The Exchanges

Q Did you always have that intuition, or was that also built up?

A I think, um, that's, that's definitely built up. It's definitely built up. I, before joining App Labing, um, you know, in my previous job, I already carried both the engineering part, also the product part of the work. Um, as I, as I shared in the beginning of the podcast, when I joined App Labing, I saw that the engineers were kind of disconnected from, from business. So one part of my job was try to help our engineers to understand the full context. Instead of assigning tasks to them and tell them to do exactly what they have to do, I show them, you know, how their work is impacting the business. And because our, our, you know, engineers are very talented, once they are provided with that kind of context, they have a much better sense of what to build.

AI assessment note: “that's, that's definitely built up. It's definitely built up.”

Q So I guess most people are trying to rebuild right now because they just, they're acting a little bit more reactively versus proactively is what I was trying to get at. And so in terms of others that are trying to rebuild right now, what do you think the biggest mistakes that they'll make are?

A This is a very interesting question, right? Like everyone wouldn't, I think everyone here would agree. The AI is making, is helping us to Improve the productivity of every engineer, every business person tremendously. But it's also kind of interesting to see that not every company, not every organization has been able to multiply their output as a result of the AI empowerment. I think a big, big mistake that people are making is that because of the AI, building things become easier. People are making a lot of bad decisions, bad tastes, wasteful ideas that actually offset the empowerment of AI. Right? You, you build things faster, but you're, you're making a lot of bad tasted ideas. And in the end, you might not see the end result. And this is also why I don't think, you know, we just have to rebuild everything simply because of AI. We need to understand what's the problem we need to solve. AI really doesn't, AI really helps, helps us to solve the problem, but actually doesn't change the problem we have to solve.

AI assessment note: “a big, big mistake that people are making is that because of the AI”

Q now for the AI era. I've listened to many of, Adam, your interviews where you're talking about you build for the next, you know, three, four or five years along ahead of you. Um, so whatever you built right now, you know, you built back in 20, 22, 23 or so. I'm curious, Does that still hold? How do you rebuild in such an era where things move so fast?

A Yeah, I, I don't really want to rebuild. You know, in the last three years, we've built a lot of things that made us feel so proud of. Um, AI is, you know, powerful, but I don't think that's a reason for us to rebuild. What AI really helps us to do is, you know, to build on top of what we have today. Um, with, you know, the empowerment of AI, we were able to achieve much more. You know, I have been thinking about this, this, this, this growth trajectory, this growth journey of, of our company. As, as we shared previously, you know, the size of the engineering team was basically the same, the same size as, like, today as three years ago. And typically when a company went through such a growth of their business, you know, your, your business, scale of your business is much larger. The type of problems you're solving is Also much more sophisticated, and this kind of growth have to be paired with the growth of headcount and team size. But luckily, you know, I feel our growth just was perfectly in sync with the, the, the, the, the, the advancement of, of AI. So as the problem we are solving becomes more complicated, and our team was elevated by AI. So we were able to manage the same size of the team while we're solving more challenging problems. As we're speaking now, you know, we build this model, the generation, new generation model three years ago. Now with the, the assistance of…

AI assessment note: “I don't think we need to rebuild anything, but we are definitely building on top”

Q So what were some of the big questions that you started to ask?

A I mean, I guess I didn't start asking questions. I know. Well, my, what are my position is that when I joined, right? I saw those gaps and were not a great model at the time, but before training a model, you need to have the right infrastructure for it. Uh, so my first month here, I actually just started writing code. It was very easy. I was working directly with a bezel. We did not have a lot of meetings. Which, by the way, in the beginning, I was not very used to it. I asked Basil to set up weekly one-on-one meeting with me, and he didn't understand what that was, because it's not a thing here in Abloving. Um, but, but as soon as we, we canceled that meeting, because we, we, we communicated with each other, mostly with code, and that was much more efficient.

AI assessment note: “I mean, I guess I didn't start asking questions.”

Q Well, where do you get your inspiration from? Do you get it from outside sources, from, like, abstract things?

A First principle. I guess you just have to, you know, like, ask the questions of what, how, and why. I think a lot of times people overly focus on what. People say, oh, there's AI. Let's do some AI in the company. That's a, like, you know, that's a, let's see how AI can, can, can, Help our business. And then they will start hiring a big team working on AI without asking how? As AI, how do you apply AI in your product? And then why? Does your product actually need that? Right? This applies to AI. It also applies to previous generation of technology. So when we were building Exxon two point O, the team was really, really small. You know, I had a lot of experience in the industry. I knew how a recommendation system looks like in many, you know, big companies. I think it was impossible. It will be, it will have been a big, big mistake if we try to hire a huge team and try to replicate every single piece of that. So it is important to filter through all the noises and figure out what is this a one percent or most important thing, and you have everything you can in your team to focus on it.

AI assessment note: “First principle. I guess you just have to, you know, like, ask the questions”

Q No, I mean, that was a great answer. You've been really hardcore on making sure that your team is AI active or AI enabled and AI first, whatever you want to call it. How do you evaluate people on that? How do you make sure that they're continuously upgrading themselves and upgrading their skills?

A So for people, of course, I think, you know, uh, the AI works differently in the business team, um, comparing to the engineering team. I can speak about engineering team. So So AI has been evolving very rapidly in the last few years, but there's still a boundary of what AI is good at, and we are, what AI is not yet good at. For example, even today, AI is extremely good at answering questions, writing code, probably also is good at doing research and answering, like help you to shape your, your, your, your opinions. But still AI is not good at doing long horizon plannings. A lot of work that we have to do today requires long horizon planning. So then we still need human to be involved. Maybe one day AI will be better at that. So the way I see the relationship between AI and our employee, our engineer, is I don't want our engineer to sit next to AI. I want our engineer to sit on top of AI. So as AI is improving, there's a boundary between AI and a human is also moving. Right? Previously, AI was only useful for correcting the syntax errors when they are coding. And then AI became better. AI can, like, complete the whole phrase, the whole paragraph for your coding. And nowadays, AI, you know, can just, you can just give him an idea. AI can write an entire PR. As AI is improving, humans just sit on top of it and focus on what AI is not good at. So because of this, I think almost eve…

AI assessment note: “I want our engineer to sit on top of AI.”