# Scaling Engineering Culture and AI Integration in Streaming

**Podcast:** Tech Lead Journal
**Published:** 2026-04-06

## Transcript

And the minute you allow some slop, be it human generated or AI generated, then the ball rolls downhill.
New engineers come on and then they reproduce that slop and it keeps getting worse.
Today's guest is Tommy Sullivan, CTO of Video, Indonesia's number one streaming platform.
Tommy has spent over a decade building world-class engineering culture from the ground up in Southeast Asia.
When you came here, maybe not enough good engineers available.
How do you actually overcome that?
Indonesia went through a lot of brain drain early on.
We had a team of about five people.
Several of them were on minimum wage.
It's better to find people who are hungry and who are smart and who are growing and learning, hire for attitude and not just pure aptitude.
Feedback is a core principle of extreme programming.
And feedback in terms of the code that you're writing, in terms of having a CI that's running all of those tests, in terms of if you are a pair of programming or working very closely with people.
I myself also fear one day I'll be obsolete.
Would you think now that with this AI you don't need so many engineers?
Writing code becomes more commoditized.
Many aspects of extreme programming are still valid, but other aspects in terms of building a product are becoming more and more commonplace.
And I think that's what's becoming more valued.
My conversation with the business is all of look at all of these opportunities that we can pursue.
We wouldn't otherwise be able to.
I too have to feel a little bit disheartened in terms of the amount of time that I've spent coding that syntax is no longer important.
But I need to also remember that, wow, I can have an idea now and get it out so much more easily.
People need to be cognizant of the Gartner hype cycle.
It's a great time to get experience into play with all the things that are developing in AI.
When you technically have an understanding of how they operate, nothing is magic in tech.
The whole open claw thing is essentially markdown files.
The limit is not sentience, but markdown files.
Hey, quick pause.
My goal with Tech Lead Journal is simple.
Learn from the best in tech so we can all grow together.
If this resonates with you, hit subscribe to follow the channel.
It's the biggest way for you to support the show and help us keep bringing great guests and insights to you.
Thanks for being here and let's get back to it.
All right.
So thank you so much for your time, Tommy.
Today, we are going to talk a lot about, you know, building engineering culture at video.
Also some of your stories.
So, yeah, welcome to the Tech Lead Journal.
Hopefully, stories and jokes.
We can mix them all up.
Right.
Thank you for having me.
Yeah.
So for people who don't know Tommy yet, Tommy is the CTO at Video.
Video is actually one of the largest OTT or streaming platforms in Southeast Asia.
So it's actually a pleasure to meet Tommy.
So we had a connection through Mohan.
Mohan is my past episode.
So always stay tuned.
It's always a pleasure to have people who are doing great things in engineering around this region.
Well, I'm glad that I knew about the Tech Lead Journal before Mohan introduced me to it.
But I'm glad also I have a bit of a chance, an opportunity to rebuttal anything that he said without him being able to complain.
But yeah, so thank you for having me.
So let's start maybe a bit from your story.
Like how did you end up here?
I think if I'm not mistaken, you have spent over than 10 years being in Indonesia.
So someone who grew up in US, how did you end up here?
Well, I started out working for kind of a dev shop in San Francisco and right after CMU, after college, then I was working at a dev shop.
And there I had the opportunity to work on a project with a much better dev shop.
And around that time, this is around 2009.
And kind of in the middle of hyperscaling in SF area in terms of Facebook and Zynga and all of those things were blowing up.
And this is also right past 2008 financial crisis.
And so I was just growing my career, starting my career.
And I was working at, again I say dev shop, somewhat pejoratively in the sense that a place that's mostly focused on getting stuff.
something out just getting it done focused on the result not necessarily how it happened and I worked on a project with also with some engineers from pivotal and at that time I saw the work that they were producing and how they were essentially like craftsmen in the truest sense of the word and it was inspiring around that same time I also had the opportunity I was interviewing with pivotal and interviewing with Airbnb and you know I I had the experience of working in a dev shop in seeing how a small group of engineers like if you're going to be a junior on a team you could potentially be at the whim of whatever your team lead or the CTO wants to do they could be a good idea and actually I have no scars on that for my first company you know but but I saw how they control the tech stack the you know the decisions in that culture and you know I saw how they control the tech stack the you know the decisions in that culture and you know I saw how they control the tech stack the you know the decisions in that culture and you know I saw how they control the tech stack the you know the decisions in that culture and I was very well I was you know talking with tech stack the you know the decisions in that culture and I was very well I was you know talking with tech stack the you know the decisions in that culture and I was very well I was you know talking with Brian and Nathan and in the early group at I was very well I was you know talking with Brian and Nathan and in the early group at I was very well I was you know talking with Brian and Nathan and in the early group at Airbnb I was a bit cautious of joining a Brian and Nathan and in the early group at Airbnb I was a bit cautious of joining a Brian and Nathan and in the early group at Airbnb I was a bit cautious of joining a two three-person engineering team at that time Airbnb I was a bit cautious of joining a two three-person engineering team at that time because I knew that no matter what they could potentially you know steer down a route that that you know who knows and so I decided to join pivotal and thinking of the long term what was best for my career to become a craftsman in software development and so I'm very happy with that decision no I certainly would be way richer being the third engineer at Airbnb but from pivotal then we opened an office in Singapore and Singapore is kind of like Asia 101 in terms of it's a very comfortable market to get into and but in Singapore you also have the entire regions kind of unlocked it's very close and so there I was working with pivotal consulting with tech companies in the area and Pivotal is interesting you they they do engage for software they're close now but they did engage for a software development development but their svp had a good quote once that was kind of like if you're engaging with pivotal just to increase your like engineering manpower or your to add more devs to your team you're wasting your money and because it's like the goal of pivotal was to go in there and to help improve the engineering culture of the teams and so my job as a consultant was to help focus on engineering culture and principles and uh so i was doing that throughout the region and i started doing it with mtech here uh with with mohan yeah right i know a few people from pivotal labs and in fact i used to work at potworks as well i think we kind of like admire each other so we didn't touch on that so we were kind of like into craftsmanship you know building the right culture and and what you said is right right so they hire people to labs or it's not just for hiring another engineer but it's to learn the best practices and locate the practices and all that right so i think uh thanks for sharing that because i'm sure today we'll talk a lot more about building great engineering culture but um maybe let's let's start from after the story you know you you come to indonesia what did you see here in terms of back then and then if you pull it out maybe 10 plus years now like how has the taxing changed it's it's gone through quite an arc right um when when mohan and i first came here um it was just as indonesia was starting to like tech boom all of that you know sort of like gojek and tokopedia traveloka you know all of these things were starting were at their seed phase right and so and we saw those early teams there and when and it was also a lot of while that was happening there were also a lot of traditional companies that were trying to digitize and become digital natives and stuff you know we had like matahari mall i remember they saw amazon and you know back then there was a phrase of them having 500 million that they were investing in terms of just like and so they were building a product to replace their you know pre you know non-online presence and they were building three at the same time actually because they had so little trust that one would be successful and they had so much money and um that's really hedging bets in terms of just it it shows it such a problem with trust of they had a team working on their product and they had another team working on a competing product oh that that just because they were trying to hedge their bets and i think that there was an issue with with regards to trush and with regards to understanding software engineering um and so like when mohan and i came first came here um certainly i mean the the tech we that we had engineers we had a team about five people when we first joined and several of them were on minimum wage you know and and so the scene there then was engineering is just such a side function and um the same as like an office boy or something in terms of just you know make the website work or whatever and mtech really invested in engineering with with working with pivotal and then pulling mohan and myself in and we saw you know like people here working on laptops like with or laptops connected to monitors like with literally just down the office over here of like 12 inch monitors and and stuff like that and it can seem hilarious now you know yeah but um and i can understand why the company was in a state like that it's kind of like a vicious cycle you know like you can you can hire engineers and then and then they want the fanciest tech and all of these things but that's just one piece of having like a proper engineering organization having a proper culture right but that that cycle is also two parts in the sense that um it's the company not necessarily having invested up until that point but it's also the engineers not investing in themselves to some extent i want to again normalize that with respect to you know a lot socioeconomically not everyone can can afford you know a great investment in terms of the proper tech and the computers and all those but i do think that one um and for me really when i when i see like these um you know with this piece of one sort of uh sort of thought or thread or theme one theme that i think that uh i keep in my mind is the rifleman's creed which is uh it needs to be taken with a bit of um uh candor but is is that you know like uh this is my rifle without it i am nothing without me it is nothing and i think that as engineers and in tech our computers are a rifle and for me being able to buy anything i don't like don't dislike that at daily with whatever i'm to doing and quickly and for me being able to be more human being and isn't equal to my ability of getting this far afield what im Literally just Specific knowledge and how i do that from an engineering perspective that's�� a very powerful city year definitely um so i don't live in an industry like the давай aisle government center but you know i was decades ago computers are our rifles you know yeah and we need to have the most effective working tools at our disposal and so the company not investing in them and engineers not investing in themselves for that I think that that's a perspective change in terms of these tools being so important and investing in oneself in the team so so early on when Mona and I came in we kind of that was the texting that we were familiar with yeah during that time it was certainly we saw a lot of organizations that were more focused on LinkedIn post and more focused on what is popular to say versus what they're actually doing and you know I remember early on we had a lot of poaching happening you know we tire engineers you know develop them and then organizations of poaching them in some of the organizations there was a peer-to-peer lending situation for a while they kind of grew up there were 300 peer-to-peer apps here of which only like 200 were approved by Ogica and all those organizations were democratizing finance and such an altruistic goal and yet they were charging 500% APR and and so in some of the actually at one point Mona and I had staff that that owed significant amount of money there and we were building a team the culture building trust with our engineers and even more donated money to help you know what we pay that pay that debt off and that staff which i think is just a side effect is just a symptom of the trust that we're building the team and the the level of engagement that we're building as the team and that has served us for years instead yeah so i believe right back when all these some прав Ив إلى I mean, once after, you know, the Gojek, the Toko started investment coming in, I mean, we can see a lot of engineers.
Soft bank, crazy money.
Yeah, a lot of engineers starting popping up.
You know, the salary also increased very high.
What do you see now?
Because, you know, the economy situation, especially in this region, may not be the best at the moment.
And also, you know, with technological changes and all that, do you see people are moving up engineering or, you know, like, what is your sentiment here?
Well, Indonesia went through a lot of brain drain early on.
So when we first came here, there was, like, good engineers in Indonesia were leaving Indonesia because they could make more money in the Bay Area or whatever.
And so that was something that certainly suffered.
What is actually, I guess, a positive benefit of the crazy money that came in, the soft bank and all of that, is that a lot of those engineers started coming back because they realized the opportunity.
And so we, those engineers have, you know, Indonesia has even surpassed some of the developing markets in the area of Vietnam, you know, I think of in Malaysia, in the sense that because VC money and all this money came in and just saw the crazy opportunity and just kept flooding it to the point where it became a bit of a bubble in the sense that there was so much growth and opportunity here Yeah.
that it led to essentially organizations that were never going to be profitable and based on hype more than actual results.
Yeah.
And so now it has kind of come down a bit and it is, I think that now we're in a more mature phase of tech companies here that are focused on profitability, that are focused on economics, right?
So maybe for the audience here who are not familiar with Indonesia or this region, right?
Tell us why the opportunity in Indonesia is such a big deal, especially considering if you look at the global, you know, population and countries, right?
Indonesia is always there, like maybe the digital, social media penetration, number of people, definitely population is huge.
What, if you can summarize, what do you think is the opportunity in Indonesia compared to other countries without, around the region?
I think that, you know, there's that funny quote where Indonesia is the country of tomorrow, even tomorrow with regards to some, because a lot of times it constantly feels like it has so much opportunity and sometimes it's not realized, you know, and that opportunity stems from vast natural resources.
It comes from a relatively significant middle class, right?
Yeah.
It comes from a huge population.
People don't realize fourth largest population in the world.
And so VC companies, engineers such as us, you know, see that, you know, if you can have a product or something that sticks here, it can grow so quickly.
And Indonesia is actually very liberal with regards to business, I think.
It can be in terms of the employment side, it can be a little tricky in terms of, you know, hiring and firing people is much harder than in the United States, or in Vietnam or something like that.
And certainly regulations can be can be tricky.
But on the macro scale, the ability for tech penetration is significant.
I remember, for example, a concrete example of that is my father was involved in healthcare in the United States.
He was the chief doctor for Medicare and Medicaid.
And he was working on telemedicine in the United States.
And in the United States, your doctor's license is for one state.
And so he was working on telemedicine in the United States.
And in the United States, your doctor's license is for one state.
And so if you want someone in California to consult with someone in Massachusetts, that is a totally different you need to be granted your MD needs you but you need to be relicensed in that other state, whereas Indonesia it is you just the entire archipelago is and and so the the ability to if you have a product or something that is working in Indonesia, I think that you can scale it very quickly.
Right.
Yeah, country of tomorrow.
I hope tomorrow will come eventually.
Yeah, so thanks for the quote.
So let's start into talking about building engineering culture.
You mentioned when you came here, you know, maybe not enough good engineers available.
You in fact, mentioned about five people, some of them are like, you know, kind of like maybe juniors, starting from a very low wage.
Maybe my first question, a lot of engineering leaders would be frustrated given this situation.
Yeah, how do you actually overcome that?
How do you actually, approach this situation?
Because you came from Pivotal Labs, I'm sure like, you know, top of the cream, you work with great engineers, having to face this challenge.
Like, what's your approach for leaders out there who are also maybe in similar situation, they don't have a good tech team, but they are tasked to actually build a great culture.
Maybe let's start from there.
So there was a quote I can't attribute, but it was I think it was Rob Mee or someone who was talking about building a team and having the opportunity to hire engineers who are familiar with your tech staff, or hire engineers who aren't in any way familiar with tech staff, but have that quality and that experience, right?
And always leading towards the whatever, the experience and quality, right?
And, you know, in terms of hiring engineers, sometimes people can be okay, I've got a XYZ, you know, tech stack, or I want to build, or I want to build some AI Python stuff, you know, so I need to find AI Python engineers, right?
And that is very short sighted.
And it's better to find people who are hungry and who are smart and who are growing and learning, and to then work with them in the tooling that they're familiar with, right.
So Mohan and I came in, and we it wasn't, we knew we were limited in terms of finding the team, it was more grow the team, right.
And, and part of the extreme programming principles that we that we brought in from our pivotal experience, one fundamental thing was pair programming back then, which is an incredible tool to onboard engineers and, and to onboard anyone in any process, where you do the work together, right?
I think that pair programming is certainly changing in this whole, you know, AI world, but in terms of two people sitting together, communicating, discussing the problem at hand, and whichever tools you use to solve that problem changes.
Right?
Yeah.
But, but the goal is to is to find the the hire for attitude, and not just pure aptitude.
There's a, I think I'm going to ruin one of your there's a phrase, it's I'd rather have a hole in my team than an asshole in my.
And, and it's, you know, hiring for that fit and letting the team grow it, letting the team hire to, to build that culture that you want.
Right?
Yeah.
Yeah.
Yeah.
Yeah.
Yeah.
Yeah.
Yeah.
Yeah.
Yeah.
Yeah.
Yeah.
Yeah.
Yeah.
Yeah.
Yeah.
Yeah.
Right.
So what we did is we spent a lot of time early on defining the culture that we wanted to have here.
And many of that is from extreme programming.
Some of that is, is influenced, you know, from our own experience.
A lot of it is around hiring people you want to work with, and that you that enjoy solving problems.
And, and so we grew the team from about five people to at 1.200, and at 1.4 offices.
And now we're back.
Back down to just one at one office here in Jakarta, but it's still a great team.
And I think that the, the tenure, the average tenure of the, the engineers on my team, which is about seven, seven years is indicative of we've, we've succeeded in that regard.
Right.
I think another aspect, I guess just that I, I take a bit of pride in the team that we built is early on when, when we were here, we were engaging headhunters, right.
To help find, to find engineers.
And one of them was a geek company, not, not geek camp, sorry, geek hunter.
And we were working with them to find engineers and in their onboarding form of applying to use them as a headhunter.
They had a, they had an input box that was with regards to which company would you like to kind of potentially poach from and what type of engineers.
And I'm very proud that their number one input was our company at that time.
So the results kind of showed there.
Right.
I think it's always great if you can build culture from the ground up, right.
You shape the people that you hire with and all that.
I want to go back a little bit on the pair programming that you mentioned, right.
Because I don't know, like these days it's not well covered because of the AI and all that stuff.
Right.
But I know that Pivotal Labs actually practice it like very religiously.
In fact, I heard like eight hours, you know, the whole working day is actually to sit in pair and work together.
Right.
So maybe from your view, having done this so many years, what are some of the great benefits of pair programming?
And also the one that you see succeed really well here in video?
I mean, certainly onboarding, onboarding knowledge transfer in terms of not having, you know, when you have two people working on the same project, the same project and having the same deep understanding of the code base, your bus count is reducing.
Right.
I think that also there are subtle aspects of it that that aren't obvious from the outside in terms of also mentorship.
Right.
When you're working with someone more senior than you, mentorship isn't only when you have a one on one with your manager.
Right.
If you are working together, that is an aspect of mentorship.
That's a softer skill.
Yeah.
That people don't, you know, see intuitively from the outside.
And so, I mean, pair programming is one of the many sort of practices within within our the extreme program.
And that, that we were using.
I think that now with regards to the world of AI, I think that programming in terms of like writing, you know, the the the code is is we're becoming more abstract layer on top of it.
Right.
Yeah.
We're not writing.
I'm curious, have you ever written assembly?
No.
Yeah.
Uh huh.
Have you written C?
Yes.
Okay.
C++, not C directly, but C++.
Okay.
Yeah.
Okay.
And and, you know, it's like, I don't think you're ever going to write C again, you know, or C++, you know, but we're kind of like layer subtract on top.
Right.
And so as that abstraction layer continues to rise, there's still that huge, you know, the the the activity of two people fleshing out what do we want to get done?
How do we want to get it done?
One aspect of pair programming that I think is very hard to translate now in terms of the AI thing is, is we used to do a lot of ping pong pairing, where it's like, I write a test, and then you make it pass.
And you write a test, and then I make it pass, which was quite enjoyable.
And that's why Pivotal, when they IPO, at some point, they IPO, they they all had like ping pong paddles, because it was, you know, part of how they how they did engineering, right.
And that is a bit harder in terms of the, you know, with an AI agent and stuff, unless you really lock it down.
But that that said, you know, extreme programming is, there's a quote from Martin Fowler, that's, if you're doing extreme programming, the same way you were doing it a year ago, you were not doing extreme programming, right?
Because it's just constantly about assessing what is working in your organization.
If it's working well, what does it mean to take it to 11?
You know, and, and an aspect of that pair programming is just a component of two people thinking on the same problem, how do we take that to 11?
Right?
And we're adapting that in the new world, right?
Yeah, definitely, the world has because of the ai especially in programming software development right yeah ai seems to be like a huge disruptor i would say a huge leverage as well for some people um so maybe if i can ask you now your pair programming practice how how has it changed with this ai um which part that you think will get reduced maybe like you know the ping pong thing the test uh writing but what part that gets more leverage yeah yeah well i i do want to correct a little bit because you said in your pair programming practice which is that's not our practice is a bit more on the extreme programming which is many things right which is um a component of values and then principles and practices that uh practices pair programming is a practice right and practices are defined by principles and principles are sort of driven by values and these lead into each other and that in that pair programming aspect is is sort of the same thing right and so i think that's a good point certainly changing but but the other components of um communication of transparency of fast feedback i think that a lot of those um are just as true as they as they were ten years ago um one thing that i think is is changing a bit that uh an experience that both both mohan and i had is as we were working for pivotal one of our the office we were in got sold to a Japanese company which is a very famous company for one of the first computers we were working for we had a company a digital digital garage and um we had the opportunity to work with eric reese a bit and eric reese was um well known for his book the lean startup and i think that as writing code becomes more commoditized many aspects of extreme programming are still valid but other aspects in terms of building a product are becoming more valued and and so i think that there's a bit of a mesh that we're seeing in terms of engineers becoming a bit closer to being able to take on a bit more of product and objective of what is the goal of this individual component or feature that we want to do and at the same time also engineers you know people can a solo person can launch a company now yeah you know open claw or whatever and and they need to have that product product sense in terms of what they're building right and the the lean startup is a very good way to validate that right because um must have some quote around you know one of the biggest problems engineers have is like over optimizing something right and optimize and a common problem is optimizing something that shouldn't exist yeah you know and and so sort of those lean startup principles are you know validating the product getting it out there getting feedback and and so us us learning um from that sort of eric reese investment slash organization i think is also influent is is quite influential into where we're going right um we're also seeing that sort of product mentality we're also seeing here in video as well in terms of product managers and designers are starting to code a bit more right and and because it's opened that up for them so xp as a practice right i think XPA is very important to me because you know we all we all know as a software crossmanship it it brings a lot of value uh we can see it produce great culture you know great set of engineers and great quality output right and not just that i think when you see xp practitioners they are highly regarded as a software crossword right but i don't see in the industry it is widely practiced widely used my question is like how did you sell xp back then like you know that this will work at video and the other aspect is why is it not widely practiced you know around the industry so you asked about xp and how it is practiced but not wholly practiced across the industry and um how we were able to sort of apply it to the team that we were building and when you join a team i think you get a runway of trust that that works for a certain period of time and we were able to through you know focusing on mvps through focusing on sort of like lean startup principles we were able to get something out and to to deliver results which is what the business is focused on yeah and and from that we gained more trust and and gained more leeway but um you know there's that ben horowitz quote that we that we mentioned which is how startups tend to be like result focused and equally important is not necessarily what you get done it's how you get it done and while mohan and i in the leadership were very focused on delivering results we were also incredibly from the team perspective we're very focused on how we deliver those results and in understanding that that we were building a scalable platform and the word scalable so frequently in tech is with regards to the number of requests or anything like that yeah which for me is is not what i'm referring to it's it's the scalable in terms of after you grow and develop this platform for a certain number of weeks months years it isn't falling over right and and a component of xp definitely supports that which is the regards to the test driven development right which is equally as important in xp as pair programming or even more so um in the way that you're right building a system sometimes i think about um programming as укnder it because it's kind of Oke أي srog tgr it's you can write a script and it's kind of one way but if you're if you're writing it through in a test driven development manner you're writing code that that creates that output but it's also validating the output so it has that feedback loop yep and feedback is a core principle of extreme programming in feedback in terms of that feedback loop in terms of the code that you're writing the feedback loop in terms of having a ci that's running all of those tests feedback loop in terms of if you are a pair of programming or working behind the scenes or so.
very closely with people that feed that, that human feedback loop in terms of like, maybe we don't, you know, so I think that the XP there, we were able to apply the principles at work, show results, build on those and to continue to grow.
I think a very tricky thing in engineering now, if you ask McKinsey, he'll have a different opinion, but is to measure like ROI or measure productivity of engineers.
And fundamentally, I think that it is, is damn near impossible to do that.
I do think that the DORA metrics are relatively useful, but they, they aren't, you know, an organization tries to like measure people at the individual level and, and that's incredibly hard and DORA metrics don't do that.
Instead, they're looking at how is your engineering organization running efficiently?
Yeah.
The, the deploy time, the mean to correct, errors, you know, to roll back all that.
I think those are still very valid, right?
I think things that when someone writes a blog post that says, yes, we have figured out how to measure engineering productivity.
I think that's a very click baby because many of the other non-DORA aspects of that, uh, I don't believe so much.
One of the other aspects of that that are highlighted is in terms of like developer happiness, which is, which is valuable, right?
Um, but one aspect of that, that McKinsey post is, is around, you know, how many engineers are looking for jobs or, or how many engineers are being approached.
And I take great pride in when, when we started the organization here and engaged with a few headhunters is that, um, essentially video was back then we were under a different name, but was one of the top listed companies of people wanting to work with the engineers and poach from us.
Right.
Which for me is, is potentially an indication of, you know, developer productivity and success there.
Yeah.
And not to mention also the culture, right?
Because people want to work in a great culture company.
So I think, yeah, you kind of like mentioned a little bit like why companies should invest in XP.
And I like the quote, the Benz quote, like you should not just focus on getting things done.
Right.
But how you get them, how you get them done.
And I think it's more applicable now in this AI era, because I'm sure you have heard a lot of people thinking that AI can, you know, produce results fast, you know, doing vibe coding and all that, but not necessarily the quality is always best.
Um, especially if you want to talking about scalable, if you want to evolve the systems that you built over weeks, years, you know, decades and all that.
Right.
Um, I think it will be tricky if you don't have all these core practices in place.
I do think that it's very, we need to stay away from grandstanding though, that, you know, AI produces like lower quality code and stuff.
I think that what, what I'm happy we've done here is set some boundaries.
In terms of people being able to vibe code things.
And, um, and so as I mentioned, we have, you know, some designers vibe coding products.
We have engineers certainly, um, using, uh, AI assisted code in, in terms of our development.
And I don't want to limit and prevent and say that, you know, like, oh, all this five coded stuff is going to have massive security issues and all of those things, which, which it will.
Um, but, but what we've done here is create a little bit of a sandbox where we.
Allow, uh, designers and people who typically aren't writing code to produce products that can be used by our organization internally.
And so we just have those behind an IEP and, and they're essentially walled off from the outside internet.
I don't need to worry about, um, you know, huge security issues there.
Uh, if we were to take one of those and then give it to our users in production, I would be definitely more worried.
Right.
But in terms of internal tools, that, that can make our team more efficient and stuff.
Um, that's an area where if it works, it works.
Right.
You know?
Yeah.
I think this goes back to the lean startup principles as well.
I mean, you'll see a problem maybe using your product approach, build something, validate that whether, you know, there are people using it, find it beneficial and all that.
Obviously product productionizing something is a different level, right.
Especially if you used by, you know, external customers.
So I think, thanks for adding that.
Speaking about AI tools, what's, what's the, what's the, what's the, what's the, what's the, what's the, what's the, what's the, what's the, what's the, what's the, what's the, what's the, what's the, what's the, what's the, what's the, what's the, what's the, what's the, what's the, what's the, what's the, what's the, what's the, what's the, what's the, what's the, what's the, what's the.
What have video done in their engineering team?
You know, what kind of tools have you implemented?
Maybe if we can share some success stories, failure stories, that would be great.
I guess, let me start with a failure.
And, and it was potentially me, um, you know, like not listening to the sort of lean startup principles, which is, you know, AI and, and LLMs are great at producing a code and, and SQL, particularlyize in general hype app.
Right.
Yeah, I think this is having you, taking into account of, like, you know, why there are so many Nah, my error-فسus exhausting UI environment.
Right.
And so early on, we created a sort of text SQL inside chat bot, right, where anyone in our organization could chat and it would query our data lake and get them results and all of that.
And that was essentially I have heard of success of democratizing data and giving people access there.
But it didn't work in our organization because once it's wrong once or twice, then there's no trust there.
And it was fundamentally easier for someone just to poke a data analyst and tell them.
And it's also because of accountability.
Right.
If if someone's just just talking with a, you know, LLM and they're getting some data.
Results, it's a lot easier to say, well, I got the information from, you know, this person.
And so they're accountable.
So I think that that was us failing by building a product and tool that actually our organization didn't need because they already had data analysts that could answer those questions on the success side.
I think that we have had gains in terms of using video.
So our first usage of AI is jokingly around and not intern.
Yeah.
Which means like a child intern for the non-Indonesian speakers and and using.
So a lot of our video streams don't have a CTE markers.
Right.
So typical terrestrial streams or that are live stream video that's coming along.
Sometimes it'll have digital markers in terms of where an advertisement starts.
The break starts and ends.
And initially we would potentially have humans and not intern AI clicking and then doing TVC replacement there in in areas where it wasn't digitally transcoded for that replacement and and then starting to use models.
Right.
That are trained in terms of here's an ad break.
Here's not an ad break.
Yeah.
And so I think that the the AI in video and we've gone much farther than that.
Right.
In terms of using a models to detect.
Sentiment within videos to to identify characters identify, you know, for metadata labeling and all of that.
I think that it's most important to start from seeing a pathway of this is this is a a flow of information or this is a you know, this is a process that we have and then using A.I.
to then automate it and make it faster and better.
Right.
That's where we've had more.
Right.
How about in development?
Are you full into, you know, having everyone having license to, you know, whatever AI coding assistant tools or are you using as like a new, like when you do pair programming is AI now is like third person, like maybe in software development, what have you done?
In the development space, I think that it's gone through a few sort of step changes, I'd say.
Right.
Like initially with the like GitHub copilot and stuff, I was never really impressed and the team was never really impressed with those tools because that was typically kind of like one way of like you have our code base.
You potentially can load a portion of it in your context.
You can help answer questions in that regard.
Obviously, the model has been trained in terms of.
How to write a for loop and all of those things, and it's very useful there, but in terms of working with a larger code base, it wasn't as impactful.
I think there was a very serious step change when we I learned about Kleinbot Kleinbot, which I believe was a tool that was created from some ex-Claude engineers.
I believe they worked at Anthropic and then started Kleinbot, which was what it was, was the sort of copilot on all that LLM for coding.
But it was.
Also connected with a bunch of pre prompt layers where it was, I'm going to ask you about this code and what you're going to do is you're going to check this memory bank for this information about our system.
You're going to give me these recommendations and all that.
We're going to talk about it, work about it, and then you're going to update that memory bank.
Then you're going to update our coding styles and that is creating a feedback loop in that feedback.
For me, that was a step change in terms of it's not just a one way street to copilot.
It's a feedback loop.
And so then we started using LLMs and AI much more significantly because then it's also improving your documentation, which is also improving the recommendations and so forth.
Right.
Right.
And then so that feedback loop is valuable.
And now I think we've kind of gone with the newer models that are just so incredible in terms of being able to run much longer and have much more of a context window.
I don't know where we are.
I do know that, you know, we definitely enjoy using Claude Code more than engineers.
I think that it's a lot of success there has also been in terms of like, here is a pattern that I have identified within our system, be it of the 20 different apps that we have or services that we have.
You know, here's a pattern that we want to that we like we use and we we are inconsistent in terms of the application of that pattern.
And help me apply it to.
This other place, help me apply to that other place.
And in that is an area where we've had success as well in those kind of those refactorings or those sort of like pattern translations.
Right.
Speaking about these patterns, do you actually encode them as like, I don't know, prompts, you know, or maybe skills these days?
Claude has support for skills or some kind of like agent MD or whatever.
Is there any such practice where you, you know, encode these patterns?
Yeah.
I mean, we we certainly use.
Like we the our DevOps team is actually quite good in terms of they've created a a layer for an MCP to help document, you know, the various aspects of the infrastructure that we're running and and what services are best at certain things.
Right.
And so that MCP has been useful, also a great MCP with regards to, you know, our data lake in terms of the thousands of tables that we have in what you can find where.
Right.
So those MCPs have been useful.
They don't cure cancer, but but in terms of, you know, like a bigger factoring in that regard, that would be something that would be, you know, someone on a branch and and defining it, writing it into some UML potentially and and defining the problem within some sort of memory bank files, whatever, however, those be named and and selectively applying those to pieces of data.
Right.
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massive huge chunks of code and then later they write massive huge chunk of tests yeah and it's kind of the opposite putting the cart before the horse and and so employing them and working with them from the engineering perspective certainly of applying our principles is still valuable right speaking about AI I I'm sure many people in software development are quite scared yeah I myself also fear one day you know I'll be obsolete because this model tends to improve significantly yeah you know week by week month by month and in fact we can see every time Anthropic release a new thing the stock market even could drop yeah the SAS the security products and all that for you as a tech practitioners for many many years and in fact you are also someone who is trained in you know great engineering practice and all that what's your view about the impact of AI would you think now that with this AI you don't need so many engineers you know you can cut number of engineers or do you think it's more like an amplifier that you can even increase number of engineers or something like that yeah I mean my conversation with with the business is all of look at how much more we can get done look at all of these opportunities that we can pursue that we we wouldn't otherwise be able to right so it's more on the amplifier side of things video is in or we try a lot of things and and some of them fail some of them don't what's important is that we don't invest too much in the individual features or or products and we put it out there with minimum viable product and we see the result and then we double down right if it's working so certainly it has been an amplifying aspect right of of what we are able to get done and what we were able to experiment with right I think that it is scary in the sense that you know both we we have spent considerable time learning the syntax of various languages and stuff and that is something that you I'm sure many people I'm sure are having existential crises in terms of you know all of that is is no longer valuable I think that I started writing code because I did like to get things done and I like to see the result right and there's just a tool in that process and and I to have a feel a little bit different about the process than I did in the past.
disheartened in terms of you know the amount of time that I've spent coding that that those that syntax is no longer important but but I need to also remember that wow I can have an idea now and get it out so much more easily right so I think that we should I you know talking with the team we were talking about pair programming and stuff and we're doing less programming and and more I hate to say prompting but but you know leveraging the tools to get things done mmm right so maybe speaking about culture video culture what are other things that you think are unique to video and that you are proud of maybe other practices that you have apart from you know those things that you have mentioned like the XP practices you know the AI usage the experiment thing that you have any other culture that you want to highlight here your question was about unique aspects of videos engineering culture I think one thing that has been valuable in terms of building videos engineering culture as we did start with that pair programming we did start with small teams and and when you and when you have pairing when you have a good distribution of knowledge within the team where you don't have silos of you know this guy knows that thing and he's the only person that knows it when you build that resiliency in terms of multiple people knowing what's going on in the place then it's easier to move a different��둘 people around between teams.
Now, certainly we have experts who focus on, we do have some engineers who are focused on just video transcoding, right?
And that is not necessarily applicable to other parts of the business.
But when you have engineers who are not necessarily wed to a particular component of the system because they've built it with others and they're pairing, what you can do is you can move engineers between teams more easily.
And so that has been a valuable tool for me as an engineering leader in the sense that I can see someone who potentially is getting a bit bored on their current team with the particular problem that they're solving.
That doesn't mean that they're not necessarily putting in the work, but I can see that they're not learning as much new within that domain.
And so then I can move them to another team.
And when we create a culture where moving between teams is quite normal, we can move them to another team.
And so that's normal.
Then it's easier to maintain someone's interest.
And when I had an engineer who early on was just exceedingly good in a particular area, and I didn't necessarily have a career growth roadmap of, oh, now these engineers are going to report into you.
Now this is going to happen.
And so instead, the way that I dealt with that was by to try and move him to another team that was a bit more in this...
This example, it was a bit more ML focused and the training things.
So he could at least get some knowledge there.
I remember my dad used to always tell me, you need to work at a company, you need to either earn, yearn, or learn.
And I'm trying to pay my engineers the best I can.
But in terms of yearning, yearning in terms of just loving the company, fortunately, we do have a product that is quite fun in cases in terms of we do have some entertainment.
But the one thing that I have the most control over is the fact that I'm not going to be able to do anything right.
And so being able to make sure that someone in their career is constantly learning from others, and it's not always going to be forever on one team.
You need to keep people mentally challenged.
So that's one aspect of our culture of moving people around that I think has been valuable.
To that extent, to double click on it further, is we have team leads, and we use the phrase of someone being an anchor.
And I think it's quite unique that here, the team leads are not always the most senior person on the team.
And because if it's the most senior person on the team, then well, age is linear.
So it doesn't really rotate.
So what we do is a lot of times we have team leads who are, you know, experienced, but it could be someone who's hungry and learning in that space.
And so then you have a more senior person on the team who can back lead them as the team lead.
And so that's another opportunity, instead of switching someone from one team to another team, you can back lead them as the team lead.
But it's to potentially rotate the team lead, such that they can play a different role within that team.
And then that's mentally and intellectually challenging for them, while it's still, you know, bettering our business, right?
Speaking about learning, I think thanks for sharing all these great things, right?
Because we tend to forget that you have to build a culture where the people in your team is learning, right?
Touching a bit back to AI about learning, right?
Because a lot of people now, you know, they're also have the fear, we are not learning as much as possible, if all we do is just prompting, asking questions to AI, and this model gets smarter.
But we don't necessarily get smarter, because we don't have critical thinking, we don't, in fact, worse if we don't validate the output that AI is doing.
How do you prevent this in your team?
How do you prevent, like, for example, AI slop that the team is producing?
Because, you know, it's so easy now to produce anything.
Within just a few prompts, you can, you know, like generate something.
Like, how do you prevent, you know, people to stop learning and producing crap output?
Well, I think that's something that individually people need to be responsible for, right?
It's very hard for me to, if, you know, I've seen, I've heard of some interviews and so forth where people, where the interviews have anti-AI tools to make sure that, you know, you can't use AI in the interview and stuff like that.
And to me, that sounds a little bit like banning calculators, right?
And so, I think it's important to use the best, best tools that you can at, at any time, right?
Right.
So, I'm not gonna, that said, with regards to like brain plasticity and all of these things, you know, if someone is offloading their thinking to an AI tool and, and not thinking themselves, then it's, it's, it's, it's really hard for me to inspect, you know, whether or not they are offloading that thinking.
Critical thinking is important, not just in soft, you know, in so many aspects of life, right?
A funny aspect in terms of producing slop, don't know if I'm making the cut, is, you know, we have built a culture of people wanting high quality and high quality results.
And in the minute, you know, you allow some slop, be it, you know, human generated or AI generated, that you allow some slop to come through, then, you know, the, the ball rolls downhill, right?
People, you know, new engineers come on and then they see this slop and then they reproduce that slop and it keeps getting worse, right?
Right.
And, um, I remember once a person on our team in, us, us having built the culture of constantly trying to, to raise the bar and to, or at least maintain it, right.
Someone was so passionate on our team with regards to the work that someone else produced.
They were looking at reviewing someone's uh, work and called it Haram, uh, because, because they said that this is not of the quality Right.
you are being paid to produce and, and so, it's essentially you being laid off.
you being lazy and not putting in your full responsibility as a Muslim.
And so I think that that was just indicative of the, now both of those engineers are still in our team, but I think that it was indicative of the passion that someone had for defending a code base and not allowing just garbage to come through.
Yeah, culture is definitely very important, right?
When you have these people setting the bar that everyone should meet, right?
You know, it gets replicated very easily.
Same thing happens if you start, you know, producing worse quality, right?
Your culture will also degrade.
There's that, what's the phrase?
And correct me, you can have speed, quality, cheap, you can have it fast, quality, cheap.
But then another aspect of that is also, Is the scope.
Yeah.
Right?
And so on, in terms of the, there's the fast is one that I will compromise on, right?
In terms of like, this may take a while.
There is the cheap, well, we're within our budgets, you know, however they are.
But the quality is not one that I will sacrifice on.
But the one that engineering leads need to be most careful of is with regards to the scope.
Yeah.
And actually that's a direct quote from the extreme programming.
And so, is we need to be very careful.
That is the lever that we need to play with.
Speaking about setting the quality standard, right?
Sometimes it's very hard to define.
But is there any metrics that you guys always look at to ensure that, okay, we can see it dropping, that people should, you know, get it back up?
Is there any metrics or some kind of?
Yeah.
Metrics are always, you know, I think that some of those, you know, like, Yeah.
Certainly, you know, early on people can take, what is it, Goodhart's law, which is that any, when at any time any metric becomes a measure, it ceases to be a good metric.
Yeah.
And I have heard of teams concerned with regards to lines of code that are covered in tests and those things.
And I think that the metrics in terms of quality that we are most focused on is we do have blameless postmortems, right?
And whenever, whenever something goes wrong.
And you're having a postmortem, ideally, it's not one or two things that failed.
It's like six or seven things that failed, right?
When a plane crashes, it's not one failure.
Yeah.
And so, so I think that it's in that review of what are all of the causes that we go through as a team, blamelessly, you know, and, and, and that is where we look at, you know, potentially an aspect of the system was, was built, you know, rarely.
It could be.
It could be true, but rarely are we framing it with someone, you know, did a bad job or someone, you know, dropped the quality bar because it's always under the understanding of, you know, quality is a function of time that was spent on it.
Right.
And so, but we look at them from that, those postmortems from that angle and it's there where we decide.
Now, certainly if you're having, I saw you had a great interview with your code as a crime scene and, and, and that's an area where you can, you know, step back and look at the metrics and see whether.
Uh, something's being changed too frequently and potentially, um, so there are potential quality metrics and kit that could be looked at in terms of design of code or design of systems.
Right.
Um, but it's nuanced and everyone's a contextual, right.
Um, certainly like downtime and uptime systems, those door metrics in terms of, you know, the time to recover and all of those are, but those are more an aspect of the.
Yeah.
I mean, I think a lot of time, uh, people just get obsessed with, uh, the way that things are or what, how they, what, how they're connected to the, to the team or to the system.
Right, right.
Right.
And so I think, you know, always the, the, the, the most important thing is just the team having produced a system that has quality.
Right.
Yeah.
Not necessarily, like, is that line of code?
Yeah.
Yeah.
I like that you touch on, you know, blameless.
Post-mortem right.
Coming back to psychological safety within the team and also the systems.
It is a very nice app, comparable to all other streaming apps out there, the Netflix and all that.
The quality is quite good.
But speaking about the content industry itself, we all know that there's a major competition about this, and also the AI now can produce seemingly great content as well.
What do you think is the changes in this content landscape?
What do you see happening within the next one year or so?
It's been for the past 10 years where we've seen auto-generated content on YouTube.
It's very easy to have in the publishing space, it's very easy to have auto-generated articles.
And I think in the news space, those auto-generations have been around for a long period of time.
I think that it's...
Where the content space is going is you can look back at sort of the publishing industry, and so like the Wall Street Journal, maybe 50 years ago, the Wall Street Journal was probably 80% advertising in terms of the revenue that they were receiving was from advertising, and 20% subscriptions of, back then, the physical newspapers that people were buying.
And now it's shifted to advertising is much smaller and much more on the subscription side.
And given that news articles and the articles...
The articles being written in the Wall Street Journal can be auto-generated very easily and very quickly.
And then the question is, why are people still subscribing to them?
And I think that that's an aspect of trust and of quality.
And when you're reading an article from the Wall Street Journal, you don't need to worry that this...
Now, granted, they can be factually incorrect or something, but you know, you have such trust that they have high quality journalism, right?
And in the video space, in V-I-D-I-O space, or in the content space, I think that YouTube has some incredibly high quality content, but it also has a sea of auto-generated kind of garbage, right?
And so in the streaming space, I think that it's gonna be going through a similar shift in terms of the advertising component is shrinking and shifting more towards the subscriptions of...
You know, for high quality content that has been curated.
Now, granted, if AI can produce high quality content that people like, then that's great, but that's not gonna be a one-shot produce 10 series episodes that's good enough for Netflix, right?
Yeah, right.
I was just in India working with Geo Hotstar, or talking with them about potential collaborations, and they don't really use AI for dubbing at all.
Dubbing...
Which is a huge component in India, and they still feel like the quality isn't there.
And so, I think that producing content that is of the highest quality possible, because people's times are limited, and so, yeah, not eroding the trust from your consumers by creating that slop for them, right?
Yeah.
Yeah.
Video does have...
Recently, we just launched a series, a totally sort of AI-generated series, but I don't think that it necessarily...
There's a lot of fear in the industry in terms of destroying jobs, or in that regard, in terms of artists and content producers, but it was a very long-running series in Indonesia that was five, six years, 500 some odd episodes on TV, and I remember going to that production house.
It was about 10 years ago, and it was probably eight, nine people, guys, girls, working in Blender, creating some animations and all of that.
And now, it's eight, nine people that are working with Vue 3, C-Dance, various models, still crafting those together to tell a story.
And I think that the only difference in the size of the team is about the same, the cost of the production is...
The production is pretty close.
I think that we're getting better in terms of reducing cost of production there, but the output is so much better.
And so, it's about leveraging the tools to create something that consumers want.
Right.
So, if I'm not mistaken, this series is called Kwarda Pak Somad, right?
It's like Pak Somad's family in the English term.
But this one is speaking about animated series, right?
What about live movies?
Because if we see C-Dance...
Yeah.
People have prompted a lot of seemingly cool kind of like live action thing, right?
Do you think it will also kind of like disrupt the industry, like all these people, maybe the stuntmen, so the background, whatever that is, right?
What's your view on this?
I guess before I get into the live action, one thing that I would flag is somewhat similar, I guess, is...
So video, a huge component of our consumers are looking at live sports.
Yeah.
Right.
And so, I could just as easily create an AI copy of Ligasatu or EPL and have little players run around the field and kick balls, but at the same time, I think that you know that that wouldn't mean anything to you, right?
Yeah.
So, I think that...
I don't know how to describe that of, well, AI wouldn't work there, right?
But in terms of the live action and in terms of the artists or storytelling...
That you're telling, I think it really just depends upon the story that you're telling and I think that it is a tool that is useful in the process.
I know we've...
One of our recent original series, you know, had quite a few zombies and all of those things and currently, right now, it is easiest for them to film it with humans.
Yeah.
Well, zombies.
Right.
But we...
The team...
The team, now that they've gotten more involved in the Chloridopatso Mountain, gotten better at AI generation stuff, is they took a few parts of it that weren't even the human aspects, but they were just, for example, like blowing up a bridge or some effects, right?
And I don't think anyone can complain that, oh, that effects was replaced by...
Because it's all just what is the tool to make that happen, right?
And so, that's a tool that makes that storytelling cheaper, easier, more accessible, right?
And so...
Yeah.
I think that in the content space, what's going to drive success is going to be telling good stories that are meaningful to people that they want to follow and artists will be involved there.
I think more...
I don't know about more, but certainly equally important is the storyteller.
Yeah.
That'll have value for a long time.
Right.
I mean, speaking about all these effects, right?
Visual effects.
I mean, people have used a lot of tools and in some movies, in fact, even like those cheap, cheap visual effects is there, but people still watch, right?
I think what you mentioned, maybe it's kind of like true, right?
People don't want to see crap movies, even though with good visual effects, right?
So I think storytelling, maybe also the acting, the expression, all the props, the cinematic thing might still...
I saw an amazing example of use of AI recently.
It was around for advertising of sports artists...
Sorry.
Of athletes.
Yeah.
Yeah.
And who are busy many, many months out of the year and in them lending their faces and their likeness to a completely different person, but then using that for commercials and TV of that regard, which is interesting place to be in.
Yeah.
Yeah.
I'm sure people have seen all these scammers now can also replicate people's face, people expression easily.
We'll see some more.
I don't know.
I think it's a huge use case, I guess, in advertising, content generation and all that.
So speaking about video, I'm sure you must be proud that it is one of the biggest streaming platform, OTT platform in Southeast Asia.
Maybe to grow to that stage seems to be a lot of hard work, I assume.
What are some of the memorable challenges, maybe technical challenges, or maybe, I don't know, like industry content challenges that you remember that you can share with us?
We certainly have hit scale at...
Yeah.
At a few different points.
I think that scale in...
So in the OTT, in the over-the-top space, you have sort of AVOD advertising base and then you have SVOD.
And video overall has kind of shifted a bit more towards SVOD, it's a lot easier to make money when someone's paying for it.
But when we had very large live streams that were AVOD based and anyone can just log in and then watch those.
Yeah.
So we have had some very interesting scale situations.
And I think that a memorable one was like the Asian AFF, which is Asian Football something games.
And we've hit around the four terabytes per second in terms of bandwidth.
We've had about like 2.2 million concurrence.
Those aren't Indian hot star scales, but in Indonesia, it was a significant portion of people that are watching.
So hitting those scales, we have had to solve technical challenges.
And the technical challenges aren't necessarily the technical challenges of how do we build more scalable infrastructure.
Many of them are how do we deal with our users' devices and the internet access that they have, right?
So we've gone pretty far in terms of trying to use...
We've tried to use the most advanced codecs that we can to reduce the stream to make the highest quality stream that people can watch.
We also have at times looked at all of our consumers and the network that they're on and give people different streams depending upon the ISP they're on because we know that that ISP is going to be hitting sort of bandwidth constraints, right?
So I'm quite proud of our ability to scale to some of those large events.
And the engineering team has done a great job.
Yeah.
It's a great engineering effort we've put in to make the experience better for the user that isn't necessarily on do the servers work well and stuff, but it's more meeting that user at the technical maturity that they have on their side.
Yeah.
Yeah.
I haven't worked in the streaming companies, especially the live stream companies.
From my perspective, it must be very stressful whenever there's a live event, it could be live sports, entertainment, whatever that is.
With higher number of users, is it stressful for you?
How do you actually ensure a large scale event go seemingly smooth, less issues, or if there's any issue during the show, how do you actually ensure that you can recover really fast?
Well, I'd be lying if I were to say that it hasn't been stressful for me.
There certainly have been some stressful times.
But one thing that we do benefit quite a bit from, as I mentioned that sort of AVOD, SVOD space, right?
Is video does have a lot, we have a lot of free to air TV that people can watch.
So kind of like YouTube live, I believe in the States and so forth, is people can subscribe to video and basically watch the equivalent of a bunch of cable TV channels that are 24 hour running, right?
And so from that, we have that prime time boom and we have that standard sort of the daily sort of rise and fall of people going to bed and then waking up and wanting to check the news.
And, or watch kids stuff in the morning, they turn it on for their kid and then they're at work.
And then, you know, maybe during lunch break they're watching.
So video typically on an average day has about an 11 X dynamic range of, of people, you know, the prime time people watching and then, and then going down.
So that's where I feel like it's actually been quite a bit of our benefit in the sense that when we have had big premium sports things that people want to watch.
I'm already used to this dynamic range here, right?
So then, and, and to see your system as it scales from that 11 X, then it's a bit easier to understand, you know, how it's going to scale in on the other side of things.
And that's because on that 11 X, you're obviously going to be optimizing in terms of not over capacity and running things very efficiently, right?
So, so from that you can extrapolate a bit more in terms of how to run the bigger events at scale.
Yeah.
Yeah.
Yeah.
Yeah.
We've hit some, I remember once we had we needed to like Redis we were running some Redis and it was certain starting to hit sort of operations per second limits.
And we had a big incident where we already sort of went through the playbooks of, okay, you know, if we're, if we're running into capacity limits how we would scale up, you know, spawn another instance.
And, and run there and those capacity limits weren't tested at peak load of spinning up another Redis.
That was definitely an issue that we've run into.
Right.
Like one thing that I would imagine because all this, like not all, I mean like some of these streaming platforms are now going to the live streaming thing.
I remember like Netflix is doing maybe the first big live stream they have like Mike Tyson against, you know, Jake Fury, Tyler Fury, I forgot his name.
Yeah.
But.
Jack Paul.
Jack Paul.
Sorry.
Yeah.
It's like the number keeps increasing, you know, as the, you know, rounds go by.
Right.
How do you actually plan for such event that you didn't even anticipate before?
Yeah.
I think that, you know, I certainly watched Netflix from the tech side of things as, as that live stream, that was Jake Paul versus Tyson was their largest single day signups ever.
And I think they had around 60 million concurrent.
Yeah.
And that's from a podcast of the CTO of Netflix.
Right.
And so they had those 60 million concurrent and, and we have never hit that scale.
So that's fine.
But what, but for, you know, video scale, I think that that, that, that dynamic range, that load testing that we do, that we get to do naturally from that rise and fall has benefited us quite a bit.
Well, I mean, it's mainly the fact that we have that dynamic load.
That comes every, all the time.
And I mean, we will have, I remember before video, actually, when I was working at EmTech, we had when it was just the publishing platforms, some celebrity, some art, you know, popular celebrity died and, and we saw more traffic in that one day than people wanting to read the articles and all of that.
So you can't plan those deaths.
But, but, but, but you can.
Optimize a system at, you know, it's, it's peak in those loads and yeah, optimize around that.
Yeah.
Where do you see the industries moving towards?
Like maybe if you, because you have seen the last few years and maybe a little bit of glimpse of what is coming, you know, with, you know, maybe using AI more, where do you see the industries going?
Actually, maybe I can circle back on that dynamic one a bit.
Oh, okay.
Um, so one benefit that video gets from being, we're, we're mostly now.
But we still have users who can come in and just watch sort of like TV on video.
And so we have that Avon aspect, which is very lightweight in terms of people can just sign in, sign up and, and watch without even subscribing.
Right.
And so we, we get that.
It's easier.
We get that free consumer that, and there's obviously then a lot more scale there, right?
Which is why hotstar has massive scale.
They do have tons of subscribers.
Yeah.
Yeah.
Yeah.
Yeah.
They also have a, the majority of the revenue is coming from advertising base, right?
And I think a big differentiation between us and Netflix in that regard is to Netflix.
If you are to play a single video on Netflix, you need to log in, subscribe, right?
And so when they have that walled garden of a credit card or whatever payment process, then that really prevents them from having to optimize for that advertising base that is just so much higher throughput.
Right.
Yeah.
Yeah.
Yeah.
And, and over time, again, I've, uh, and I, I've never that I was in a lot of risk and, and connected with Netflix before.
So it, and I also know that Netflix, uh, just hasn't covered from back then, right?
I met them from back then, but now I've been in the Netflix space, the small OTT space.
I've seen sort of, I've seen organizations rise and fall and, and the, I remember I was on the stage in Bali once at an event talking at, um, a advertising event.
It was called AposTech and it's, um, it's, it's quite a fancy event.
And I was on stage with, uh, Hook and View and iFlix, and then Video and half of those companies are dead now.
was a joint collaboration between Singtel and other partners in the area.
I think maybe even Sony.
And then you had iFlix, which was invested and expanded in SVOD throughout the area.
And I think it's very difficult for those companies that start out SVOD and start out where every user is going to be paying this much.
And so as long as the user is going to be paying, let's say, a dollar a month, and so that as long as our costs are, let's say, $0.50 per user or whatever, then they can optimize.
But what that does is that means that the business, and they're going to try and grow quickly, is they're going to use vendors and they're going to use a lot of third-party services.
And they want to keep quality high, so they're going to use a lot of third-party services to get the service, the highest quality out there as soon as possible.
But then what happens if you were to scale into just start adding advertising as a mechanism for people to consume your service?
Then you went from $1 a month down to $0.02.
And now as you scale massively, there's a funny phrase, it's what we lose on margin we make up in volume.
And it's funny because it doesn't make any sense.
And it just means that you are going to be massively hemorrhaging money.
And of those on the stage that hook iFlix, view, and video, view and video both started out on the advertising space.
And then we've moved into the subscription model.
And the hook and the iFlix, and even Netflix, has introduced the advertising space.
And I think Netflix, well, hook and iFlix have died.
And I think Netflix will be fine.
Obviously, they're a pioneer in the space and they're such an incumbent.
But in this market and in certain parts of the world, the ability to subscribe and pay is reduced.
And that's why Netflix has added that advertising space, to expand their business.
So I think that having that high efficiency and being very critical of every dollar that you're spending on a third party is incredibly important to ensure the success of your company.
Now, I think we're going to also see the same thing in the whole AI space.
You have, granted, Claude or Anthropic and OpenAI, I think that both of those are the Netflix incumbents.
So I think that, I mean, it's interesting to see that, you know, but even, who's the founder of OpenAI?
Sam Altman.
Sam Altman, you know, talking about his sort of, him firing back at Anthropic of, oh, we have more customers in Texas than Anthropic has customers.
Right.
Which is, it does show that scale is incredibly important.
And because they're more advertising based and they're going to start adding, you know, more ads.
And, um, I think that Anthropic being subscription only based, again, you know, I think that right now what's important in the AI is the quality and being able to subscribe.
But I think that when we start talking about companies that are built on AI and are bringing AI to a very niche product market and stuff, I think that we will see some of them, you know, there are some jokes around, you know, like tell me you have an AI company and then show me your OpenAI bill, you know, is it?
Depends on the level at which, I think that's, it can be great to, to jumpstart your company and to solve the problem in that space.
But I think that you're not a tech company unless you're thinking about every time where you're employing AI there and okay, maybe that could be not even an individual model.
Maybe that could just, that piece of the system could be some rule set.
You know what I mean?
That costs, you know, literally, you know, nothing compared to tokens.
Yeah.
Uh, I think that people need to, what's that?
Yeah.
Yeah.
I think that, you know, there's a lot of, uh, NPM module that's like using AI to, to check whether something's, uh, uh, is odd, you know, but you know, the upstream cases where using AI in areas where it is inefficient.
Yeah.
And so there will be so many places where companies are able to bring huge LLMs and into certain domains in evoke efficiency and help improve people's lives, but they won't survive.
Forever.
If they're beholden entirely to a third party.
Yeah.
Certainly when people refer to maybe AI bubble, you know, when you see a lot of AI products out there, right.
But if all they do is just like wrapping on top of, you know, whatever AI model that they use, probably that's a bit of trouble.
Maybe in the future, if this model changes in terms of capability, the price is the raise.
Yeah.
Yeah.
So definitely a good, a good, uh, advice for people who are building AI products these days.
One thing that, you know, one area where video is beholden to is, I mean, 50% of our costs are network delivery, right?
So I mean, it, that is a huge aspect and, and initially we used one CDN and, and optimized on that.
And I think that it was painful to go multi CDN, um, but it unlocked the ability where I wasn't beholden to a particular vendor and I was able to say, well, I can just move this over tomorrow.
Yeah.
And, and so I think maybe in the AI space, we need to think about that is if you do use, you know, AI and you're not running or building your own LLMs and things like that is maybe being vendor agnostic certainly unlocks the skill of being able to negotiate between vendors.
I think you have mentioned a couple of potential trends coming.
Like for example, I liked the discussion about the subscription versus advertising, um, creating more AI generated content or AI being used in general.
I think content in software development development as well.
Is there any other trends that you pick up that probably you can share for us here?
Given the current date and time right now, which is what we're, we're mid February in 2026.
Yeah.
I think that it is impossible to avoid the recent trend of the, uh, the open claw and the whole agentic.
Yeah.
That's the tie that you have there.
Yeah.
Yeah.
Yeah.
That's the tie that I've had for a very long period of time, but it is identical to a lot of open call logos.
Right.
And, and on my desk you can see this Mac mini and I only have a stack of one, unlike the people who are trending on X who have, you know, 15 Mac minis and all of that.
So I mean, it's, it's a lot of fun to play with and it's an interesting and exciting area to be in with regards to that agentic letting, you know, some agent run along and burn some tokens until, but I think that the trend needs to, you know, be focused.
People need to be cognizant of the Gartner hype cycle and, um, and it's a great time to get experience and to play with all of the things that are developing in AI.
Um, and I think that it is foolish to ignore them and to, and to not, you know, um, experience and see where their breaking points and their limits are.
Right.
So I, I have certainly, and, and when you play with these things and when you technically have an understanding of how they operate, right, and, um, you know, nothing is magic in tech.
And, and I think that once you're, you get a deep enough understanding of, okay, this, this agentic, the whole open claw thing, right, is essentially, I mentioned earlier how, how, you know, we had the copilot.
Yeah.
And then we had Klein that was feeding back into itself and getting better, right.
Getting better based on Markdown files, the limit is not sentience, but Markdown files.
Right.
That's essentially all this open clause, right.
Is Markdown files and potentially vector, you know, embeddings and search and stuff like that.
But it's, it's not sentience and, um, and, and at least understanding how it, the, the underlying components of it.
Yeah.
And so I think it helps you leverage that for what it's good at.
It's good at, you know, uh, I have my own open claw and, and the first things that my friends want to do is, you know, book some time.
Tommy needs to be here at this time with me or whatever and to remind me.
And it can be very useful for, for getting different perspectives on certain things.
But if you're gonna prompt it to go online and make money or something, then I think that, I think that you wouldn't have, yeah.
a good enough understanding of of this is not magic that you're talking to it's a bunch of markdown files right yeah i like it that you pick up this uh open claw some people know about cloud board mode board mode book and all that but this open claw is definitely one trend that is happening in the tech world and in fact the founder was just hired by open ai maybe we will see new evolution of this open claw and yeah definitely interesting for everyone to just understand how this evolved the ai pace is definitely very fast but at the same time i think we have to i mean for people who are in tech we need to understand where it's going the limits although seems like the limits keep increasing day by day but if you have a deep understanding of it then then you know if it is if ai is magic to you then it makes sense to potentially ask you know like go make money online or go cure cancer yeah but like when you understand that it's just predicting the next word in a very oversimplified manner right right then you can have a better expectation of what is possible what isn't possible even if this word prediction gets better by you know several years right what is still possible yeah speaking of that i remember one quote that i have with with my past guests like ai is smart until it's dumb like one day you'll find it dumb it's like oh it's not magic after all yeah right so tommy i think we have covered a lot of things unfortunately we have to wrap up pretty soon um in my podcast we have a lot of things that we want to talk about but i'm going to wrap it up and talk about some of the things that we want to talk about and talk about and talk about some of the things that we want to talk about and talk about and talk about i have a tradition to ask all my guests you know this question called the three technical leadership wisdom think of it just like advice that you want to give maybe for people engineers tech practitioners out there maybe you can leave your version today that will be great yeah so i'm a huge fan of malcolm gladwell and i've read all of his books i love his books and um and in one of his i think it's the tipping point where he talks about in a particular area, you know, the 10,000 hours, right.
And, and how Steve Jobs and Bill Gates, you know, at that time when they were, they happened to be really at the right place at the right time in terms of having access early on to technology.
And then, and then obviously they took it upon themselves to play with it and learn and love and, you know, just, just absolutely pour time into, and then get those 10,000 hours.
I think that mastery of, you know, any skill takes, takes time.
And I think that, uh, as, as people who embrace, you know, our new LLM overlords, and, but people who have successes and failures and, and, and leverage these tools and, and then see what works and then build on that.
I think that that is as true as it ever was.
And, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and so these tools are something that, you know, shouldn't necessarily be feared, but, but, um, people need to be respectful of the Gartner hype cycle of, of not getting too overzealous and keep focusing on the outcome of, is it, is it helping you get done what you want to, what do you want to do?
And double-click on that and double-click on solving real people problems as opposed to optimizing something that, you know, or creating something interesting.
And Ada, that's it.
Thank you so much for sharing that.
I believe mastery will, the definition of mastery will change definitely with AI, right?
Because there are so many things that AI, and in fact, for example, if, if I want to get into a new thing, new domain, you know, new understanding about something, it's very easy to pick up from AI, but definitely mastery here is, doesn't mean that I know more things now because of AI, right?
Mastery is something that you have done, experienced, and you know, you know, you know, you know, you know, you know, you know, you know, experiment, fail, I think it's a big part of that mastery as well.
So I think definitely the future is going to be different.
So yeah, Tommy, thank you so much for your time today.
I think learn a lot about, you know, your practices, building culture, which I think is quite admirable.
You know, video is, I think, one of the big tech products within this region.
Wish you all the success out there.
And yeah, thanks for sharing everything that you have today.
Thank you for having me.
It's, it's been very, a learning experience for me, as well, chatting with you.