Asia Tech Podcast recorded an interesting conversation with Wally Wang,  Founding Partner at Scale Asia Ventures. Scale Asia Ventures is a Palo Alto-based American venture capital firm that aims to support startups at a global scale. 

Some of the topics discussed by Wally: 

  • The factors that influenced Wally into the startup world
  • What got Wally interested in artificial intelligence and machine learning
  • The significance of getting enough exposure to be able to instantly identify what’s missing from a startup 
  • How a change of environment can instill traits of a founder
  • The advancements in technology that help machines evolve themselves
  • The similarities between the creations of a machine and a human
  • Wally’s sneak peak at what’s coming

Some other titles we considered for this episode: 

  1. Eating the Whole Internet and Coming Up With a More Intelligent and Better Internet
  2. Think About Change
  3. There’s So Many Things You Can Do

This episode was produced by Stephanie Ng

Read the best-effort transcript below (This technology is still not as good as they say it is…):

Michael Waitze 0:03
Hi, this is Michael way. Love that smile and welcome back to the Asia Tech Podcast. We are joined today by Wally Wang, a founding partner at Scale Asia Ventures SAV. I love the three initials. We’ll get to that in a second, too. While we thank you so much for coming on the show. How are you doing today?

Wally Wang 0:22
Thank you, Michael. It’s great pleasure I’m doing being fantastic.

Michael Waitze 0:25
It is super great to have you here, before we get into the central part of this conversation and dig deeply into what I think is going to be some pretty interesting stuff. Why don’t we get a little bit of your background for some context?

Wally Wang 0:35
Absolutely. Absolutely. So I grew up in China. I was born and raised in Beijing, and then went to chin Hua University, which is MIT and in China. Yeah. That was a lot of fun. I mean, I was really into research in machine learning and AI, doing a bunch of we can we can do talk more about that. I mean, it’s great to see that recently, the generative AI has been really hot, oh, my God, world. Such a great to see the, you know, back in 20 2009 2010, I was in the research lab to develop that, you know, computer vision and natural language processing back in the days. And I moved to this to the States to pursue my PhD at NYU, in New York, the Stern School of Business, studying information systems. So I was doing research and publishing papers. But i Besides me, it’s not my thing. So I moved it to startup world. So I’ve been in the venture business for almost a decade now. So initially, I was the operator, I was doing some angel investments. And most recently, I’ve been doing full time investing over the past four years,

Michael Waitze 1:52
I want to go back to your education. If you don’t mind. You said 2009, it feels like a lifetime ago to me, I’m sure it does to you at some level as well. But what gets a kid interested in artificial intelligence and machine learning, I want to get to sort of generative technology in a second. But what gets you interested at the beginning of that, and doing research like was your mom and dad doing that kind of stuff? Like what got you interested?

Wally Wang 2:16
I mean, being educated in from China, I mean, everybody’s very good at math and physics. So I was also getting some, you know, good studying. So I was studying computer science at Tim Hart. And naturally, a top grade students will go to the US to pursue a PhD. So I was perfectly on that track. In my annual study that I’ve been doing, you know, picking picking research labs back in, you know, my sophomore year, in two Ha, so there was quite a few directions. Within computer science that I found data mining at that time, it’s called data mining, or, you know, big data, data analytics, it has been quite an exciting for me, because I think it’s very relate to your essence life, or our daily lives, where we can use machine to automate a lot of things and also making predictions. So I found that very exciting. But there’s also some other new technologies like 5g or the you know, the networks and networking technologies, etc. But I decided to dedicate more time in the, you know, in this research lab, which right now, it’s fascinating to see my prior advisor, I Ching Hua, they were actually developing a open AI in China. So Chinese version of open AI interests are quite a big thing. I mean, with collaboration with Baidu, and with all the top universities in China, and they’re doing their own Chinese version of the whole thing I ran. And now also worked on Microsoft Research Asia, heparin in Beijing back then. And also, I was a visiting scholar at Cardinal Manor University for my junior year. I also do some research at CMU as well.

Michael Waitze 3:54
So did you live at CMU? It’s around Pittsburgh and Pennsylvania, right?

Wally Wang 3:58
Yeah, I lived there for summer. I was a research scholar.

Michael Waitze 4:02
At least it was the summertime. I mean, living in that part of the United States in the wintertime can just be brutal. I know my family is across the street in Philadelphia, but I just hate the winter. I don’t know about you, from Beijing writes, you should be kind of used to cold.

Wally Wang 4:15
Yeah, that’s right. That’s right. That’s right. And I but I live in New York for a few years. So winter is also quite cold compared to where I am right now. And

Michael Waitze 4:26
when somebody says to me, like I wasn’t interested in academia, or continuing, right, I’m just really curious, like, what drives you out of that? If you did it your whole life? You worked in a research lab? You’re good at it. You were probably brought up in an environment where that was very prestigious, right? We’re studying learning getting good at stuff was really prestigious. So what drove you out of that, and then into operating?

Wally Wang 4:50
You know, that’s a great question. I think, you know, back in the days I see a lot of my other classmates or schoolmates join now Research Lab and Microsoft or Google or joining as an assistant professor at research universities in the world, I think a lot of the works that we have done or will dedicate the next decades to do is mostly about a poster fact who to summarise or to try to generalise some knowledge of what has already happened. So that’s the most important thing for at least from the business schools perspective, a lot of the research has found really in that way. So we are publishing papers, talking about the summarising or the business phenomenons and try to generate some abstract knowledge from it. I think those are quite far from your day to day life. And especially when you’re seeing the excitement in from the mobile internet world era, back in 20, early 2010 2012, when you know, everything is booming, literally just went public. Around that time, you know, Facebook is just still growing very fast. And we see Uber and all the sharing economy just taking off. So a lot of things really happening in the cutting edge. And a frontier is really done by the startups in Silicon Valley or elsewhere in in the world. So find that that’s more rare. You’re really creating the future, versus just summarising after the fact. So that’s what I found that I wanted. And when I was still young, so I wanted to experience more of that more excitement.

Michael Waitze 6:30
Still love people’s perspective on age, right? Like how old am I? What do you think? You are? Probably 50? Yeah, 57. But I still feel young. Right? So I don’t think it’s just interesting to hear like, and

Wally Wang 6:44
how old are you turning 34.

Michael Waitze 6:46
So I love to hear people in their 30s talk about when I was young. Yeah, because I think you’ll look back on this time, actually, in your 30s, your mid to late 30s. And think like this is probably going to be some of the most productive time of your life. That’s what it feels like to you. But the reality is that as you get older, you get more perspective. And I think you just get better at everything that you do, particularly someone like you are that is kind of research based knowledge based in database, you’ll see as you get older, like this perspective widens, and you’ll just get so much better at stuff. Talk to me a little bit about the move from operating to investing. And I had heard, here’s the thing I really want you to focus on, if you don’t mind. I had heard 10 years ago when I first started investing myself that the best investors were x operators. And I didn’t understand why I really didn’t. And I took for granted the fact that that actually wasn’t true, but I was wrong. And I’m curious about your perspective on why you think X operators make the best investors.

Wally Wang 7:44
Yeah, I’m still a newbie in the in the investing world. So I cannot convert. Now yet to characterise myself as Why do

Michael Waitze 7:52
you think you’re going to be good at it? You know, because you did make the move. Yeah, definitely

Wally Wang 7:55
dive into that. So interestingly, talking back in 2014, when I first moved to Silicon Valley, to win a bike rider company, called pebble so at the time I was into the YC ecosystem, I also made some equity investments back then. And, you know, I feel that it’s very hard for me to, to understand or to teach or educate or help the founders than me masking. Yes, you are just new to the field, or you don’t have much of a operating experience. So I feel that’s really what I’m lacking. So I dedicate my initial I mean, from 2014 to 2019, I dedicate this five, five years, five, six years into our premium role, I took three different startups as two of them got acquired. And I also have the third one raised over 100 million before I launched my own cybersecurity startup in Asia in 2018. And we quickly access that in 2019. So I got some experience firsthand as founder, and as also as x executives at companies that have got acquired seeing the whole journey of really incubating the idea to some really seeing the fruit of exiting the company. So through only by going through the whole journey, you can understand and why or how a company can be successful from day one. So that’s where I start to have more perspective. When I’m seeing entrepreneurs from the very, very early on stage.

Michael Waitze 9:27
Do you think you lean on that experience? Now as an investor where you look and think this is a great idea? It’s a good team, but they haven’t figured out yet these three things or whatever it is that I learned when I was hired at a startup built a startup exit and start you know what I mean? Because there’s so much out there Yeah,

Wally Wang 9:43
exactly. Exactly. You know, that’s how that your my operating experience taught me or give me the privilege to really on the spot can tell or can can feel Yeah, where where is missing because you know, be a successful company has to be work very well run it. So there’s so many mistakes you can make. And, you know, personally, I feel I mean, every single companies that I work, I will take a lot of word learning. And also lessons from that definitely helping me even when I’m seeing the company’s killer make

Michael Waitze 10:15
an equivalency between. It’s like scientific research in the research, you’re doing a Ching Hua, and also at CMU, and actually becoming an investor, just work with me on this and tell me where I’m wrong, right? Because I’m not always right. And even with just building, but as a researcher, you’re almost encouraged to make mistakes, right? You supposed to have a thesis, then you’re supposed to go out and test that thesis. And if you’re lucky, actually, your thesis is wrong. Because it’s through the wrongness, that you learn all these little things, and you go back and adjust that thesis and then come up with a better conclusion. And isn’t that equivalent to the way you invest? Right? You start with this big thesis, and I want to get into what your thesis is, as well. But even when you’re building a company, you’re like, let’s go do x. And then you start building you’re like, that’s not working. But I’m used to things that aren’t working. Let’s go back and fix the thesis. And then we can fix the execution. Does that make sense as well?

Wally Wang 11:07
Exactly, I don’t know. I will say even even today, around 80% of my time is regarding research, try to come up with a hypothesis and try to, you know, research and figure out the points and validate that and to you to apply that model into the companies or the founders and make meaning with so it’s very similar. Yeah, what do you do scrappy,

Michael Waitze 11:30
you brought up open AI earlier? It’s something that’s super interesting to me, before we talk about investing. Can you just talk to me a little bit about just like what, you know, what I should know about open AI? And because there’s a lot of talk about it today, right? What are the things that I should know about it? And then I want to get into how it drives the investment thesis as well. Is that okay,

Wally Wang 11:55
I think just a quick summary of I think opening now you can think about that as a, you know, a big brain that you can just rely on, and everybody can have access to developers can have access to use that brain. So it’s an infrastructure play, it’s like, the era of ours in the AI world, is basically providing a huge brain. And that ring can do a lot of things. So there’s like a, they can do search, because search engines have a more advanced search engine, more intelligent search engine, can also do creative works, like generating graphs or sentences, that can do a lot of things. And the reason why it comes up with such a brain, and that is so exciting. So popular, I read the news that it’s valued at 20 $20 billion as we speak, but I mean, the other settings are way higher, and we’ll see more coming up in that field. The reason is that, basically, the deep learning, and also the generative models have really the machine enough intelligent, so it really trains itself. And, okay, I just burned a lot of money feeding the machine with a lot of the informations as much as in the whole internet is basically eating the whole internet and coming up with a better or more intelligent, better version of the internet that enables a lot of more advanced capabilities. So that’s what I see.

Michael Waitze 13:18
And are we like at the earliest stages of this? I mean, obviously, people have been working on open AI for a while but implementing open AI to come up with end user products. I feel like we’re kind of at the earliest stages of this. And is this part of your investment thesis as well thinking, Okay, people have been working on this for a while. But now it’s actually turning into things that are useful. And you get to benefit from that as an investor. And I got to start investing in that stuff. So is that what you’re doing here?

Wally Wang 13:40
Yeah, that’s part of my, my thesis, I will say, I mean, the application of AI into changing or redefining different industries or different vertical of application. So for example, what we see right now that I actually invested in a company called jasper.ai, they’re building on top of open AI. So they’re calling the open AI sprain. And then they build that is like a marketing content creation tool, to enable every single marketers to just write whatever they want. And they are doing a fantastic job in commercialization of that AI technology into bringing that to the marketers around the world, either small, medium sized companies or even larger enterprises are using that solution. So that’s one application that I’m investing into. There’s so many other applications that are applying the AI to really change that industry. And

Michael Waitze 14:38
I want to back up and talk to me about this scale age of ventures. What is the overall thesis here in the context of using some of this generative technology and AI but I’m interested in this in one of the things that you’ve mentioned to me offline, and that is this idea of living outside of your home country is kind of like the rest of your life because I’ve done this for 30 years, right? Like I left the United States when I was 20 something years old, and I’ve lived in Asia. So you and I I basically crossed in the sky at some point, right? And it’s hard sometimes to live in a foreign country for the rest of your life. And I’m curious, like how that colours the way you invest, the type of people in whom you invest, is what that’s all about as well.

Wally Wang 15:13
Yeah. And I briefly mentioned about my investment thesis. And as you see that a lot of that relates to, you know, we need to have a very high technology barrier or technology moat, what, you’re not building the company. So I’m investing primarily into those like AI or software driven b2b companies, and also the infrastructures behind it. And we can see over the really the past two decades, a lot of the software companies, the founders are expats that are easily immigrants or European immigrants, a lot of great great AI researchers are from from Asia or from other parts of the world. And they are immigrants. To the US, the main market is still in the US. But they have a lot of the prior similar journey as we are first generation of immigrants. We want to be really good at what we are and also collaborate with others with local talents. Yeah. So think about globally global market from day one in that’s my mandate for for the fund is to invest in those app experts and those aspects strategizing a lot of great returns, I read the a few numbers, and more than half of the public listed software companies are funded, or has at least one founder from expats. Excellent founders. So

Michael Waitze 16:32
here’s my theory on this is that I think if you grow up in country A Yeah, and move to Country B, by definition, you have to start questioning all of the things that you believe that were true for the first part of your life, if you have an open mind at all right? Because like you said, you grew up in Beijing, it’s a very specific kind of lifestyle. Not only that, but you’re surrounded by a bunch of people that are very similar to you, you have conversations that are a little bit, not just you, but like all of us right in this little bit of a bubble, but then you move and you experience the world in a different way. And for a guy whose mind is open, because you couldn’t be good at research, unless your mind was open. Yeah. Yeah, you live in a different place. And it’s you and all the people in whom you’re investing, start thinking, okay, not only my questioning me, but I’m questioning my view on the world. And the way that the tools that I understand get used, what can I build, that’s now going to help me do this better. And I think expats have a unique view on this because they’re automatically questioning themselves and their original environment in a new place. Does that make sense as well?

Wally Wang 17:31
Absolutely. Absolutely. So entrepreneurship is such a rare character. Yeah, human life. So people usually come to as you mentioned, I mean, x has usually struggles, at some point and during their life have really settled down. And it’s very hard for them to be settled. So they’re always thinking about breaking through their current, environmental, things like that, and making new, you know, new friends or new networks, and also thinking about change. So I think that those type of characters really, is what is needed. As a founder plus host pandemic, I see your unique advantage of those aspects, because they have they can average their own countries, in r&d. They’re better engineers are cheaper engineers overseas, and they can collaborate more openly. Yeah. Different parts of the world, different types.

Michael Waitze 18:22
Can you talk to me a little bit about GPT? Three, this idea of generative pre trained transformer three, I think is so interesting, and I don’t think a lot of people really understand it. And for someone like I am, who builds content, who does writing who does his creative, I want to understand, first of all, what GPT three is, maybe you can go through that. And then also tell me how it’s going to change the way we create stuff in your mind and also in the minds of the entrepreneurs in which you’re investing and even just the ones you’re talking to.

Wally Wang 18:51
So yeah, I don’t want to talk too much in the technical details. But I think you can think about that the prior days of AI or of a software is that we teach we write codes and teach what the machine can do. So it’s more of a one to one direction, we tell what the machine will help us to, to do it. And then they can do it fast and they can repeat it all the time. That’s the book version. It’s basically the DVD three or the basically the behind it is a deep neural network. And also the derivative anniversary network, which has really revolutionary in the past decade in the AI field is that instead of we teach the machine machine can actually teach itself so that will create a whole new level of inheritance because we don’t need to to really tell our instructor every single steps for the machines, let the machine hand evolve itself. That’s how the basically the whole technologists behind it. And because of the world has accumulated so many, so much information Over the past, you know, two, three decades of the digital, digital media and internet world. So if we can feed those informations and just give it to the machine and machine, just train yourself and learning by itself eventually, and it’s so fast, it’s coming up with you know, TB three, or TB for their opening and developing entities 435456, whatever. It’s just keeping, keep learning and keeping the more and more intelligent at some point, it will be more intelligent than that humour. Because we are not really it’s not boundary bound. It’s not a boundary. So human human tears will not be a boundary. Because the machine hanger and by itself, that’s why we see, I mean, the generated content becomes more and more, better and better. And yeah, that’s where basically, how I explained this, so work

Michael Waitze 20:52
with me on this for a second, I’ve had this theory for most of my life, that music is just math and science that people can dance to. And I’ll tell you why. Right? Because it’s super logical, you know, even if you’re not a great musician, you can hear when a notes out of balance, right? You can hear someone playing a guitar playing the piano in your ears like, Oh, that’s terrible. That note should not have been there. Right? So it’s naturally logical. And I’ve always contended that great scientists are probably great guitar players, right? Because they know how it works. And they can learn how it works as well, right? What’s the implication for we can go GPT? Three, and like you said, it’s going to go 456 I don’t really care. But this idea of general pre trained transformer technology in your mind, like, what’s the implication on art and music? As well, if you think about that, right? In other words, if I can’t tell if something is written by a human, or if music, which I always feel like, it’s kind of like math, right, in the sense that it’s not created, it’s discovered, like a great musician, takes the notes that exists and says, Oh, I just found this melody that I love. It’s a simplification, right? But what’s the implication in your mind on like, the creation of music and art through this GPT mechanism using open AI?

Wally Wang 22:01
I haven’t thought about this that deep. I mean, in terms of how this will change the the arts or the, I mean, in general, the humans perceptions of ours, or whatsoever, I feel, at least from what we see in the application side of GBD. Three is that the machine is really good at, you know, generating the patterns, learning the pattern and also generating new patterns. And I think that other arts music goes are still there’s many patterns. Yeah, many logics, as I mentioned, mentioned behind it. So the machine can catch up it very quickly, and just generating something similar, or even new patterns based off. So I think it’s very similar to humans creation of this type of art.

Michael Waitze 22:49
I mean, if you think about the recent documentary recently, last year that came out on the Beatles, right, they show these guys with their team, basically, like sitting in a room and just like kind of banging on the piano playing the guitar, just thinking does any of this work. And if the GPT three can do this at scale, like you said, faster, but also teach itself how to do it, it just feels to me like we’re coming up to this era where like, great music and great art can actually get created by the machine. Because it can iterate a lot faster. Like if you think about how long it takes a great musician, The Rolling Stones sitting in a basement somewhere in France, and just like banging stuff out and thinking, Okay, I’m gonna write Sympathy for the Devil machine should be able to do this at some point. Yeah,

Wally Wang 23:26
of course, of course. Yeah, that’s definitely the way to think about it. I think that’s why people are exciting about it. And also, I think, great artists, musician, I mean, they have the talent. And basically, the talent is that they come up with some new variation, new variations, and actually the machine and you can actually find the machine to do those variations that’s very similar to what the human is thinking and producing. So that’s why I think that’s definitely something that you can really

Michael Waitze 23:53
expect. So you’re sitting there in Silicon Valley, right? And you’ve made this decision, you’re going to invest in these sort of enterprise software and artificial intelligence and all this GPS T related stuff. Do you come up with your own ideas as well for the types of things in which you’d like to invest in and try to go out and find like the five entrepreneurs that are building it, and then pick the best one, I’m obviously picking random numbers, but you know what I mean, as opposed to waiting for people to come to you,

Wally Wang 24:16
of course, we see firm claims, or I’m also part of those Silicon Valley VC firms that have a big research driven or research driven methodology. Right. So we do a lot of the top down research ourselves, and we try to figure out what are the domains I want to spend time and trying to meet with for companies in that domain? As soon as early as possible, and it will compare with companies in that sector. Yeah. So part of the top down

Michael Waitze 24:44
and are you investing early middle late in the development of these companies? Where’s your sweet spot? What do you think is the best place to invest? Yeah,

Wally Wang 24:51
so I do a smaller chat in the earlier stage, that seed stage series receded to a pre A stage I also invest in series a stage where, you know, the company started have some revenue validations from customers and also maybe some repeatability in acquiring customers and getting they’re expanding their contacts within the customers, etc. So that’s the sweet spot and investing around series A Series B run going, the answer is B. Ron, I also have fun vehicle that we can invest a grove together state as well. Interesting. Yeah. So it’s trying to be through the whole spectrum.

Michael Waitze 25:29
Are you surprised sometimes by some of the stuff that you’re seeing, you know what I mean, we’re like, even you can imagine, like, you could do this with open AI, you can do this and that, and then somebody comes to you with something you’re like, oh, my gosh, of course,

Wally Wang 25:40
I that’s the beauty of you know, why we are trying to also talking to as many new fields as while trying to learn it ourselves. That’s why there’s always new, you know, new sectors or new carriers, especially because I’m investing in new industry verticals. For example, I have one portfolio company during the software or business aviation industry, we sell to companies and operator and operating companies, as well as as local airlines and government airlines. Those industries have not totally completely not from their work. I don’t know what’s, what is needed are all the customers there. But they have so many no house, I learned a lot from entrepreneurs.

Michael Waitze 26:21
But I mean, this is kind of cool, right? So if you could attach and tell me again, where I’m wrong, but you could attach sensors to like a bunch of different planes, right, and just fly them and gather all this data, and then feed that into like an open AI engine, and try to determine even just the small nuanced ways to increase the efficiency of how a plane flies or even just the route that the plane flies or how to react in real time. Like even that alone is super cool. No,

Wally Wang 26:42
yeah, there’s so many things, you can do that definitely. So I would say, I mean, first of all, this, the portfolios are not doing that advanced yet. But I’m just thinking. Go ahead. I think yeah, it’s definitely something that they can think about, I think, yeah, I agree with you. I think that’s why it’s so fascinating. I think the open AI is actually be you got the water in your daily life in the future. So everybody will consume it, every company will use it, or try to leverage it to embed that into their solutions.

Michael Waitze 27:14
Do you think this changes the way that software gets written as well, right, I hear a lot about you know, you read a lot about no code, low code and all this stuff. But if the machine can teach itself to learn, it should also be able to teach itself to code you just feed in like Ruby on Rails, your feed in C Sharp you feed in whatever it is.

Wally Wang 27:32
Exactly, I think that will actually come very soon. I think maybe in the next five to 10 years, we will see the best way that the AI models to replace quite a bad that especially with our engineers than we do. So you just described it. And then people will no longer learn need to learn about that. Because the machine can just read produce that code those codes automatically. Yeah, for sure. But there’s also, you know, in software engineers, I mean, there’s so much around how do you architect and and how do you define the product. So there will be still positions for those engineers to because they’re doing those high level instructions and then guiding the machine to actually writing the codes and in fact that the machines can actually read it. But three more beautiful codes, that and it’s working 24/7 don’t need to rest.

Michael Waitze 28:23
So one of the things that Sam Altman spends time talking about is right, just some of the fear is maybe the wrong word. But some of the potential downsides of open AI, do you worry about this at all as well,

Wally Wang 28:34
I would say that the, you know, the mission or the responsibility for opening that company to take care of, I mean, they can do the control, you can control what the bottle can produce, and what’s the limitations the machine can get? I mean, what’s the boundary? I mean, there’s a definitely a lot of research and a lot of work has done around that. Danya. That’s where that come in play. That’s where the scholars and researchers, they do all of that and tell the industry, what policies or what things shall follow in that stuff. And there’ll be as some chaos chaos during the process. But afterwards, I think definitely one of the revelations will come in to just define the boundaries. I wouldn’t worry too much on that.

Michael Waitze 29:14
No, I wouldn’t either. And to be fair, I’m kind of bullish on humans, that, you know, in the, in the long arc of human history, people are always complaining and fearing about like how new technology is going to ruin the human race and at some level, but they do right. But at some level, I feel like humans are always figuring out like, yeah, we can walk into the abyss. Or we can just live at the beach if we prefer it. I think most people prefer to live in the beach as opposed to walking into the abyss. Here’s the one last thing that I want to leave you with. Like, I can’t stop thinking about the potential of open AI now. And this idea of these generative technologies, like the more I talk to you the more it makes me think I mean, now it was just the aeroplane idea just popped into my head and we were talking about this and you’re like, slow down, dude, we’re not doing that yet.

Wally Wang 29:59
I think that is still in very early stage. Currently, we’re still seeing just getting started explosive of entrepreneurs getting into this field. And I think, on the other hand, I feel there will be for sure will be some big companies in that those domains will just pick up opening and embed that into their existing solution. For example, for example, Chatbot. Or even translating software’s or whatever that marketer tools, whatever, they will just embed or have them will just embed this LTI to their existing offerings. I feel there’s a time window for new companies, new startups to come in. But there will be also some quite a bit of like touch ups, at least from like a big companies like Microsoft, or Amazon or Google, they will really benefit a lot from this revolutionary technology.

Michael Waitze 30:57
I think so too. But I think you said this earlier, and I’ll let you go. When I’m done with this. The open AI has the ability to be the AWS of like so many things, right? Because in the old days, if I wanted to start my own company, I had to build my own servers, right, my own all this kind of stuff that I had to do. And today I just plug into it, right? I just Yeah, pyre in s3, and I’m just off to the races, right. And I think open AI is going to be similar, someone’s gonna make a lot of money doing it, but also a whole bunch of new products are going to be enabled and a whole bunch of new services is going to be enabled. Because we get to use the collective brain of the entire universe. And that I think, is going to be really powerful. No,

Wally Wang 31:31
exactly. Exactly. And it’s almost a free. I mean, the pricing is amazing. Is it Yeah, exactly. If you bring that into reviewing the navigation, so is this. That’s why I think I make the analogy to either as Yes, really, really cheap, everybody can just log in Sharra credo Cano pIane and just building the building the applications and just as spending, for example, were generating 1000 Word will just cost you 20 cents a piece of that block. A readable block will only cost you 10 cents. 20 cents to John. That’s, that’s just so cheap. And so yeah, that’s quite a few an exciting time.

Michael Waitze 32:14
It is. So I’m gonna leave you with that. Wally Wang, a founding partner at Scale Asia Ventures, I cannot wait to have more conversations with you and with some of your portfolio companies. I really appreciate your time today. Thank You.

Wally Wang 32:25
Thank you, Michael.

 

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