EP 310 – Rebecca Schwabe – Founder at Saigon A.I. – Pioneering Data Science and AI Innovation in Vietnam

by | Jan 17, 2024

In an enlightening conversation on the Asia Tech Podcast, ⁠Rebecca Schwabe⁠, COO and Founder of ⁠Saigon A.I.⁠, shared invaluable insights into her journey through the tech industry, the evolving landscape of technology in Vietnam, and the unique challenges and opportunities of being an entrepreneur in this rapidly transforming space.

Some of the topics that Rebecca covered:

  • Her career pivot from finance to tech
  • Why she identified Vietnam as an emerging tech hub
  • The impressive growth of Vietnam’s tech ecosystem
  • Breaking stereotypes and encouraging women to participate in the tech revolution
  • Artificial intelligence and the future of tech

Some other titles we considered for this episode, but ultimately rejected:

 
  1. Truth and Explainability
  2. It Was a Lot of Fun Until You Got a Page at 2 AM
  3. The Next Tech Frontier: Vietnam’s Emerging AI Landscape
  4. Empowering Women in Tech in Vietnam
  5. The AI Revolution in Southeast Asia
Read the best-effort transcript

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

Michael Waitze 0:05
Hi, this is Michael Waitze. And welcome back to the Asia Tech Podcast. Rebecca Schwabe, a founder and COO of Saigon AI is with us today. Rebecca, thank you so much for coming to the show. Let’s give the listeners a little bit of your background for some context.

Rebecca Schwabe 0:22
My background actually starts in finance. I worked in finance for about 10 years. And then I pivoted to, to the tech field, I started, actually, I started while I was doing finance, I helped with all lilyc servers, I helped with the just maintenance and setting up and we did a lot of website hosting in the first company I was in. And so I was able to set up DNS and all that other stuff. And yeah, it was a lot of fun until you got to you know, page 2am that the server was down. That was no fun. But anyway, we pivoted then to, to product, which was a lot more fun. And I’ve done some design, I’ve done a lot of coding. I actually took courses in blockchain because we had a product on blockchain. Yeah, so that was fun. And then I pivoted to data science. And I’ve been doing this for many years.

Michael Waitze 1:25
When I was at Morgan Stanley, I started off in the back office in the controllers department. But I knew that tech was going to change the way these big financial services companies existed and made money. So I joined a tech team that directly supported the fixed income business. And I was actually assist a Unix systems administrator for a floor of like 100 and something Sun spark stations, which anybody who’s listening to this now that’s like under 35 has no idea what that even means. Learning, but learning from a sysadmin perspective, I think it’s a really good way to understand like the real impact of tech. And you reminded me because you said you got paged at two o’clock in the morning. I mean, every single day, anyway. Yeah. That’s great to hear. Look, let’s talk about and where do you live? Now, by the way, I’m

Rebecca Schwabe 2:07
in Vietnam now.

Michael Waitze 2:08
And how long have you been living in Vietnam?

Rebecca Schwabe 2:11
I’ve been here for seven years.

Michael Waitze 2:13
So what prompted the move to Vietnam?

Rebecca Schwabe 2:15
Well, we were looking for my partner and I were looking for a new place to go where the tech boom has not happened yet. Got it. So we were researching Southeast Asia. And a couple countries came up but Vietnam came up highest with post-human and the nine being the top cities. And we came to coachmen city, and we we’ve met with business people, we met with universities. And we just talked with a whole lot of people for a month and realize that this was where the next tech boom would be. And we came here and originally, we set up to support a US company that we started. And then it grew into its own thing. And so now we have our own company here. So

Michael Waitze 3:14
after living in Vietnam for seven years, right and making this decision after doing research around like where the best place would be because the sort of tech boom hadn’t happened yet. If you look back to where you thought you were back then and where you are today. Do you feel like you made the right decision?

Rebecca Schwabe 3:29
Absolutely. Absolutely. The the country is amazing. And it’s growing up. And it wants to play in the international market. So

Michael Waitze 3:41
again, I want to share something with you. When I was in college in 1983 to 1987, which feels like 700 years ago, one of my professors, maybe the favorite professor on campus was actually a Vietnam War veteran. And if you think about 1983, it was only really eight years after the war ended, which as a kid you didn’t really understand. My first time in Vietnam was in 1991. And Americans weren’t even allowed there. So now that I see what’s happening there, particularly the tech boom, I’m amazed but also really proud about what they’ve been able to accomplish. And I think it surprises a lot of people who are of a certain age, who just like Vietnam, really, but I think you’re what you say is actually true. And I love it. Let’s talk about Saigon AI, what it is, what it’s meant to be, and what the long term goals are.

Rebecca Schwabe 4:31
Yes, so steigen AI, we focus on data science and AI design, through implementation. We do a lot of predictive models, of course, in everything that we’re doing, but we’re also implementing new technologies new AI into business process where it makes sense. I always talk about Everybody goes, oh, I want to chat GPT put that in my business. And it’s like, okay, but how are you going to use it? Right? And they go, I don’t know. It’s like, okay, well, that’s that’s what we have to talk through. But we do we do everything from auditing of other companies like their data science teams, we audit their models to make sure that everything is from EDA, to data drift to the result, that everything lines up with the truth of what the data is saying. Our big push is truth. And explainability. If you can’t explain it, then it’s not good. Exactly.

Michael Waitze 5:45
So you brought up GBT, I wasn’t even gonna bring it up. But I’m always curious, right? We’re a little bit more than a year out from when it was kind of unleashed on the world. How do you what do you think the difference is that it’s made besides people just asking you like, how do I use this in my business or just implemented for me? What did it do for the interest? Because Saigon is not a new company? It’s been around for what? Seven years? Right? So yeah, I didn’t do that either. I didn’t do thumbs down. That’s good. But it’s been around for a long time. Have you seen a massive spike in interest? And if you have, like, how could you characterize that?

Rebecca Schwabe 6:22
Well, what we’ve seen is that it’s gotten so much publicity, that everybody is just going, I need to have it with no background of what it is or how it works. And we have to talk through and then you have the education sector, which is scared of it. And so we’re partnered with a couple universities. And so we do like demystifying the and you know, what do we call it? cancelling out the noise, right? And we talk through and say, Okay, this is what it is, this is how it works. And these are the use cases that we see it being good for. But in the end, it’s good input. If you put good input, you get good outcome. But if you put in bad input, you get bad outcome. And it’s making things up. And that’s the whole key is it’s not truth. It’s just making things up. So we do see it working really well in marketing, though, because it can help wordsmith really well. We don’t put any code into it, because obviously, then it has it and learns on it. So yeah, I know a lot of programmers like to, like to use it to help enhance their code. And, and we don’t let that happen here at all. But

Michael Waitze 7:44
here’s what you think about these recent lawsuits, I think from at least the New York Times, suing open AI saying we don’t want you training your models on our articles. I wasn’t I wasn’t even think about asking you about it. But you were talking about demystifying this stuff for for universities, and also not putting code in because you don’t want to give that code away, and let the AI train on it. What do you think is gonna happen here? In that respect,

Rebecca Schwabe 8:12
I think that it’s going to have to be a lot more transparent on what data they are using, and how they’re using it. Because there is caught there are copyright infringement issues. And, you know, if you put in and you’re looking, let’s just say you’re looking for white papers, it the whole thing is it makes everything up. So it even makes up the numbers and everything. But if you go to that it’s fake. So the problem that we’re having is, we don’t know what it’s doing and how it’s getting to where it ends up for you. Right. And that’s gonna have to change, they’re gonna have to be more transparent.

Michael Waitze 9:00
So one of the things that differentiates, you know, a great team is like, how you assemble that team? And how you get the right team for your product projects, excuse me in AI and data science. How do you focus on that?

Rebecca Schwabe 9:14
Or a lot of our team, we have a very rigid for here, a very rigid interview process. And where we’re looking is at, we like to get younger, and you’re gonna have to get younger here. If you want data science. We look at the younger, right out of uni or still in last year of uni and our interview process is one where we asked a lot of questions about really, vetting character. It’s to us it’s more about the character interested if they Yeah, if they have the skill set or have the raw talent because you’re not going to get a four or five year experienced data scientist here, it’s too new. Yeah. So we have to, you know, go from ground up a lot of times, but we also look for people who are on the business side. Because those in International Business and Economics with high statistics, skills, we can teach the tech if they have the raw skill. But yeah, our interview process is focusing more on character, because what we value the most, is teachable. Really, that’s if they’re teachable? You know, we can we can use that teachable with raw skill.

Michael Waitze 10:45
Can you comment on the sort of technical education in Vietnam in places where people may not understand like, actually how great it is? And what the programming and programmer landscape is just the technical skill abilities in the country are?

Rebecca Schwabe 10:59
Yeah, well, I can tell you from seven years ago, the highest or most advanced programming that they were doing was php. And in the last seven years, I mean, they have really pushed the tech for, for Python for making sure that their students understand the models and how they work. And going into even robotics is there’s huge degree programs for robotics. Now. RMIT is here. So that’s the International School. But even the local schools, they’re all teaching higher advanced. So we have a school that we’re partnered with, and they even have a smart city department for teaching smart city. So they’ve really come a long way. Yeah,

Michael Waitze 11:52
it’s amazing to me, I think they’ve leapfrogged a couple of other countries in this region, in a way that very quickly rising, like you said, like five to seven years ago, because if you look at where some of the other countries are, even the larger ones, right? They were nowhere near where they are today. And they’ve just completely leapfrog them yet. How do you as a team, stay ahead, like, where do you find the time to keep up to date on stuff?

Rebecca Schwabe 12:17
That’s the challenge. That is the challenge. And a lot of what what I do is more research, and, you know, finding out what skill sets that we’re lacking, so that we can train. And I have to say we try it, we have the list of what we’re going to train on. And I think we did, too, last year. It’s tough, it’s really tough. When you’re, you’re, you’re running the business, and then you’re trying to say it, we do have a AI researcher. on staff, we have a mathematician on staff. And we have a couple of people on a team that are just now starting where they’re going to be on the research side. So it’s not falling all on my partner and I can

Michael Waitze 13:05
we talk about a little bit about getting women and females into technology. And the reason why I ask is because there are legacy reasons. And we can argue about this a lot in large parts of the Western world, too, for why there aren’t a lot of women in technology. None of those reasons are good, but I almost feel like Vietnam and Southeast Asia a little bit of Greenfield’s, right, like what I see in Thailand is there’s nothing stopping a woman from doing what would be traditionally considered in the West a man’s job. Like, in some ways, like Thai women blow Thai men away. I shouldn’t say that out loud. But it’s actually really true. And I’m just curious about what you’re doing to encourage women because it’s so early, they’re still, right, there’s no stigma or any of that kind of stuff. How do you get them involved in what’s going to be the future of their country in tech?

Rebecca Schwabe 13:51
A lot of what we do is encouraging them if they’re, you know, they have higher math skills and, and have the have the patience. You know, technology. That’s a patience game, you know that because it’s, it’s constantly changing. So it’s like, oh, I just learned this, oh, it’s obsolete. Let’s go to the next thing. But anyway, we we look at that, and we encourage them when we first came here, and I met some people in the park, because that’s where the students go to, to practice their English. And we met a young lady and she was in tech. She was freshman. And she had professors telling her she didn’t belong. Really. Yes, yeah, that happens a lot. And so we have we encouraged her. And when she got to year three, and four, She interned with us, and the company culture that we’ve created, even, you know, we’re 5050, male, female, and the culture that we’ve created here is to support We’re each other, and to encourage each other, and it’s a safe place. So what we do is we encourage them, we say, Look, we can even if you’re not going to work for us, come in, turn, train with us, and see what it could be like. And what I tell our ladies here is, I’ve already fought the battle for you. I’m sorry, I fought hard to be where I am in technology, because this, we had issues, what 9697 When I was starting in tech, and I would have customers say, Now I want to talk to a man, right? And it’s like, seriously, like, we’re gonna give you the same answer I just gave you which they did. And, you know, but I said, I tell them, I thought that battle already. So come and see how you can stand firm and be confident in your skills. And we also have to do a lot of training with that, where we take them to do speaking engagements at universities, the ladies that are with us. And I purposely pick the ladies to come with me because I think it’s important.

Michael Waitze 16:14
I was doing a recording for another one of my shows last month or the month before, it’s probably in November, one of the guys that was from Japan was talking about diversity and equality, right. And he made a point that in Japan, it was actually really hard because women would reach a certain position. And he said, Look, we want you to get promoted, we want to give you more to do and stuff like that. And they would say, I’m getting a lot of pressure from my family. Both my both my parents, but also my immediate family not to do that I’m satisfied where I am. Do you see anything similar like that in Vietnam? Or again, are you early enough where you can disintermediate that? Yeah,

Rebecca Schwabe 16:49
we’re not seeing a lot of that. But what we are seeing a lot of pressure is they have pressure, they young ladies have pressure to be married by 30. Really, they do, because there is a stigma here that after 30, you’re just not marriage material, I got it, which is so wrong, in many ways, but they do have that pressure. Where it’s changing, though, and where this new generation is, is that they know that they need two incomes. It’s not like it’s like every other country, you need two incomes to survive, even though cost of living is low here. It’s not that low. And so based on you know, with the economic scale of salary, and everything, it’s really the same as everywhere else. So they do find that they do need to work and they are encouraged to work. However, once they have children, that changes a bit. And because there isn’t like a lot of daycare, or help or things like that, and they do get pressured to stay home, one of the things that we’ve been working on is how can we retain that talent, because like I said, we have a lot of young people here, but they’re gonna start getting married and having families and, and we’re actually putting into place some things where we can encourage them to maybe stay on remote part time, keep their skills up, keep working, so that when their kids are old enough to go to school, and they want to come back, that they’re not behind, and that they still feel like they can’t

Michael Waitze 18:33
write it to a certain extent, like you said, the technology is moving so fast, right? So even if they come in four years later, or five years later, kindergarten, whatever it is right when their kids are in school, if they have a base that they’ve been working on or maintaining, they should be able to quickly catch up and kind of jump right back in particularly if they’re remote, right? Because all it really is, is having a keyboard a really high speed internet connection, which in Vietnam is very quite prevalent. It is and then just the desire to do it. Yeah,

Rebecca Schwabe 18:59
that’s right. That’s right. And that’s the key, though. And if they don’t have the desire, I mean, that’s okay, I understand that be fine. I’m a mama too. I just continued working. You know, but that was my choice. And, and they have a choice as well. But we try to encourage that we also encourage further education. So we have a program where we even help with tuition, if they’ve been with us for three years. And we help them with tuition if they want to do like a master’s. And they can continue working. And we’ll support them to do you know, as slow as they need or as fast as they want. And we actually have one young lady doing that right now. And she’s still working, but she’s also taking classes.

Michael Waitze 19:45
What kind of, you know, I was on a recording a couple of days ago with a guy from India who was setting up and running a pretty big company there. And I said to him, what kind of joy do you get? Because we he was talking about mentoring startup founders right that are much younger than he is he He’s in his 50s. And I was like, no, he’s late 40s Excuse me, he’s gonna get mad when he hears me say that. And I said, What kind of joy do you get? And he said, Joy is a good word. But I would change the word to fulfillment. So how fulfilled do you feel when not just for the cows, but for the guys as well, where you can take somebody and change their lives inside the context of Saigon AI?

Rebecca Schwabe 20:23
Yeah, it’s, it’s undescribable. Because it’s what gets you up in the morning. You know, it’s what keeps you going, because we actually have a young gentleman just went off to a master’s degree in Canada. And, you know, it was we, he interned here, and then he worked here. And, you know, it’s just so exciting to see them grow. And when he first started, he, he couldn’t understand me at all, he could not understand my English at all. And I could not understand his English. And you know, and then he left, and we were understanding each other and working on projects together. And it’s just an amazing feeling. And even we have staff that like they intern here, and they work for a few years, and then went on to do something else. They went on to PT or to Zillow, or to one of these bigger companies, and oh, my goodness, it’s so cool to see them thrive.

Michael Waitze 21:23
I mean, look, we used to when I joined Morgan Stanley in 1987 weeks to call it Morgan Stanley University. Right? So this idea of like, You’re not mad when great people leave you like we train them, they killed it, and they wanted to something bigger. You just sat there and thought that was awesome. We didn’t think like that person is mean to us for we train them, and then they left you just like, Yeah, we did it. Because it opens up positions for other people to come in, and then learn more and impact more lives.

Rebecca Schwabe 21:46
Exactly.

Michael Waitze 21:47
Is there one thing that sticks out to you like in the past year, we’re right at the beginning of 2024? So 2023 is in the books? Is there one thing or a couple of things that stick out for you from last year? You just thought? Oh, that’s how I can use this as well? Do you know what I mean? Like some epiphany moment where you’re like, This is gonna get even cooler.

Rebecca Schwabe 22:05
Ah, yes, I a couple go for it. Sure. Well, what we’ve learned a lot of and we’ve done a lot of behavioral product projects, so behavioral data projects. And so now we’re looking at Oh, okay, how we can now implement that into a customer that we have their product in order to help with scheduling for healthcare, which is a big problem right now. And so we can, we can go okay, so if we can now analyze how the scheduler does their job, we can now give better predictions of, okay, they go in, they want to schedule, let’s just give them, hey, this is what you did before a couple things. Here’s some suggestions based on, you know, distance of worker and things like that. But we get to implement and mash some of those projects, which is really nice. When

Michael Waitze 23:06
I was in the stock market, which I have not been in a long time, right? I used to go run around Tokyo with one of my buddies with whom I worked. You know, we used to see the names of the companies that we were trading up on billboards, and we would challenge each other to come up with the stock codes off the top of our heads because they were numbers, right. And every sector had its own numbers, like 676758, I believe was Sony, the 7203 was Toyota. So 7206 was pond or something like that, right? We could never get the business out of our head because we were just surrounded by it. And I wonder like in this space, like data analysis, data science, sorry. And artificial intelligence is your brain is constantly processing the data. How do you get away? Or can you get away from this idea of I think we can use this Do you know what I mean? Even when you’re not at work?

Rebecca Schwabe 23:54
Yeah, I don’t know that concept at all. Because the problem No, right there. That’s the problem is the problem is that you’re always looking, you’re always analyzing and I mean, just, I tell people this like my drive to and from the office, hurts my head. Here’s why it hurts my head. Because we’re on motorbike. You’ve been to Vietnam, you know how this goes. So I’m driving motorbike and you’re constantly doing calculations and predictions in your head so that you don’t wreck right. This happens all the time. And I say just, you know, what would be great is, you know, if I had an app that with the camera that

Michael Waitze 24:44
don’t go there.

Rebecca Schwabe 24:46
Don’t go there. Exactly. But that’s just a funny example. But now it’s always in your head and you’re always thinking forward because especially as business owners, that’s your job is to think what’s the next Then what are we going to do? What are we going to do next? How are we going to fit into the this trend, this new trend? Because one of the other new trends is synthetic data. And so we’re, you know, we’ve been talking about that where you don’t have enough data. If you don’t have enough raw data to do a predictive model, there’s ways that you can compensate for that. But wouldn’t it be better if you actually could create synthetic data? And there’s companies out there doing that? And for us, I mean, that’s important. Where, what is synthetic data, though? So it’s, it’s fake data that looks real. It’s not duplicated data. So when you’re doing a model, and you don’t have enough data, a lot of times you use duplication, duplication to get enough, right? And it’s not great. But what the synthetic data is, and people were starting to do this is that it’s actual, real data based on a combination of what you already have. Interesting. Yeah, that’s kind of interesting way to do things. And it does help with models, and it makes them more reliable that way.

Michael Waitze 26:18
So what other trends do you see coming?

Rebecca Schwabe 26:21
We see an upward trend for explainability, which is a lot of what we do here where people are going to want to know, okay, what was your entire process, so that we know how you got to where you got to so that we can validate each step. So we do see people are getting smarter, which with everything, when there’s a boom of anything, then people get smarter and know how to ask better questions. And then we have to, you know, we have to be able to answer those. But I think there’s also going to be some more regulations coming out, run everything ay ay, ay, do.

Michael Waitze 27:04
So if you can, if you could make like two regulations or one regulation, what would they pay?

Rebecca Schwabe 27:10
I think one of the regulations is the transparency, we you know, we have to be transparent with Financials, as big companies with big companies, they have to be transparent with financials and where things are going. And I worked with some nonprofits, I mean, we have to be very transparent, I think that trend is going to come into play with AI as well, people are gonna want to be able to see more than what they’re seeing. I don’t know, I’m not a fan of regulations, of course. But I know that if you’re not ethical, in what you’re doing, you, you we have to put regulations in place to help those who are not.

Michael Waitze 27:54
So if I’m a big company in Vietnam, right. And this just loops back to something you said at the beginning, right? I don’t want to put my code into open AI because I don’t want to give away whatever sort of coding secrets that I have. You said like MLMs, people are going to stop focusing on them a little bit. But I want to be a little bit contrarian here. I think what’s going to happen is that a lot of these big companies, if they haven’t already started doing this are going to build their own or use like off the shelf large language models, and only train it on their own data. And even if even if it’s just for marketing, right, they take all their own internal data, all their own internal data science, all their own internal statistics, and then create sort of constant new marketing data off of that we we know this, we can help you do that. Or we can personalize our own products by using this information. They can then be very transparent about that, because it’s only their data and the only training and on data that they either they know, and they’re never giving out that data to the public. Right? It’s almost like having your own private blockchain. Yeah, exactly. So do you see that happening as well? And are you in a position to be able to help companies do that and implement that

Rebecca Schwabe 28:59
we are actually implementing for ourselves. So yes, absolutely. We have the ability to do that. And I agree. Yeah, that’s where it’s good to go. Because people want to know, what’s going into things so and they want to keep control of their own copyright in their own IP. And so I agree with that, that that is where it’s going and that’s why we’re implementing for ourselves.

Michael Waitze 29:24
Okay. Rebecca Schwabe, a founder and COO of Saigon AI. Thank you so much for coming and doing that today.

Rebecca Schwabe 29:31
Thank you very much.

 

 

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