them more reliant on homegrown alternatives,
Speaker 4: which again are one to two generations behind, and they're
Speaker 4: also in short supply. So if you can't get the
Speaker 4: infrastructure to run these models off, then clearly that will
Speaker 4: impact your growth curve over the next few years. So
Speaker 4: these are some of the factors that have impacted the
Speaker 4: performance of these stocks. It's more complex than that, but
Speaker 4: maybe you can do another podcast and that another day.
Speaker 2: Robert, Have you even checked out any of them The
Speaker 2: mainland China apps relating to AI of course, yeah, and
Speaker 2: how do they compared to chapt This.
Speaker 4: Is purely anecdotal, But myself and a colleague this is
Speaker 4: into last year when ernie bot, which is Baidu's Chat
Speaker 4: GPT like janitor of a tool, was first launched. We
Speaker 4: compared that and you know, just to put a few
Speaker 4: questions running through GPT three and a half and ernie bot,
Speaker 4: and I think by dou is perfectly adept at answering
Speaker 4: any questions relating to China, to Chinese history, to Chinese culture.
Speaker 4: No major issues however, when it comes to answering questions
Speaker 4: on things that lie outside China. So for example, we
Speaker 4: asked it some questions on Bill Gates and just tell
Speaker 4: us a bit about him. You know, it gave two
Speaker 4: three lines, very simplistic lines without a lot of detail,
Speaker 4: comparing contrast to Chat GPT, giving significantly more information with
Speaker 4: a lot more opinion and a lot more depth and value.
Speaker 4: Add Now, why is that? There are two reasons? Again?
Speaker 4: I think one is the access to training data. Something
Speaker 4: like ninety percent of the world's worldwide Web content is
Speaker 4: in English and roughly ten percent is in Chinese, and
Speaker 4: I think the availability of training data is key to
Speaker 4: the effectiveness of these models. So the breadth and depth
Speaker 4: of training data available to Bido's early bot is insignificant
Speaker 4: compared to chat GPT, and that is reflected in their
Speaker 4: relative performance. And obviously, the Chinese data that anybot is
Speaker 4: trained on is largely China specific, which accounts for its
Speaker 4: superior results. The second issue, just briefly is obviously on
Speaker 4: the censorship rules. So obviously, if you asking anybot on
Speaker 4: anything that's politically sensitive, you're going to get a curtailed answer.
Speaker 4: Let's just leave that answer at that.
Speaker 1: Mandy, look into your crystal ball. Two years, five years,
Speaker 1: maybe ten years down the road. Where is this taking us?
Speaker 1: Where is AI going to lead us? Is it going
Speaker 1: to be to a good place or a not so
Speaker 1: good place?
Speaker 3: I think the answer there is simple. It definitely will
Speaker 3: lead us to a much better place in terms of
Speaker 3: the efficiencies and the productivity that we think generative AI promises.
Speaker 3: And the reason I feel fairly confident on that front
Speaker 3: is we have already seen the proof points when it
Speaker 3: comes to all the contextual knowledge that's embedded in these models.
Speaker 3: I mean, you're talking about intelligence systems that can understand
Speaker 3: human language and really never get tired, and you know
Speaker 3: it can respond in a very intelligent way. So when
Speaker 3: you apply that framework to different facets of our life,
Speaker 3: the world will be a much better place. The only
Speaker 3: thing I would put as a caveat is the guardrails
Speaker 3: need to be in place, because this can potentially be misused.
Speaker 3: All these foundational models are trained on a lot of
Speaker 3: bad things as well, which are embedded in the model.
Speaker 3: That's why the guardrails are very important.
Speaker 1: Rob Lee closing thoughts, Okay.
Speaker 4: Yeah, I don't disagree with any of that. I think
Speaker 4: if you look at who are the interested parties within
Speaker 4: the world of AI, there are the makers of the infrastructure,
Speaker 4: so clearly that's the in videos, TSMC's armholdings, etc. And
Speaker 4: they're clearly profiting from it at the moment as the
Speaker 4: infrastructure is built out. There are then as sort of
Speaker 4: mandeeps referred to the end users of AI, and I
Speaker 4: think there have been plenty of examples where the utilizing
Speaker 4: AI can drive significant cost and productivity savings and we're
Speaker 4: only on the cusp of that. So the potential benefits
Speaker 4: in the long term through the use of AI, you know,
Speaker 4: potentially immense. Then there are the software companies in the
Speaker 4: middle again, and I'm referring to the Chinese companies here.
Speaker 4: On one hand, they are having to spend significant amounts
Speaker 4: on building out the infrastructure, so it's costing them tens
Speaker 4: of billions of US dollars at a time when they're
Speaker 4: monetarzation model is at a very immature stage. So I think,
Speaker 4: again tying back to John's earlier question, I think that's
Speaker 4: one reason why we're seeing on the performance in the
Speaker 4: China AI stocks at the moment, because they're caught in
Speaker 4: the middle anyway, and until they find a better way
Speaker 4: of actually driving into profit and driving some learnings from this,
Speaker 4: I don't see that we're going to get any short
Speaker 4: term change there.
Speaker 1: Our guests have been Man Deep Seeing, Global head of
Speaker 1: Technology Research based in New York and Bloomberg Intelligence, and
Speaker 1: Robert Lee, Senior Asia tech analyst based here in Hong Kong.
Speaker 1: It's been a fascinating discussion on AI, the risk, the opportunity,
Speaker 1: and the business involved implications for Asia and around the world.
Speaker 1: Man Deep and Rob has been great having you.
Speaker 3: Great Thanks for having us John and Tom, and yeah,
Speaker 3: look forward to doing this again in the future.
Speaker 4: Absolutely, I completely echo that it's been good fun. Let's
Speaker 4: do it again sometimes.
Speaker 1: I'm Tom Corbett in Hong Kong and I'm John Lee.
Speaker 2: This podcast was produced by Clara Chen and you've been
Speaker 2: listening to the Asia Centric podcast