MDM Podcast, Season 7, Episode 11: Understanding Unity's Vector (with Felix The)

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The problem is that the distinction needs to be drawn between the competence of the economists and the correctness of their analysis.
Welcome to the Mobile Dev Memo podcast. I'm your host, Eric Seuford, and I'm joined today by Felix Tay. Felix,

welcome to the podcast.
Thank you, Eric. Thank you for having me.
I'm very glad to have you here to discuss all things Vector, all things Unity Ads. I'm really excited for this conversation. I've been looking forward to it.
Yeah, me too.
Before we dig into the meat of the conversation, could you please introduce yourself to the audience?
Sure. Felix say I've been at Unity for 9 years, so I've seen the the different stages of Unity, the ups and downs. I'm excited to be here. I have a background in both I guess optimization sciences if you want to call it that and game development from my undergrad. So Unity in a way is kind of the perfect combination of the two. I am passionate about video games. I am passionate about our creators. I am passionate about finding ways for them to be able to monetize their creation and help them getting discovered. So I think Unity plays an important role in the ecosystem and I'm happy to be a part of it. And maybe just for some context, so so we've we've known each other for quite a while. So my wife works at Unity. She's been at Unity for over a decade. But we met the first time we met in person was

almost actually I think it's probably like over seven years ago now because we were doing the the class that people do in San Francisco when you're about to have a baby. It was like the CPR class, right? It was taught by the firemen and that's the first time we ever met. I think we both lived in the same kind of area, but that's the first time we met in person. both both of our kits they're one week apart I think.
Yeah. Right. Yeah. And also full disclosure so I mean this is public but I am an adviser to Unity. I'm advising on a lot of the AI initiatives that Unity is undertaking and those are sort of diverse and wide ranging. Today I think we're only going to really be talking about the ad network initiatives but we also kind of interact in that capacity.
Yes.
All right. So I always like to start these conversations off with like a big kind of broad conceptual question since we're talking about Unity today. talking about Unity ads. I'm going to start off with Vector. So, what is Vector? How does it work? And what is its purpose?
Vector is Unity's AI powered growth and

user acquisition platform. It is the fastest growing part of our business. It represents a full rebuilt of our machine learning and data infrastructure that powers our advertising platform. At a high level, factor connects gameplay behavior, monetization signals, campaign performance into one continuous learning model. What really sets it apart is the architecture factor specifically designed to ingest and interpret very large volumes of gameplay related data. The model we use is also very centered around games and application discovery. It makes a natural fit with Unity and our engine business because we understand how games are built and how players actually behave inside them. By pairing the gameplay insight with our ad network at scale, Fector can learn faster, make better predictions, translates into stronger results for developers and advertisers. Since launch, factor has grown rapidly. Revenue was in January, as you heard in the earnings, 72% up year-over-year. It's been growing quarterly,

sequentially, quarter over quarter. We believe we're still in the early adoption curve of growth. Now our focus is to work on the core engine driving performance across the grow business by making sure that all the constituencies data elements that we are partnering with the developers to use can power factor in ads and also beyond that's factor we invest in vector because we understand games we understand games not from the perspective of just the machine learning elements and the AI elements but also the way games are built and the way games are played. And I mean this has been like an ongoing initiative for some time, right? Do you have like a was there like a I don't know like a start date or something like a like a birth date like when when did this initiative take shape?
The most big bang moment would be somewhere between Q2 and Q3 2025 I guess because that's when the industry actually saw the impact from last year. But in R&D typically you don't really have a single release right like in in AI and machine learning for ads like there's elements of the model that you need to update sequentially. there's the

data architecture that you need to update sequentially. So that work was ongoing but we just saw the majority of the benefit happened in this big moment around Q2 Q3 of last year. So that's when we went public with a message that this is the impact of factor.
That's a really good point about the like the R&D process. Maybe maybe we could just kind of hover there for a minute. So so like talk to me about that because I think particularly with respect to ads, right? I mean like so I was on this crusade for a long time against the use of the phrase AI in this context because my argument was like this is kind of this is ML it's not AI you know but I gave in because it's more exciting it's more exciting to just say AI but nonetheless right so like that that's become like the big topic of discussion you know across the industry right now across the entire landscape is like these applications of AI to advertising and with Facebook I mean meta they talk about gem and Andromeda and you know kind of Every time they do, they'll say something in earnings, right? And then you'll get kind of like these LinkedIn

influencer posts about like, oh, the new Meta tool or the new Meta AI algorithm that is changing advertising wholesale and then they'll kind of just regurgitate what someone said about Andromeda, right? But the reality is Andromeda has been in place for years and they make sequential updates to it, right? This is a research project. It doesn't happen as you said in one big release or something like here is Andromeda thrust onto the world. It's introduced. It's improved. It's tweaked. It's tuned. Talk to me about that process like just the the constant improvement and iteration process in these kind of systems like vector.
There's two. One is you can basically tune based on the scientific advancement like there's of course a better model. There's a better way to basically develop a certain protocols, parameters or adjacencies to the model. Like for example, do you want to basically tune the model based on a certain outcome? Do you want to tune the model based on actually understanding multiple outcomes

in a multi head? There's a pros and cons. Those are like just modeling techniques. Modeling techniques basically improve over time. That's one path. The second path typically when it comes to the iteration is like you don't have to limit yourself on model development based on just model development alone. based on the availability of data that you have and the way that data is represented and how complete it is and the nature of the sources of the data it opens up avenues of outcomes for modeling. So in a way think about model as a two track right like the modeling team can basically think about improving the model independently and the second thing is like you want your modeling team to actually understand the nature of what's possible based on data and based on what's possible based on data the paper you use as the base the technique you employ and even the approach itself will become fundamentally different. So those are the two things that tend to be iterative in nature. One is the modeling itself but secondly data changes and with data changing modeling also change
right and then within that sort of that data you know sphere you've got a number of different opportunities available to

you with like data augmentation thinking about like noising the data like purposely noising the data to add more diversity to it or to train the model to pick up on what's noise and what's actual signal and then you know on the modeling side I mean you could go down to like you know just changing the optimizer with some different hyperparameter or whatever but like this is just just to make the point that like you're tweaking things, you're you're making adjustments. Some of these adjustments could be big broad adjustments like you said a multi head architecture or they might be just like we're changing the you know the momentum in an atom implementation and and that might actually have you know meaningful effects right but but again it's this is like a regular ongoing process you've got researchers focused on this doing this iteratively and then they get shipped and it's not just like okay v2 is out and that it was the you know the first time we've sort of deployed this you know this very dramatic change.
That's right. That's right. In a way like the factor that we released from 2025 is not the factor that people experience today. We didn't really say like it's factor 2. But in reality because we have researchers and scientists that actually actively

working behind the scenes. Every time there's a better way to do something either through modeling or data with modeling we release it and we just let the customers experience the benefit. That LTV model you're using to optimize your campaigns even the most accurate one in the world won't guarantee better performance. Google and Meta run on signals. Developing and deploying them at the right time in the right format for every platform. That's its own discipline. One that has to learn alongside the auction, not behind it. Openor, SEM Rush, Miro, they all work with Voyantis for exactly this reason. Because the model was never the hard part. It's everything that comes after. Signal engineering, testing, maintenance. If you're ready to close that gap, visit voyantis.ai. That's v o y a n t i s.ai.
So talk to me about using the engine data for ads targeting. How does that data contribute to these better outcomes? And maybe kind of talk me

through like what that data might look like.
Yeah, I'll talk about how that data will look like as a second topic. The first topic is Unity sits in a unique intersection of creation and growth. Unlike most companies that squarely represent their service in the growth area of the business, we have a lot of developers especially in mobile who make their games with Unity. More than 70% of the top 10,000 or engine has the coverage of that developer segment and there's a portion of that developer. They're not really using services around user acquisition at monetization or services around in a similar category. What the engine data will allow us to do with the right developer framework and consent from developer is these are gameplay data that we could actually use to benefit them when they're ready to actually engage in that type of services. This is also the data that they will get in return of getting products like diagnostics and all these things. What this means for Unity now though is that is the footprint of data

that I think Unity potentially would have access to that typically you don't get in many other sources out there. So that's exciting. The second part is our runtime data from a data quality perspective. We are the it right like the game boots that's the Unity runtime the first thing that got instantiated. So our runtime data technically doesn't have to worry about quality issues that you know SDK get initialized late sometimes they get skipped that the coverage might be you know missing certain things. We don't have to also worry about joining data from like disparate sources of third party in which like it only takes one of the third party sources to actually not do their job in a high quality for the whole thing to not create that much of a meaningful value. That's the nature of data, right? Like it has to all be excellent across the board. So instead of pulling from a patchwork of sources or manually export data sets that have been stitched together, we get to get access to the cleanest quality data

straight from the runtime and the boot time when applications got loaded. That in my mind is the power of the Unity runtime. So one coverage two quality of data signal in terms of the data itself like yeah we we probably have quite a bit of elements of data that you can't find outside but I think sometimes focusing only on that detracts from the primary benefit of runtime data which is the first two that I mentioned
and a point that you had made to me that I hadn't really considered but I think is is I mean is valid in retrospect is is like a lot of this stuff is that having a time series is really important And knowing the fidelity of the time series is also really important. And when you're dealing with like SDK data, I mean, you don't actually know if like event A happened before event well A minus one. And you might just be getting it in the wrong order. But, you know, when you've got the runtime, you know with sort of certainty that, you know, these events happened in this sequence and then when you're doing sequential model, I mean, that's that's it's

imperative if you're doing any sort of like sequential modeling that you you believe that the sequence is is real, right?
Yeah. Causality is important. If causality is broken then like you said advanced modeling techniques like understanding sequence of events become moot. Also the lack of a causality sometimes you you don't think correlation is causation that's also dangerous. So the idea of having a clean data with a right sequence of events being represented correctly in the same order without joins is really powerful.
Right. The only way to to truly know that the sequence is correct is to have access to the runtime.
Right?
Because with an SD I mean with an SDK you're always dealing with just transmission, right? So there's any number of things that could cause that to be out of order,
right? And SDK needs to be initialized. That's the that's the thing. So
and I mean, you know, the other issue is developers have to know what to send to you for you to help them, right? I mean, that's not always clear to a developer. I mean, what what should I instrument, right? Is everything, you know, valuable? And no one may know. It may take that experimentation process that we just talked about to to unearth

something that's actually really valuable. This was the whole this was the whole process that took shape with a lot of developers and I worked on like I don't know a number of these projects when SK ad network was first launched it's like okay well now we have to really be thoughtful about the conversion values do we know what's valuable and a lot of developers had no idea I mean once you went through that process of actually trying to like adjudicate the value of these conversion values of these events that you would then encode into conversion values it became clear that like a lot of developers prior to that had never really thought about it and they were sending things that they just assumed were valuable when in fact they weren't. And and actually going back to the sequence idea, encoding a single event wasn't really that meaningful. What you really needed to do was encode an event onto a sequence. When the sequence happened, that was the event. And that's what unlocked a lot of usefulness in the conversion value.
Right. And to build to what you said, not just the manual work by developers. I mean, like people can interpret things differently. Some people do it correctly, some people do it wrong. But the lack of standardization across

events makes it really hard for AI products to use. So I think standardization and consistency with a manual intervention that can cause error is what I would say one of the benefit of runtime data because it's high quality.
Well, and I mean just the volume, right? Because I mean if you think about like if I'm a a game developer and I've got a game
Yeah.
I've got one bank of data, right, for you know and I I don't see that much. I see my game and you know we know that conversion events are rare. People buying stuff in a mobile game is rare, right? When I wrote fremium economics, the first chapter I talked about this 5% rule. And it's like, okay, as a rough heristic, a rough rule of thumb, you can expect 5% of your users in a premium product to monetize. And that was me bucketing all types of, you know, premium apps in or products into that into that premium category. But the reality is with mobile gaming, I mean, I would say now that number given, you know, some categories could be sub 1%, right? So, you're talking about like a very tiny minority of users actually

doing the thing that you care about or, you know, doing one of the things that you care about, which is is making some sort of purchase or contributing revenue in some way. That's obviously that's changed a lot because the ads are a more material portion of of mobile gaming revenue than they were when I wrote that. So those users contribute revenue. But but nonetheless, you've got this broad stratification of of revenue profiles, right? Like if you think about LTV, it's much more stratified, I think, now than it was 2014. And and that matters because that longtail is where all the value is.
Yeah. And you're right about that. Sure. Let's say like ads become more relevant. There's a bit more diversification. But modeling is also about like that longtail. It's about the signal versus noise. It's the power of differentiation. And sometimes the part of differentiation is less about like understanding like look CPM varies but you know CPM is like high low CPM they're good number is a very different thing than a whale purchasing $10,000 in a game. So that signal differentiation coming from transactions still matters a lot in our ecosystem,

right? And and anybody that's ever worked on classification with extreme class imbalance knows like if you've got 1% of users purchasing making APS, right? If your classifier just says everyone's not going to make an AP, it's correct 99% of the time, right? I mean, so like it looks really good if you said, "Hey, my classifier's got a 99% correctness rate." You say, "Wow, that's pretty impressive." Except it's wrong for the group of users we care about.
Correct. Correct. Correct.
All right. So why now though? Why I mean so Unity's you know when was Unity found Unity is what 15 years old? It's
something like that.
Unity ads was amplifier acquisition that was I remember when that happened you see lock and then was brought in was it 2012 or something?
Yeah sounds about right.
So Unity has had an ad product for quite some time. What why now? What was the impetus for for vector? Prior to Vector, the team did work to improve our system. Typically, when you are in that crossroad, when you already had, say, a working deep

learning model in the past, it's always a big decision when you have a viable business that is running, but then you're wondering, is this the system you want to replace? Essentially, you're flying a plane when you're building a new one at the same time and you need to find a a way and chart a course for a smooth cut over. It's not simple and any R&D rebuild comes with a real risk. So that risk when we evaluated we got really comfortable in taking when we saw a few things line up. new modeling techniques, new information that is available for us, new machine learning frameworks that we just so happen to use in vector that we didn't use in the past, next gen inference tech that simply didn't exist when the original system was built. Combine that together with our goal that we believe this is the pivotal moment where we can actually build the best user acquisition solution for games, it became clear that rebuilding was the right course of action. So that's what

we did and uh needless to say we're pretty pleased with the decision we took and we're pretty pleased with the outcome.
Talk to me about some of those like new techniques. I had met as VP of of AI ad products on and I was asking like pretty pointed questions. I think they like weren't happy about that but but so I'm not going to ask you for any like secret sauce. Let's be I mean to be fair to him Matt Steiner he was pretty open. But just talk to me about some of these new techniques. I mean like what's been like an unlock?
Generally speaking I believe in big models not small. So I think what it means by big models not small is like you could create models where they can be like an army of smaller models trying to do different things or you could you could combine them that they can actually understand what each other needs to understand. You know autocorrelation is a thing, coariance is a thing, same training pipeline is a thing, understanding causality of decisions that are made by another model that influence another model is a thing. So I feel like the future is about big models. That's one. The second part is

even the data platform world and inference world has changed quite a lot. So something that we could not imagine in the past could be something that we imagine now. On the data side, I think as we talked about having a full sequence of data about a a player will unlock new modeling techniques. You mentioned about sequential modeling. That's certainly one area because I believe like the the future of understanding a player is not about just understanding like at a certain point what they want to do, but to actually be able to predict what's next. That is so powerful because then you can actually think about the application of what you could do into multiple uses. And then let's see third big model should not also scare us like I guess in the past like big model by itself well one without the utility of data what is the purpose of a big model but we surpass that but inference techniques there's a lot new papers there's a lot new techniques being done so that even in a big model for a company like us we can actually do it in practice because we can manage the the cost of a big

model like you could optimize inference so I believe like the future of the space is about big models, how to optimize your cost on the big model and not be scared that there's going to be new data sources that you can use to actually make your model even bigger and bigger and bigger.
So, we talked about the kind of tuning process like what what's the kind of I don't know call it like the midterm road map look like? What's being developed? What's being deployed? How do you expect Vector to evolve in the kind of midterm?
Generally speaking, like outside of Vector, we've talked about this in in our earnings too. We're very keen on starting to utilize the runtime data. So a lot of the factor performance that we've celebrated to date is without that we're pretty confident based on what we are seeing so far the early signals that this is something that we can utilize and that would also unlock new modeling techniques for us to think about. So that's one on runtime data. The second part is even outside of advertising you could see like how AI has shaped the world of development. AI has shaped the world on how people interact with products. We just don't think like the

current classic way for people to interact with a the product will matter that much in the long run. What matters is interacting in a product where you can express intent of what you want to do. Like whether it's a marketing campaign, whether it's like the money you want to make from ads or IP. A lot of this hinges upon our ability to actually embrace AI beyond just the predictive ML that we offer at the core. So we believe like the new workflow for Unity ads will be centered around like embracing the notion of AI that can actually influence your campaign performance. Those are top of mind for us. And then the the third is what I mentioned earlier about big models.
What's kind of nice about the mobile gaming context is like you know you have probably like a clearer and you know correct me if I'm wrong too but you probably have like a clearer picture of like what you want right? Right? So, if I'm thinking about like a DTOC advertiser, I could be setting some number of objectives, right? And they could sit across different points in the sort of funnel.
But for mobile games, I mean, you want the user to install and then you want

them to to enjoy the game and to be engaged, right? I mean, that's ultimately what you want. I mean, it's pretty clear, right? You're probably less
like to the point where you don't even really know how to price it from the advertisers perspective. probably like much less interested in just somebody going to the App Store page, which you have no visibility really into anyway because once they click out, you don't know what happens. You don't know what they're doing on the App Store page. I mean, that's that's just the way the App Store works and Google Play works, right? You don't have any visibility there. You know those channels your colleagues keep bragging about, the ones getting all the credit? Yeah, they might be doing squat. Attribution makes every channel look like a hero, even when it's a zero. Incremental tells you who's actually doing the work. It's like a lie detector for your marketing budget. Start using Incremental today. Get your demo at incremental.com. That's ncrmn.com. Mention that you came through the mobile dev memo podcast for a special 15% discount for the first 6 months.

Talk to me about how AI is impacting creative strategy for gaming advertisers because I'm seeing a lot of really really fascinating stuff happening. I imagine you're seeing more of that fascinating stuff. just just talk to me about like the kind of new creative production workflows that you're seeing and and the the new types of creative that they're yielding.
It's very liberating for advertisers to be able to generate creatives very quickly and a lot of them look really good. I think AI reshapes the creative strategy for gaming advertisers, but it can also make things messy, you know, in a lot of ways. If we basically just embrace Gen AI for creative generation by itself, just that it basically shifts the cost. I think you talked about this in one of your article, it it really shifts the cost of creative production to exploration really like because when you're trying to do performance advertising and when you're seeing something new for the first time, you don't know what that thing does. So you got to have the algorithm basically check it out and see is it good? Is it like performing? Yada yada yada. So

technically speaking, in this new era where AI is impacting creative strategy, we don't want to be in a scenario where we spend less time making assets, but we spend more cost in marketing them. You want to have both. You want to actually get productivity without the added cost of marketing. So we think the right kind of AIdriven creative is something that is not just built with a visual understanding about what is acceptable and what's nice but also built with performance advertising in mind. Especially in the realm of ad network, right? It's not enough for an ad to just look nice. It needs to be tuned and understands about like what is the potential conversion of this thing because it's an ad. It's not like a interactive experience for you to just take a look at. You want people to take action against it. You want people to install. We want people to click. So the core of our strategy when it comes to understanding AI and gaming advertiser is we don't want to get ourselves away from the root like we understand performance advertising. So when it comes to Gen AI, it has to be built with

performance advertising in mind. I guess another way to answer the question directly is Genai that we hope will work in the factor network will be the one that is not just visually stunning but context aware about what people like. What's the most innovative stuff that you're seeing coming out of advertisers in terms of their creative? Anything I mean anything that is sort of like unexpected or new conceptually? Anything that is driving performance in particular that wasn't being done before that's been sort of made possible by JAR or is it just higher volume?
I think for me like the way I think about most innovative is really the same with my answer to the previous question which is like what's the one that is most impactful. So rather than the outlook, the visual outlook about what the creative is, it's more about combining the two understanding of the systems on how creative is made and how it creates conversion. So I feel like the best way that this industry can basically embrace

innovation in the creative space is to make uh creative that really understand a smaller and smaller cohort of users. Imagine this today a creative today like let's say you you put in this campaign is targeting everybody globally that's pretty broad let's say you do localization then you basically target people by region or by local language that narrows it down but I think the holy grail of creative is like creating a combination of creative and the backend understanding of that creative into a smaller and smaller cohort where one day you could narrow it down to a very small cohort and it's truly personalized because then people will convert. It's hard for me to describe whether there's something out there that actually has established this because I don't really know what's going on in the back end, right? But I can tell you like from our side, I believe that's what the future of creative will look like.
Yeah, I agree. And I mean, have talked about this a lot about how like these tools like they're they're tools, right? And they're production layer tools and so you could use them to

materially grow your output cheaply and that's great. But you could also do that in a very undirected way that just shifts the cost from what was previously in production being a barrier to creating that many assets to now being just in testing because this was undirected. It's just output for the sake of output in a lot of cases. And I think that's a mistake that actually doesn't, you know, it probably just nets out to the same outcome. If if you're 10xing output, but your testing budget becomes unsustainably large to where you can't test that output because you're constrained, then you haven't really gained anything. And I I do think that a lot of advertisers are finding themselves in that trap. It's it's like, well, we've just got this machine gun now and we're I don't really want to extend like the gun metaphor, but like we're not being very strategic with how we deploy it. And
I understand your point though, meaning like just spread and then hopefully one of them will land. Right. Right. The thing that we're doing deliberately, I don't want to comment what other people do, but the thing that we're trying to

do deliberately is we don't want to bring our advertiser customer in a position where we have a gen AI tools uh that they can create a bunch of creative for us to just benefit because there's a lot more money trying to do marketing with us. We are very conscious because our relationship with the developers are not just growing their game but like a lot of these their games are made with Unity. We want them to genuinely be successful. So what we want to do if we are to launch a product on genai is not something that transfer their cost of creative to the cost of marketing. We want them to actually be able to spend and achieve success. So for us like the KPI for a good creative genai is better OS. That simple.
Yeah. Yeah, I mean I just I see a lot of people diving head first into the Gen AI tools and they're not like it just they maybe have convinced themselves that this is very productive because just look at the number of assets we're creating. How could that not be an increase in productivity but actually just shift in the cost elsewhere and you're probably not better off.
Yeah, I agree with that.
What emerging trends are you seeing

across game design and early soft launch testing across the mobile gaming space? Soft launch in particular I think you know it became a lot harder post AT just because of the lack of the transparency. How are you seeing people approach soft launches now? Have you seen kind of anything that from a design perspective that kind of like unlocks you know the potential for spending on UA
in mobile gaming development and gaming development in general. The thing that will make a lot of impact positively for the industry is minimizing your pivot and your change at the 11th hour. The issue sometimes why that cannot be avoided. It's like you know what they say about how can you model fun? How can you design fun? Game industry is a hit-driven business, right? You never know until you try. So that's true to a certain extent. But I think there's a creative way to minimize the risk. So one way that some developers have started to minimize that risk is to not actually follow the traditional soft launch pivot spend you know tweak global

launch spend marketing see if it works. They try to minimize the cost of development by porting some existing title they have reskin it change the game mechanics by a bit but like literally you have a vertical slice of the game but not the full game and then try to launch that as a playable unit in which they don't actually measure for install I mean there's nothing to install really at this point but measure engagement when you measure engagement that basically tells you a lot about marketability viability of the game concept and when you see something strong that actually is the signal that they use to say you know what this is a great idea let's double down let's make the full game out of this or when you see like the engagement score is pretty weak even with a game loop being represented correctly it is the right time to pivot and when it is that time when you pivot the cost of pivoting is very low so essentially they've completely flipped the traditional model of expensive 11-hour change after a failed soft launch they tried to

basically get that signal early as they're still iterating and developing the game. I think that's a really healthy shift for the industry. I think like, you know, if more and more people adopt this, I could see a world where there's less R&D cost that is spent too much down a rabbit hole that we don't see any viability of success and more people failing fast and making successful games.
Yeah, I think that's that's been a fascinating development to watch. So, this idea that you're describing, it's just like, okay, we're going to take this and we're going to package it into a playable. that concept just accelerated, you know, significantly and it's like
accelerate and instead of stipulating about like this is what the game will do out in the wild, you get that data of what the game does out in the wild.
Right. Right. And then and then just determining, okay, is this worth developing or not? Right. And then you're getting that early feedback and just making that decision. And then and then a lot of the the calculus comes down to how well can we proxy the data that we get from this to commercial data, right? So like so and that was always the issue with soft launches. It

was like well okay we've got day seven retention or whatever and you know we see some acquisition costs. How well can we proxy that out to like if we're spending a million a month or 2 million a month or 5 million a month whatever that becomes the challenge of game launches which is like you know an analytical exercise but it just allows you to to do that and collect actual real data much faster.
Yeah. And when you soft launch the game is complete
right?
That's the issue. Say soft launch not looking very good. Some people do some alteration. Some alteration are possible. Let's say your economy is off balance like you know the the IP is too expensive or the free gems are way too cheap that people don't need to buy, right? There's mechanics that you can actually tune to find balance the game. That's possible. But what if the core loop itself like people don't find you know at that point that's the game is done. So it's hard to undo done. The idea of this iterative playable testing is the game is not done and you get the feedback from the people that matter most instead of like people saying like this is fun is this is what fun is like the best judge of fun is the people that are going to play your game so you get

that signal
right I mean to your point I've made this point a bunch of times but like the MVP for a soft launch candidate is 90% of the way to the finished product I mean you have to build all the systems you have to build the core engines in place you've got all the systems in place you've got the economy in place you've got I mean you have a lot of things in place it's not like throwing up a you know if you're launching like a a website or something, you've just got a landing page. I mean, this is a serious undertaking to get to that point, but even then,
it's a lot. It's like a fully baked product and now you're just doing tweaks, right? So, there's there's when if you could just do like a vertical slice, as you described it with playable, the bakedin decisions are are reduced, right?
Yep. That's right.
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or use the link in the episode show notes. We hear a lot about like AI in game development and all the different ways that developers will be able to avail themselves of of these tools. I just had Yoast Van join on the podcast. We we had a whole episode about this. How does Unity support developers as AI penetrates further into the development process and becomes kind of like core to making games? How how does Unity support developers with that?
Unity at the inception of our core value and product design is open. We have a vibrant plug-in ecosystem in which like many of the tooling if you want to call it that is not really made with unity right it's made by another third party Unity is mainly used as the assembly point where you basically check that all those tools are all used in the right context of building this game that you really would like to ship and then you ship it with Unity it's built with Unity Unity runs with it in fact that's a fun

fact we ship with a game an old game engine in the past they basically you spit another executable but in Unity's case the reason why when you actually look at click play in the editor when you're making the game that is Unity right there that is actually your game running like it's just happen to be in the editor window so when you ship Unity ships with you so for us we are the assembly point the destination point the end point of a game because we ship with it the tooling it could be a tooling made by first party it could be tooling made by third party that's the preai age so we don't feel differently when it comes to AI We have Unity AI assistant which is a first party AI that we built. Are we going to open up with third party? Yeah, private beta. We have MCP gateway that we have launched with a couple of partners. We're also planning to actually find a way to open this up with more people that actually use other workflows outside of Unity through MCP server. We never shy away from opening us up from third party. We believe Unity at the end of the day is the destination point where people actually create and

bring their ideas to life. Whether the utility of some aspect of creation is using another tool that is outside of us or our own tool, we are indifferent. Now back to our AI product. One reason why we invest in a first party AI product is because we believe like we understand games. That's basically the nature of it. There's a lot of models out there where the model's really large, right? It's a large language model. It is called LLMs, I guess, for a reason. Sometimes when you use a large language model that is generally smart about many things and you're trying to use it for remedial tasks like creating an NPC character, it's a bit of an overkill for two reasons. One, it's expensive. The inference cost alone is very expensive. But secondly, they're not specialized. They're not grounded. They're not trained. They're not tuned for the object of making an NPC, which is a very specific task. Layer that with the fact that creating NPC has dependency, right? like you want to create animation rigging. Okay, first you need to make the mesh. Okay, before that you need to

make the texture. Okay, before that you need to make the concept design. There's a sequence of operations to make an NPC. Why we believe Unity can really excel in this area in our own agent is because we understand the Unity context. We can make a model that is actually very cost effective, very smart and making video games. We don't care about them being smart in doing anything else. That's not our job. But we understand developers. Developers don't want to spend too much in burning tokens to create games. They want to make games by consuming token as an expression of productivity and an extension of themselves. What we want to invest in our first party AI is to solve that problem. We believe we will have an AI agent that really understand game development in the Unity context environment. We feel we have a really strong advantage in that area. But at the same time, if you want to use some stuff outside, we're going to be open. So we are agnostic to what you use or outside.
I do think that we're going to see like I don't want to call it like a reckoning but like there's going to be a recognition at some point that like the

way that a lot of people have been interfacing with like some of these development tools does not scale. I see people like and they'll kind of like brag about it. It's like, okay, I built some product or some tool with whatever cloud code and like I needed to add a new feature and so I basically just rebuilt the entire thing with that feature in it.
Oh yes. Yes. You just hit you you got it. It's like you're referring to I can oneshot a game, right?
Right. Like oneshotting anything.
Yeah, you can oneshot anything. But like you know the the reality of video games is it's an enterprisegrade entertainment that you need to think about not just the visual fidelity of it the mechanics the services you use what monetization do you want to make what economy design is like all of these interaction oh your game is multiplayer how do you balance that right like all of these things require thought now the thing that you mentioned that actually is good the reason why we're thinking about this thing differently you don't want to basically

reprompt and make from scratch That's also another thing like the idea of game development with AI is like human in the loop, right? Like you have context of what you've done before. You want to build based on that so you don't regenerate. Yeah, we got that too. You want to change a small little bit. Yes, we got that too. You want to change a small little bit but don't touch anything else outside. Yeah, we got that too. That requires a deep understanding of how human and AI interact and that's our environment. The other thing also to note is like yes there's a lot of like smart AI stuff going on outside. The reality of a professional grade game development is you can't one shot a game with a professional grade and secondly most of the time when you multiple prompts a game you still need human the loop where you need to make final layer review or edits. So that's why like our role is to make sure that we can create an environment where that is possible with the idea of not just humans doing it but human in the loop with AI agents
right and just being mindful of like

these token call I mean I think people look at like tokens as just like immaterial or like you know rounding down to zero in any given prompt and okay for some tiny prompt they might be like these accumulate into at some point like real costs here and it's not just free to use. I mean, I I think people don't recognize that the view of genai is just like, well, I'll just continue to reprompt or try to oneshot things and I can rewrite the whole code base. I don't need to refactor. Like all those core design principles that were developed in software engineering over the course of, you know, however many decades, they're important for a reason. And those are going to be important whether you're using Genai to write the code or whether you're writing the code by hand. And in fact, they might even be more important in the GI case because you could have all these agents just burning tokens with no sort of like discernable or scalable or extensible output, right?
Yes, I agree.
All right. What are you most optimistic about? What are you most optimistic about with mobile gaming?
Well, at its core, gaming has never been

about having the best graphics or the most intricate complex design or economy design and all that stuff. It's about fun. Mobile, I believe, continues to be probably the best place for fun to show up. Smartphones are more than powerful enough to display rich graphics and experiences. They're portable enough that anyone can access that content at a massive scale. That's exactly where Unity sits. Unity is mindful that games is not just about graphical fidelity, but all the ongoing services and the design of the game itself that makes it such a good entertainment experience. What I'm most optimistic about mobile other than the fact that it will continue to become a portable platform robust allows fund to manifest. Mobile will also benefit with this AI revolution that is happening right now. Mobile has the most device penetration. It has the most volume. It has a working discovery dynamics. It has a flexible monetization models IP ads paid and all

these stuff. It also have support of graphics at any level complexity that unity extend. So we believe like we sit in the intersection of allowing mobile to happen. Mobile has propagation on device. It has a performance differences in generation of device. Unity sits to enable this propagation device to exist and it can thrive and with the content generation with AI becoming more and more accessible. More and more devices will get entertainment content. So I have high hopes for how mobile will go in this AI era.
Felix this was great. This was very informative. Thank you very much for taking the time. I just really appreciate that we got the chance to have this conversation.
Same here, Eric. We should do this more often.