# Yahoo CTO on AI Velocity and Legacy Modernization

**Podcast:** alphalist.CTO Podcast - For CTOs and Technical Leaders
**Published:** 2026-01-29

## Transcript

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And it's a very like personal special moment for me because Lee is the CTO of the first website I ever visited in my entire life.
Welcome, Lee.
Thank you for welcoming on the show.
And yeah, I'm honored, I guess, that uh we were the first website you ever visited.
I think that also applies to many of my guests.
So uh Lee, you're the CTO of Yahoo.
You're operating a broad consumer portfolio: mail, finance, sports, news, search.
Um, and and Yahoo is these days owned by a private equity company.
So I think it was public first, um, like in the Mursa Meyer times, and then changed quite a lot.
Um and I'm I'm super super interested what what changed in the last years uh since that time, and uh how how you got into the game, etc.
And how you see the the world out there right now.
Um super super curious.
Um, and also the the tech angle to it, like how you I don't I guess you have to deal with legacy quite a lot, with AI quite a lot.
So super interested in that.
Um before we start, so I also discovered a personal angle.
So you lived in Berlin for quite a while, worked for HelloFresh.
That's right.
Yeah, I did I didn't I never lived in Berlin, but I I did work uh I was there pretty often, probably once a quarter, something like that.
So um I think I lived for a month one summer in Berlin, but uh never lived lived, I guess, in the city.
Yeah, you only you don't know what uh when I lived there for one summer, right?
You never want to hit it in the winter.
Exactly.
Exactly.
Yeah.
Before we start with your your history and and Yahoo insights, um maybe we start with your early computing history and your nerd path, as I always say.
Like sure.
What was what was the earliest moment when you thought, okay, computers are magic?
Like why are you doing what you do?
Oh, gee.
Okay, so probably you know, go way, way, way back.
I remember my dad brought on a computer when I was a kid.
I think I was probably 10 years old or something.
Um, and uh I remember firing, you know, I saw him fire up NS DOS and open up uh actually it was it was Lotus 123 at the time, um, and doing spreadsheets and being like, what what in the world is that?
And then you know, he let me play around on not in the spreadsheets, that would have been bad, but he let me play around on the on the terminal, and I was just learning some of the commands and figuring out what was going on, and that and that felt like magic, right?
That you could type something and have output come back and manipulate this thing.
Um that was very magic.
And then I remember shortly after, probably a couple years later, kind of discovering how Q Basic worked and running, you know, like kind of just again exploring on the computer, it's like, oh, what's this Q Basic thing?
And then running that and then finding the default demo programs, and then be like, oh, you can actually tell this thing what to do.
Uh, because it you saw the code behind the games, and then it's like, oh, okay, let me try to understand how to change the way the game works.
And that that was some of my earliest memories of working with computers and and software.
So gaming and cheating, I guess then.
Exactly.
Yeah, that's actually funny because I remember the the two the two demo programs that came with Q Basic at the time, one was was the snake game, um, and the other was uh gorillas.
Um and I remember, yeah, cheating and gorillas to figure out like you know, how how do I make it so that my the banana always flies to the other gorilla, right?
Or how do I change the physics of the thing to to to my advantage?
And then ultimately you uh ended up studying IT or what what like how did you sure?
Yeah.
So, so you know, I was always fascinated then with software from that point on, and and I learned programming and I spent a lot of time learning different software, you know, different languages and learning more about computers and software in general, um, you know, building my own machines, right?
Uh ultimately ended up studying computer science.
Um, it's kind of funny, actually, that was not for sure.
When I when I entered university, I was deciding between applied physics and computer science.
Those were kind of like the two areas I was most interested in.
Um, but ended up choosing computer science.
Uh, and then actually also ended up doing my master's degree in computer science as well, with the specialization in AI.
Okay, cool.
And uh I guess that that is what you can use these days quite well at work again.
Or is that outdoor outdated knowledge?
It's it's you know, uh, I mean, a lot of the it's funny, a lot of the original things you know, we were studying at that time uh still apply today, obviously.
Um, you know, we studied both a combination of statistical methods and also um kind of first order logic inference methods, and and I think those are actually super meaningful even today, right?
Uh so obviously, you know, the deep learning revolution had not happened yet, and some of the beginnings of deep learning were present in a lot of what we what we were doing in university.
Um but uh yeah, so I think still still very relevant today.
Yeah, but I mean it kind of developed nicely, right?
Indeed.
And do you know the history?
Because that's when like I think many people that I know lost track a bit.
Um what what happened since Marissa Maya is gone?
Like, like what changed?
I mean, you know, PE owned, uh, yeah, was was taken private.
Yahoo is still one of the most visited sites on the internet, right?
That's probably the key thing for listeners to know is you know, we're still one of the most visited sites in and really is still a top five internet company in the US.
Um, and you know, across the board, right?
Not just in general, but you know, if you look at how we're performing in the various categories, you know, news, finance, sports, mail, et cetera, we're still, you know, the top three in all those categories.
Um, and so you know, it's still a very storied brand and and well respected and and well visited uh website on the internet.
You know, you asked about kind of what happened since the Marissa Meyer era.
Um, you know, we were part of Verizon for a while uh before being sold off the sold to private equity.
Um and then, yeah, obviously under private equity ownership the last few years.
And how does private equity life change the company and in total?
Can you can you say that?
Like uh are you allowed to say anything?
Like no, of course.
I mean, I I think ultimately, you know, private equity is great in the sense that you know, I think any company that's public is under a lot of scrutiny all the time, right?
You're expected to have quarterly results, and um, I think we have a chop we have an opportunity, you know, being private to really work on all the modernization pieces we need to do.
And that's a big part of what we've been doing here, you know, going through this transformation journey, right?
Um, and do that without having the you know external, you know, Wall street uh, you know, perspective all the time, right?
Yeah, Wall Street, yeah.
Yeah, we and we get we we get to innovate um and find a way to to reimagine you know the entire company.
And we've done that over the last few years, right?
You know, um in the last 18 months, we've updated nearly every pixel of every product uh and supercharging them with AI and and refreshed experiences.
You know, we have a whole new homepage.
Uh the news at mobile app is is is brand new.
Uh, you know, you've seen all sorts of new features, I'm sure in Yahoo Fantasy, Yahoo Sports uh and so really yeah I think an opportunity to reimagine how we uh still do what we do in terms of the original mission of the company right we're still really here to help people you know help them know what to do next help them you know figure out how to live their life how to achieve their goals ultimately we're here to help people help consumers achieve their goals in life uh and I think you know having an opportunity to do that in a refreshed way is is fantastic.
Okay.
Yeah cool.
And um I I I think um the biggest challenge then or one of the big challenges is then to innovate while having to deal with like 30 years of history, right?
I mean I know tech we both know tech.
Like if many people work on one product um that is not necessarily good I would say like at least from my experience.
I don't know about what Yahoo um and that um brings up some challenges so being able to innovate um while uh be being able to also refactor, replatform and and reinvent um I I think that's that's uh a huge challenge from my perspective if I if I can can play like outside the outside in view.
Well, I think any any modernization journey is going to be challenging, I guess, right?
Um, but you know, I think we're excited about how we can move faster than ever, right?
Really, we're trying to, as I mentioned earlier, you know, unlock that shipping velocity and and you know, do bold experiments and and you know, be smart about our risk taking.
Um, and so, you know, I I think on the one hand, yes, uh, we have to deal with the 30 years of things that have been built over the many years, but at the same time, you know, we can take a complementary approach, right?
So, for example, um, you know, we we have been migrating a lot of our systems to the cloud.
Uh, we're able to, you know, take advantage actually of the fact that uh we have all this physical storage uh in our data centers and we can actually make them work together.
And that helps us, you know, have greater resiliency and helps us deliver a great solution to our customers um without necessarily having to you know throw away everything that's been built uh before.
And um if I if I would ask you for your your mode and tech, is that the fact, like not necessarily your technical mode, but your mode and tech, is that the fact that you're able to move fast while still having to deal with that, or what would it be?
Well, I think I think for any consumer internet company, uh velocity is a key differentiator, right?
Being able to respond to customer needs and being able to build uh the experiences that customers desire and and again, going back to our mission to help people fulfill their goals, right?
I think especially if you think about one of the things I you know we talk about is the the digital wilderness that people are trying to wander through and how you know over the last 30 years that hasn't really changed uh to some extent you know people think if you think about why was Yahoo originally built right um 30 years ago it was to help people navigate that digital wilderness it's where do I where do I go?
How do I find the things I want to find?
How do I make sure this is good information?
And somehow, believe it or not, 30 years later we still have that problem.
How do we make sure that people are able to achieve their goals safely and securely and in a trusted way that you know they want to achieve those goals.
How do we do that?
And but that all comes back to you know from a we asked about Mo, how do we do that quickly?
How can we quickly get the right product into our customers' hands so that they can achieve those goals and navigate that digital wilderness fast I guess and what does that mean technically like I guess like if I if I look at your tech stack I I find whatever technology I I touched in the last years as well like or is it is it very strict?
No no you'll you'll find a lot of the same technologies that other people are using.
You know we we are very again when we think about modernization, we're very much embracing all the modern uh software you know technologies, including you know, for example, the cloud, right?
We're using the cloud, and then obviously within the cloud, we're doing things in a very cloud native way, for example, Kubernetes uh across the board.
Um, you know, we're using we're utilizing a lot of various uh cloud services that are being offered uh by cloud providers.
Um and so yeah, really trying to you know embrace a a modern way of building software um within within our systems.
And Palumi, I guess, because you work there.
No, um, so so you know, I'm not gonna get into the specifics about what we use in terms of our exact stack.
Um, but uh uh, you know, I'm I'm definitely not, you know, necessarily pushing my own uh previous biases, let's call it, uh, into the teams.
Your your dog food.
Yeah, eat your own dog food.
No, uh just joking.
Um when you talk about velocity, um I mean you have a huge organization.
Um what's your ideal setup uh from your today's perspective to kind of keep track of what is happening?
Um is it purely trust and having leaders in place that like uh uh make sure that that velocity is right?
Or is there like do you use Dora or what what do you what do you what do you believe in?
Yeah, that's a great question.
Uh I think obviously trust is the big part of it.
I think in any organization, you have to trust your leaders and you have to trust that your team can deliver.
So obviously, you know, there's trust.
Uh I guess, you know, going back to some whoever coined the term, you know, trust but verify.
Uh, and so there are there are a lot of mechanisms that um I I I like to have to make sure that you know I can understand the state of the organization, right?
And so, you know, every month we have a monthly business review within my teams to understand, you know, what's the delivery?
How's the delivery been?
Um, you know, how are we uh how are we tracking relative to our goals?
And we set goals at the beginning of the year.
Um, so we have a lot of different mechanisms to make sure that we're heading in the right direction.
Um, a lot of those mechanisms are oriented around building, going back to velocity, rapid feedback loops.
How quickly can we know if it's working?
How quickly can we know if we should double down or pull the plug?
How quickly can we know uh, you know, if there's any kind of signal that indicates success?
And so everything's oriented around operating that feedback loop, making sure we know what are the signals we're looking for and having a scientific method, let's say, like having a hypothesis ahead of time, so we don't try to you know phack our way into success.
Um, and really setting up those experiments, running those experiments, and then okay, based on the results of that experiment, how many more experiments can I go run quickly?
Um, and and that experimentation, by the way, applies in multiple ways.
It applies both to how we operate.
So there's like a meta element of okay, can we experiment on ourselves?
And there's also obviously experiment on the product itself.
Can we actually experiment with you know what we're shipping to our customers?
And is there one, uh, I mean, you talk about experiments, but is there one technical KPI that you also trust in that is maybe more like a proxy metric uh that you attach before, like I don't know, pull requests per engineer a week or something like that?
No, I I don't think there's kind of one metric that tells us if we're successful or not.
Yeah, um, I think you know, if I think about at the Yahoo level, right?
I think we're often thinking about user growth.
Are we really helping user user engagement?
Right?
Are we getting more users?
Are those users more engaged?
Are they actually getting what they want done?
From an internal perspective, because I think that's kind of maybe what you're pushing on.
There's no one single metric that tells us, hey, are all the engineering teams delivering at the rate and you know, quality and you know, et cetera, et cetera.
Dora, for sure.
I I I've used Dora before.
Um, I I'm a big believer in Dora.
I think kind of understanding the key Dora metrics is helpful, but there's no, I think Dora's actually an interesting example of it is very useful telemetry, in my opinion, for self-improvement, but it is very hard to compare DORA metrics from team to team, right?
Like the the you know, lead time to change on one team could be drastically different from another team's for very good reasons.
And so, you know, to to say like, well, you're four hours different from the other team, you must be worse.
Yeah, yeah, it's not it's not really meaningful, but but saying, hey, can I go from seventeen hours to sixteen hours to 15 hours?
Can I improve on my own metric?
I think is very meaningful.
Yeah, absolutely.
And tracking that over time, like understanding over time what what like how how how velocity changes in a team um is is what I also find find meaningful.
And then I guess if you have stuff like SLIs, SLOs, uh on top of that, for your team or certainly, yes, yeah.
We we track, I mean, we take operations very, very seriously, obviously, and we're we're constantly looking at uh our metrics there as well.
Okay, okay.
Would you describe your job, your day to day as more as like the job of a portfolio manager?
Uh because you you have a huge portfolio of like even like almost companies uh below your yourself, or to what to what degree are you yourself involved in technical decisions, um and how where where can you as a leader make a difference from your perspective?
Yeah, that's a good question.
I I think it's a combination of things.
Uh I I guess portfolio manager is a pretty good term.
I I do think probably if I think about how much time I spend, you know, doing various things, a good chunk of my job is portfolio manager.
It is, you know, thinking about uh and I actually do take it if you ask anyone on my team, I do take an investment view of this, which is, you know, for every single thing we're doing as a team, what's the cost and what's the expected ROI gonna be for that that activity we're doing?
And you know, how do I maximize yield across my portfolio uh given the resources that we have is something I am constantly thinking about.
Um and so in that sense, very much, yes, I play that portfolio manager role.
Um, but that would be, I think if I if I thought about my job just as that, that would be pretty sad, I guess.
Um, because a big part of what I love about my role is getting to work with people, right?
I think that's I think that's really what got me into management, actually, is when I, you know, go going back to my nerd path or whatever.
Um at one point I thought I was going to be in academia, or I thought I was just going to be working, you know, uh in in kind of applied research.
And um I had a manager at the time who conscripted me, let's say, into management.
And she's basically said, hey, the team is growing a lot.
I kind of need you to step up as a manager and you know, take on some management responsibilities.
And at the time I was like, I don't want to do that.
That's the dark side.
Uh I want to spend my time doing research and and hacking on code and stuff.
Um, but after I was forced into doing that, I really came to appreciate just how much I love working with people.
Um, and that's that's stuck with me ever since is, you know, at the end of the day, your greatest asset at any company is is gonna be the employees and the people.
And I I'm a big believer in that.
And I I love developing teams, working with teams, building culture, developing culture, you know, building organizations, right?
That that um are, you know, able to deliver.
And what I think is so interesting about that too is I think you can actually like I I talk to a lot of people this i i think you can take an engineering approach to doing so.
And so I I often think of my my organizations as you know, things I'm engineering, let's say, where um there's an api almost to you know an organization and uh there's you know certain capabilities or features that the organization has and and that's really a big part of my job also is not just to be the portfolio manager but to really shape what that organization looks like uh and to make sure that we have a great place for everyone who's working there.
I'd love to talk to you about AI but first I have one question which kind of uh is a nice follow-up on that um you you you you considered yourself a nerd and you wanted to go more into research etc.
Um what what is like if you look at like look back uh what's what's the last important piece of code you wrote personally and and when was it well I'm still coding today I guess right so I guess it depends on how you define important um I have some fun side projects I'm working on and those those are important to me uh so for example you know one of the side projects I've been working on more recently has been um using AI to help uh actually uh classify and understand uh basketball plays.
Um I I love basketball, uh my kids play basketball, and uh I actually, you know, but I I don't want to have to scrub back through all the film that I've recorded.
And so I'm having AI help me scrub back through and take out interesting clips of like, okay, here's an interesting place where you know the defense could have been tighter or or what have you.
Um so that that's important to me personally, I guess.
So that's I'll I'll say, you know, even just the code I wrote uh a few weeks ago is probably some of the most important code I've written.
Okay.
Cool.
That means that like uh with your kids, you whenever you visit a basketball game, you have a Raspberry Pi with you recording the like taking a video recording of the game then or no, I I I what I what I'm doing is I'm I'm just recording it on my phone, um, and then I'm you know bringing it back home and then you know having it processed by this this application I've written.
Cool.
Yeah, cool.
Um and I I I I assume you also use AI or cold code or what whatever, like these days.
Oh yeah, yeah, I think that's the thing I just described.
I built with yeah, with with uh some agentec coding tools, yes.
Okay, okay, cool.
And and how do you see that?
Like, how do you actually or how does uh Yahoo utilize AI these days?
Um and uh how do you see this uh like entering the world of engineering and the world of your your tech teams?
Like and and how do you make sure, like do you want to make sure it arrives?
I guess yes, but uh like do you want to make sure it like everyone adopts it and and and and uh and and how?
Yeah, I mean, I think I think you know, we we do want to make sure everyone adopts it.
And I think, you know, during my tenure here, we've really shifted from AI as a tool, kind of when I started, you know, I think a lot of us were using AI as a you know chat prompt, basically, right?
And now I think more and more of us are really adopting AI as a coworker.
Um I think that's a big shift because it's not just this interrupt-driven back and forth, but really finding ways to have AI be more agentic, right?
And actually doing tasks in the background on your behalf.
Um and we're and we're doing that across the board, by the way.
Kind of talked about how how are we as a company using it.
It's affecting how we build, right?
And so that's that's you know, on the development side, everything from you know AI assisted coding to testing to how we run, you know, our our operations, um, to how we work, right?
So, you know, can we actually get better business insights using AI?
You know, can we have AI help us understand uh how we do business or find business opportunities for us, and then how we serve, you know, how are we actually helping our customers with AI and across those three different dimensions?
Um I think we've been both trying to uh experiment quite a bit and also making good progress on on all three fronts.
Okay.
And for the the the folks in engineering, is it like I mean, I I know many organizations where it's really hard to uh kind of and and and and the people are not the problem, but the people are kind of the the the the ones stalling down the progress a bit because um like everyone has to learn it, right?
And everyone has to learn how great it is, and everyone has to learn how to uh how to self-accelerate.
Um I mean, I see it as like a a big uh yeah, exoskeleton for for every engineer, right?
Um like even like a bigger, bigger thing, maybe like a mechatronic robot that everyone now owns.
Sure.
And and and and how do you make sure that everyone discovers that or goes through that that uh understanding is that like per definition, if you just spend enough time with it, or uh do you make sure that like uh I don't know, career trajectories are shaped on behalf of that, like or like how do you how do you ensure that I mean I think this goes back to you know building organizations, right?
It's not just one thing, it's a lot of different things to help make sure we end up shaping the organization the right way.
Um for sure, one of the things we've been doing a lot is just encouraging everyone to go use it.
Like, you know, during I I've had uh a few opportunities to talk in front of the whole company, and during those opportunities, really making sure people know, hey, this is part of our jobs now, and this is important, and here's why, and here's how we want to use it.
Um and I think the other part of it is you know, training, right?
We're providing lots of training opportunities internally for folks uh to learn some of these technologies.
Uh and and not just training, but also I think, especially because some of it's so new is showing and telling.
And so providing forms for people to see, okay, how are other people internally using it?
How are they benefiting?
How is it actually working for someone else?
You know, what are some of the use cases they have?
Uh, and so doing that as well.
Um and I actually think because you mentioned the ex exoskeleton thing.
I think one thing that's also difficult, actually uh about deploying AI, let's say, within an organization, is it's it's not just a new tool.
And that's maybe where a lot of people sometimes get tripped up, is it's not just a new tool, it's it's also changing the way we work.
And so uh, you know, it's not like you can just adopt your existing ways of working to using AI.
You have to actually, in some cases, think about okay, is my process even correct?
Like, is this process still right?
You can't just put AI into the existing process.
You may actually have to change the process or change the way we make decisions or change the way we work.
So, you know, super simple example is just around documentation, right?
Um, or or how we write things down.
You know, before you never had to think about, well, hey, how do I make sure an agent can consume this information?
But even just making sure that the information is consumable by agents, I think is kind of a prerequisite now.
So we we talk about that, right?
Um, and you know, similarly, you know, again, going back to how you shape organizations, like as part of our interview process now, we actually ask people, hey, if you want to use AI, go for it.
Use AI.
And we in fact we we encourage you to use AI as part of our interview process because we think it's part of how people should work.
Yeah, I mean, I I see that also happening in schools a lot where I'm not sure.
Is that right?
Like is that the right moment to adopt it?
Or or what what does that lead to, right?
And what do we have to learn tomorrow?
Um I I think um one challenge I've I've been been dealing with, um, or one one thing I'm I'm observing is the fact that also many engineers don't necessarily feel good with it because it changes the way they solve problems personally, right?
And it it it changes perspective and and also the let's say um reward mechanism, right?
Beforehand, like uh the reward mechanism was like move a ticket from left to right.
I mean uh a bit simplified, right?
Um and and there was like a clear description given to them while now they have to make the description and they have to uh describe the problem and they have to like sometimes take take the perspective of the consumer.
Is this you think um a a change that will even uh accelerate further so that like every engineer becomes a product manager, every product manager becomes an engineer, everyone becomes like a mini CXO, or just a full stack engineer, or where where does your like how how does your current imagination look like?
I mean, I think it maybe gets closer to the everyone's a product manager level of things.
I think I guess it in my estimation, I I don't know how much people will need to interact with programming languages as we know them today, um, on a day-to-day basis, right?
But I think at the same time, uh software engineering is not going away.
I think that's the thing I really emphasize with both my own teams and uh when I talk about this with other people, that's kind of my my fundamental belief that software engineering itself as a discipline does not go away just because AI is here, right?
Uh the same the same principles, I guess, you know, if we think about the software development lifecycle will still apply, and it will still requires our critical thinking to make sure that that is applied in the correct way, right?
So, you know, if you think about testing and you think about uh security and you think about you know all those different aspects of the entire life cycle don't they don't just magically go away because we have AI.
We still need people to think critically about okay, how do I do the testing, or how do I make sure this is secure?
And we can use agents for sure to help us do all those things, uh, but it doesn't it doesn't obviate the the actual needs uh of of those of those critical aspects of everything that and engineers do it.
Um and so I think I I think it it it will change how we work, it will change kind of uh and there will be dissonance, I guess, because now I'm doing different things, but the fundamental job to your point, like of moving something from left to right, doesn't go away in my opinion.
And and it just I think just how we do that will change a little bit.
And again, I think everything we've learned and the scope maybe of of the things in the scope that's right that's true.
Yes.
But but I but I think everything we've learned from a practices perspective of like what makes someone a great software engineer in terms of you know their understanding of what is good architecture, what are good patterns, you know, how do I think about observability for example those kinds of things don't just go poof uh just because we have AI.
Yeah.
What was your recent moment when you thought okay this is a miracle like I mean I every once in a while have it like uh playing with Claude or whatever uh and and just think like hey this isn't possible like I I don't believe that like is there anything you you you remember?
You know, there's always times where I think I'm interacting with some kind of you know whether it's because an agent did something on my behalf autonomously after some period of time or maybe like if I could think of an example more recently where uh I was actually kind of playing around with um understanding something about myself because it was basically trying to diagnose some like health issue I had.
Uh, and I was very surprised at how well it did, because it basically helped me eliminate a lot of different things that I wasn't sure about.
And basically I was able to start trying out some of the things that were being suggested, um, which worked pretty darn well.
Um, so it was actually very surprising, I guess, in that regard.
But no crazy uh agentic move on a on a computer where it took over and just like made you shiver because um like things just worked out.
Uh not recently, no, not like now that comes to mind.
Lee, you have quite a lot of like interesting consumer products under your umbrella as well, uh, such as mail news, finance, etc.
Um, where do you actually use AI to delight users?
Um uh and and not just add a feature label.
Is that really something where you also partly think about like how we make a difference these days?
Like, I mean, uh like I don't know, people use like many people use Gmail, right?
Like uh and there are many, many other ideas out there, many other metathers out there.
Like, uh, do you adopt any of those, or do you like how do you see your your role there?
No, I mean, I think the the point isn't just to throw AI everywhere and have AI slop, obviously.
And and you know, this goes back to what I was talking about earlier.
You know, we're really trying to pair the technology with human creation to deliver relevant and engaging content and to ultimately help users achieve their goals, right?
And so, you know, if you look at a lot of what we've done uh in mail, for example, we have catch up, right?
Uh uh uh so you can know what's going on when you're mail, or in news, you know, key takeaways.
Uh, or in fantasy, we have you know, AI powered player insights.
And so it's it's about you know making your daily life easier and not necessarily you know just throwing the AI label on there for the sake of doing so.
And as I mentioned earlier, too, we're also trying to combine that with you know human judgment, right?
That's still a big part of our you know, our core value is is to add that human layer of input uh so that people can actually you know get that that uh uh that tailorized and and uh uh you know uh curated experience that you know users expect from us.
If you look at your top three engineering problems um at consumer scale today, um what what is it?
Is it latency, cost, reliability, abuse, data, or what what is it?
I think cost is definitely one of them.
Uh you know, I think for sure, you know, can consistently finding ways to optimize how are we actually delivering to customers is is for sure what I would consider an engineering problem, especially at the scale at which we're delivering, you know, every time we do anything slightly inefficient ends up you know adding up to a lot of dollars, right?
And so uh cost is is for sure uh an engineering problem that I think is in in my top three.
Uh I think velocity is actually in that top three as well, where again, you know, we really want to be able to go fast and in a safe way, though, and with a high quality.
And so that's I think a perennial engineering problem for anyone working in the consumer space.
Absolutely.
Absolutely.
So I I think those are the ones that come immediately to mind for me.
Okay, okay.
Thanks a lot.
Then I still have a little surprise for you.
So yeah yeah.
Back in the Marissa Meyer days, there was an Easter egg silently introduced to Yahoo Finance which is like a a hidden date picker um you can you can use to like go back in time um and buy Bitcoin if you want right or uh just like travel back in someone's someone's life um and and and potentially change things like and and and I'd like to like virtually travel back in time to the year 2006 when you worked at Microsoft as an applied researcher.
And I mean maybe you would whisper ditch research uh I don't know go to leadership straight away um I I I take that away from from your your option space then you now have a chance to whisper something into your young young younger much younger not much younger years um uh what would it be?
I think it's but believe believe in yourself and love yourself.
Um I think early on in my career you know I had a lot of imposter syndrome and and didn't have a lot of confidence.
And I think yeah I would I would just tell myself to believe in myself and love myself to to to let myself you know be willing to make mistakes and forgive myself for those mistakes.
Okay.
Yeah, very good answer.
And and and yeah I I hope you learned that on the way uh I mean, you you you have like a you you've you've had a a great career so far, uh, I'd say, like just looking at all the all the different like great companies you worked at.
So I I think you hopefully received some recognition uh recognition there.
No, yeah, I mean that like that's why I would tell myself you know earlier on that uh that that information, yes.
Okay, thanks a lot, Lee.
Um was was great talking to you.
Really really proud that we made it.
Um and and uh I finally got to meet the CTO of the first website I visited.
Um thanks a lot for that.
It's been a pleasure and an honor, and thank you for having me uh on as a guest.
Bye bye.
Have a great day.
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