Growth is the job is to connect users to the value of your product.
Growth sometimes gets this reputation that it's just pure metrics hacking.
You've worked at three of the most successful consumer subscription products in the world.
What do you think is the biggest missing piece that people don't get about building a successful consumer subscription product?
User retention is gold for consumer subscription companies.
If you don't retain your users, then a lot of the onus is on getting them to pay on day one.
Noam Levinsky.
He said that I need to ask you about the biggest monetization win that you found at Grammarly.
The lived product experience for most of the free users was that Grammarly was just a product to fix your spelling and grammar, because those were the free suggestions.
What if we actually sampled a number of different paid suggestions and interspersed them to free users across their writing?
All of a sudden, people were seeing Gramly as a much more powerful tool than they were before.
What's the most counterintuitive lesson you've learned about building teams?
I saw some of the highest performers just being people that had very high agency, had that clock speed, had that energy, but they didn't necessarily need to have deep experience on that matter.
Sometimes experience can be a crutch, especially in this world where the grounds are shifting so fast with AI.
A lot of your learned habits actually need to be intentionally discarded.
Today, my guest is Albert Cheng.
Albert is known as one of the top consumer growth minds in the world.
He led growth and monetization at three of the most successful and beloved consumer products in the world Duolingo, Grammarly and NowChesscom.
Earlier in his career at YouTube, he worked on streaming and gaming features used by over 20 million people.
His unique approach to growth blends marketing, data, strategy, and product management.
Thank you so much for having me.
And so much more.
A huge thank you to Eric Allabest, Noam Levinsky and Jorge Mazal for suggesting topics for this conversation.
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Albert, thank you so much for being here and welcome to the podcast.
Thanks for having me, Lenny.
Excited to be here.
I'm even more excited to have you here.
So, as I do for every podcast conversation, I reach out to a bunch of people that you've worked with, that know you well, to find out what to ask you about and what topics to spend time on.
Jorge Mazal, who is famous in my world for writing what was, for the longest time, the most popular newsletter post ever on my newsletter.
It's actually people have usurped it now, but it was like stuck there for a long time.
So here's what he wrote.
It is a mystery to me how Albert is able to do what he does.
I am actually eager to listen to this episode and learn from him.
That is super nice.
Thank you, Jorge.
I've learned so much from him.
I'm the type of weird person that likes to wake up before their kids and like pull up a bunch of browser tabs and look at experiments.
So it was perfect that Jorge brought me into the growth world at Duolingo.
Learned a ton of best practices and he's just a great guy.
Thanks, Jorge.
We're already getting into these tactics.
I love it.
Let me just give a little framing on what I want to do with this conversation.
What I want to try to do is to help people learn tools and mental models for finding growth opportunities for their own products and essentially, learn the growth mentality that you bring into the companies and products that you work on.
What I want to start with is to give us a little insight into... how you became what you became.
There's an interesting pattern I found across a bunch of recent guests, which is many people were very good at piano when they were younger and were very serious piano players.
For example, head of ChatGPT Nick Torelli was like almost going to become professional jazz pianist.
You were very serious as a piano player earlier in career.
How did you go from pianist to one of the top growth minds in the world briefly?
Well, that's very flattering, but I appreciate it.
Yeah, I grew up playing a lot of piano.
My parents were immigrants from Taiwan, and I was the oldest kid that they had.
And so I definitely felt that strong encouragement, if you will, to learn a bunch of things, take them seriously, study hard.
And so I did, right?
My parents, even though they weren't musically proficient, they had a deep love for classical music.
So I was a stereotypical baby that would listen to Mozart, I guess when I was sleeping, type of thing.
And I still vividly remember we had this upright Yamaha piano.
And at the very top of the piano, we had this countdown clock from 90 minutes.
Literally every single day of my childhood, just practiced really, really consistently.
At first, like I really was irritated by that thing.
But as I grew older, I started to appreciate like music quite a bit more.
But anyway, like I think what really accelerated my, my interest and abilities in piano was like I feel like I hit the lottery.
I had perfect pitch.
And so I was able to, you know, quickly understand whether I was like playing the right stuff or the wrong stuff and just pick up music pretty, pretty rapidly.
What does perfect pitch even mean?
Does that mean you know which note is playing?
Okay.
Exactly.
Wow.
So I can listen to a song and then like just a very, very clear understanding of which note I'm supposed to start with and if I'm playing something wrong.
So it's very helpful.
It's unfair.
Definitely.
So anyway yeah, I got quite good as a teenager, in high school and even considered studying at a music conservatory.
My intrinsic motivation for music wasn't necessarily as strong at that point.
And so I decided to go to engineering school instead.
But that would have been an incredibly different career.
And to your original point around the relationship between, like music and growth.
I didn't really reflect on this until recently.
I have a four year old and I'm like starting to teach him how to bang on the keys a little bit.
But a couple things stand out.
I mean, one is that I think music and growth, they both rely on this just consistent repetition.
Like you're constantly making mistakes.
You have this super tight feedback loop.
You have to get really resilient to just making mistakes all the time.
And you know that the way of learning is through those mistakes, right?
So that's kind of a thing that I learned very early.
And the second thing that occurred to me is that they both have this like structural underpinning to them.
With growth.
You have a growth model, you have metrics, you have experiments, you have channels, things like that.
But you also need on a day-to-day basis to have creativity.
You got to come up with like interesting solutions, like hypotheses to test.
And the same is true on the music side, right?
You have music theory of scales and stuff, but to create beautiful music you need that passion, that emotion, that flow right.
So I think that's the beautiful combination between the two.
Fun fact my wife bought me piano slash singing lessons for Father's Day recently and I've gotten really into the stuff.
So I'm learning how to play very basic piano now and learning to identify notes and hit notes with my voice.
Nice.
What a weird new thing.
Could be your next act.
It could be.
I could go the reverse.
I could become a professional piano player.
Oh, man.
No, it's so fun.
So hard, though.
I'm just like, my fingers are like, how do you do four freaking keys at once?
Yeah.
Just like, what is going on here?
Okay, so let's get into the meat of it.
I want to talk about growth.
There's a very specific framework that, as we were chatting, that I think would be really helpful for people to hear and learn from you.
You call it explore and exploit.
I think there's a bunch of different ways to think about this.
Talk about this framework and how that informs the way you think about growth.
Yeah, I initially came up or heard about Explore and Exploit through my engineering partner at Grammarly Nirmal.
And I think he actually had taken some reforge classes.
So maybe the original inventor of it might be Brian Balfour, who I know has been on your pod.
But anyway, it's a great concept.
The gist of it is that when you're in exploratory mode, think of it as like finding the right mountain to climb.
And then when you're in exploitation mode, it's like focusing your resources on climbing that mountain effectively.
And certain companies.
I think the warning is to basically spend too much of your time on one end of the spectrum, right.
If you do too much exploration you can have your team feel a little bit too scattershot just trying 100 different random ideas.
What's the through line?
What's the strategy?
How do you pattern match successes across them?
And if you do too much in exploitation, which is often the MO of growth teams, it can lead to this saturation and stagnation where you're just locally maximizing a thing.
And even though this principle of explore and exploit it's typically thought of as a macro thing, I like to work with my teams more on the micro, on the insight level.
So I'll give you a concrete example.
So I work at chesscom and one of our priorities is to encourage chess players to improve, to learn and improve.
So one of the PMs that we have, Dylan, he works on all the learning features.
The most used learning feature in our product is called game review.
So you play a game of chess.
After the game's over, we have this virtual coach that teaches you about your worst moves, best moves, etc.
And his job is to like improve user engagement and retention.
And so he's in this exploratory phase trying to figure out like, how do I drive more of that type of activity?
And what he observes is that 80% of people that review their games actually do so after a win.
And that's really counterintuitive to when we initially built the feature.
We thought that people would wanna use it after losses or to see their mistakes such they could like work on their mistakes.
It turned out not to be the truth when it came to the human psychology and the actual data of it.
And so we made some changes in the product experience when you lose a game.
Now, as opposed to surfacing your blunders and your like horrible stuff that you did, we flip it on its head.
And so we show you your brilliant moves, your best moves.
And we have coach say something encouraging.
You know, losing just part of learning, like keep it up, that type of thing.
That change alone was pretty dramatic for us.
It grew game reviews by 25%, subscriptions by 20%, user retention by a lot as well.
So that was fantastic.
But the point is that it doesn't just stop there, right?
You have to take that insight, share it broadly across the company, right?
Now adjacent product managers, like the PM working on puzzles, can now think about okay, how do I audit these cold patterns in my product and think about making them more positive?
Right, i can change the success rating.
I could tweak some copy, change the color of some buttons, and so you now can take this like experiment win and expand it out 10x across your organization, and that's the kind of exploitation phase of it.
So when done right right, you can oscillate between the two until you saturate out of exploitation mode and then you encourage the teams to brainstorm and get more creative again Amazing.
Okay, so there's a lot here to follow up on.
One is is the core piece of advice when you find something that works really well, find ways to build on that learning.
One is, here's an insight, it can apply to other parts of the product.
Hey, teams, here's something we learned unexpected, maybe this can help you.
Also just keep finding more, like run more experiments in the same zone.
I imagine is a part of that.
Yeah, exactly right.
I mean.
In my experience, the typical win rate and I hate to use that term for experiments is often something like 30 to 50.
Usually, you're trying a bunch of things.
A lot of hypotheses turn out not to be true.
Consumer products are very unpredictable like that.
Those are also super valuable, right?
Surfacing those across the company.
Like the original PM running that experiment doesn't necessarily need to be the person that figures out what you should do for all the other parts of your product experience.
But the onus is on them to clearly articulate what their hypothesis is, what they found, such that then, as like a growth leader, I can encourage people to kind of swarm around that and try a bunch of different ideas, such that the success rate is up and the impact is up.
So it's just kind of oscillating back and forth between the two.
That is the magic bullet.
I think another takeaway here slash something that I think about when I hear what you're saying is there's often a lot more wins in an area than people expect, that you can continue to find wins and growth in something for a long time.
Exactly right.
Yes.
At the end of the day, users...
I think within a company sometimes you can have this siloed approach where you break apart the product experience in 50 different ways and distribute them across different teams.
And you assume that users interact with each of the different features with a different mentality.
But oftentimes, that's actually not necessarily the case.
And so sometimes you can surface an insight that's more human, psychology based, that can resonate across the entire product experience.
And so I think when you can find that, you can double down.
People hearing this might feel like, okay, yes, find big wins and then find more.
Is there something you find that helps you figure out when to explore versus when to exploit, when you've exploited too far, just like any heuristics or I don't know ways of helping people guide them along this process of exploring and exploiting?
One thing that I try to focus on at a company of our scale of like a chess.com, right?
We're running roughly 250 experiments a year.
So we're not like the highest in the industry, but we run a decent volume, right?
And so when that happens, I invest in these like experiment explorer tools.
And we can talk about AI as well as another way to kind of uncover and pick out these nuggets of wisdom.
But basically, these explorer tools can allow me to look across the spectrum of experiments that are going on, try to figure out if there are patterns between the hypotheses and the learnings that are happening.
And if I'm starting to see more and more experiments that are not statistically significant, that may be a signal to me to say okay, we might have kind of tried to exploit a little bit too far.
Like there might not be as much juice to squeeze.
Hey guys let's, like you know, get back to the table and brainstorm and be a little bit more divergent with our thinking.
Well, let me follow this thread on AI and how you're using AI to help you figure this out.
That is very cool.
Talk about that.
I think one of the latest things that we've been tinkering around with is this text-to-SQL capability.
It's actually pretty powerful.
We have this data request Slack channel where, for the longest time and this is still true today people will toss in all sorts of just one-off questions.
How many subscribers do we have in south africa?
Or, like you know, how long did somebody play puzzles like last, last month or something, and these ad hoc questions.
They often take a lot of like human time to just go in and you know a data analyst needs to prioritize it and find time to go run the query.
And yes, you can invest in self-serve tooling to improve at this.
But also i found that ai is quite good at doing that first pass answer as well, And so we're working on like kind of training some of these Slack bots to essentially be the first party provider of a lot of these answers, which makes the company as a whole a lot more data informed, I guess.
And I think what's also kind of interesting is that.
Just human nature is that if you have a question that you feel like you know you might be a bit embarrassed to ask, or you don't want to bother someone, you just don't ask the question, right?
And so, by the nature of having these tools, you get actually a pretty large explosion of questions being asked.
And I think you see this in ChatGPT too, right?
It's like just having a thing right that you can converse with, that you feel comfortable and makes a huge difference.
Okay.
This is extremely cool.
So is this something you build?
Basically, it's a Slack bot that gives you the SQL query.
Or does it actually do the analysis?
No, it does the analysis.
Yeah.
Whoa.
So cool.
Okay.
Is this something you guys are going to release?
Or is this just like somebody... You guys should just build this at every company.
We should.
It's a good idea.
Okay.
Well...
There's an episode where everyone in the comments is like, open source this.
So we'll see if that happens again.
That is very cool.
Are there other examples of that kind of stuff that you've done or seen?
I mean.
An adjacent example is a lot of the product managers.
We're tinkering around with all sorts of different prototyping tools right now.
It's just like, go from an idea to a representative solution.
Today there's a lot of humans involved in taking an idea, writing up a spec, doing a review, doing design, et cetera.
I'm sure you've interviewed plenty of people that have talked about this specific problem.
And when you have those foundational pieces, you can then share them with the rest of the company and they can use that as a starting point.
And then they can try to, you know, put their ideas on top of that and then they become a lot more discussable and hopefully testable relatively soon.
What's in your AI stack along those lines?
The PMs are mostly using vZero.
The designers love Figma, so they're using Figma Make.
The engineers are using a combination of tools right now.
So Cursor, Cloud Code, GitHub Copilot.
Marketing teams use all sorts of tools for translation subtitles, content adaptations, etc.
Customer support uses intercom FIN.
So there's quite a lot of tools that are kind of used across the company.
I would say though, that something that is kind of annoying to me is that we haven't yet figured out the bridging from the tinkering to the workflow quite as seamlessly as I would like right.
And so each sub-function, even though the common, I guess, wisdom now is that AI is going to strip away these functional titles.
It is kind of true that, based on your experience, like you may gravitate to using a type of tool more.
And if that tool isn't as interoperable with some of the other tools that you need to pass down the chain to actually ship it into production, at least at our scale right.
I think for smaller startups, sure, PM should just go ship it.
But for us, like we are still doing some handoffs between functions.
I expect that to change over time.
And we are investing in some of, like you know, design system components and MCPs and stuff to make it a little bit easier.
But yeah, it's an investment and it takes time to smooth things out.
I want to come back to this topic of how things have changed and how you work as a product person, as a growth person, across the companies you've been at.
But first of all, I want to talk about another example of finding growth wins and monetization wins.
Noam Levinsky, who is chief product officer at Grammarly.
You worked with him for a while while you were at Grammarly.
He said that I need to ask you about the biggest monetization win that you found at Grammarly and how you discovered the opportunity.
I had the pleasure of working with Noam and his product team at Grammarly.
Some context first for those that don't use Grammarly.
So Grammarly is an AI-powered writing assistant.
And so typically people will use it as a Chrome extension or a downloadable desktop client.
And basically what it does is it overlays your writing with a bunch of different corrections.
I use it.
I'm a big fan.
I use it and it saves my life.
Fantastic.
Glad to hear that.
Grammarly is a freemium business model, which means that over 90% of our users are on the free.
And the rest of it paid for subscriptions, essentially, right?
And so one of the teams, they work on subscriber conversion.
PM there is Kyla.
That team's great.
And their job is to figure out the free to paid subscription path, right?
And so one of the realizations one is that we weren't actually tracking subscribers, the events that well, for the types of essentially suggestions that people were getting, and how often were users seeing paywalls and stuff like that.
That's kind of step number one, we had to put that instrumentation in.
Step number two is that, hey, we noticed actually, first let me explain some of the logic.
So as a free user, you basically get these underlines across your writing.
And if you accept all of them, then you see the paywall and that encourages you to like subscribe for more nuanced features.
As a free user, the main things you get are spelling, grammar.
They're basically correctness things.
And as a paid user, you get the like, how do you improve your tone to be more empathetic?
How do you improve your writing to be more clear?
How can you rewrite entire sentences?
That type of thing.
And so the observed behavior from all that tracking and data was that actually a very small percentage of our free users was deciding to accept all of their suggestions.
They were more kind of picking and choosing as they go.
And I wonder if you're experience is kind of similar to definitely because yeah, I'm always like wait, we stop rewriting everything.
Just like this part is wrong, I will fix.
Yeah, I'm very much a pick and choose, that's right correction person.
And, quite frankly, both the company brand as well as the like lived product experience for most of the free users was that Grammarly was just a product to fix your spelling and grammar, because those were the free suggestions we were showing people right.
And so we decided to flip that on its head entirely.
And we said okay, what if we actually sampled a number of different paid suggestions and interspersed them to free users right across their writing, such that they were intermingled and we would provide a limited taste of what the paid offering had to provide?
And on the surface like, even though it's rational, the concern is that if we give too much of this away, then will people want to subscribe?
We found completely like that was not the case, right?
All of a sudden, people were seeing Grammarly as a much more powerful tool than they were before.
And our upgrade rates nearly doubled just through this change.
And so I think this is interesting just modernization, learning that, especially if you work on a freemium product, try to have your free product be a reflection of everything that your product can offer you.
Obviously, to an extent there's some costs involved with some of the paid features and things like that.
But it generally will pay for itself if you're able to put your best foot forward and go do that.
So that really worked well for us there.
I think this is what converted me to being a paid Grammarly subscriber.
Wow.
What a genius move.
So essentially, it's here's a bunch of improvements, but you get like three, I think, max, and then it's like, okay, now you get upgrade.
It's basically like a reverse free trial, but in real time, like while you're writing, as opposed to a time-based one.
So we kind of adopted some patterns that are in the industry but molded it to Grammarly's specific use case.
Right.
I was going to ask.
So it's not like a full trial.
It's like a capped trial where you get a certain number of things and then you run out.
And then they get refreshed, I think, once a day or something like that is what I found.
Yeah, you got it.
Yeah.
Grammarly is... the best slash most devious at their upsells.
I'm always just like, God damn it.
I'm so close to seeing an improvement.
I just have to upgrade.
It's just like right there.
It's right there where my mouse is.
Yeah, well, I'm not proud of being devious, but... Devious in really getting me to buy the thing.
Good job.
What was it, Kyla?
Okay, nice job, Kyla.
It's very effective.
I love that.
And so okay.
So in terms of the free trial, I don't know, is there anything there of?
Just there's always this question of premium give things away, and then there's pro account you get it.
There's like trial versus time.
Some features are limited.
I don't know.
Do you have like a, for consumer subscription products, like here's the way to go?
Yeah, I think.
First of all, why do freemium subscription in the first place is a common question that you know, Like I've joined all these companies that are free in subscription.
Like, what do I like about it?
I guess.
Well, one, I think it ties really nicely to like mission orientation of a lot of these companies.
It's often like you want to spread the product as wide as possible, because that's why the founders built the thing right.
You're trying to like improve education with like Duolingo or, you know, Grammarly or chess.com.
Like these are meant to be widespread products with a really wide value proposition that fits globally.
Right.
And so that's, obviously the lowest friction to that is going to be a free product.
So that alone is part of it.
Another part of it is that a lot of these products primarily grow through word of mouth.
And especially if you can build network effects in the product, like Duolingo has a bunch of social features, or with Grammarly they have a bit of a B2C2B play as well.
So you see Grammarly being used by teams and by companies and whatnot, right?
And even if users are on the free plan, they still provide quite a lot of value in making sure that Gramly can be purchased by a coworker or by a team member or whatever right.
So I think these things are usually why I lean toward make sure that the core value proposition that you're providing users is free and is sort of permanently free.
And then you layer on kind of a sampling or a taste of some of the premium features that are on top of it.
That's usually the sweet spot that I've seen.
As to the trials, reverse trials type of thing, I think it largely depends.
I think if you have especially a B2B feature where you may have some lock-in reverse trials, can be super powerful.
You just want to get people in there.
You don't need to ask for their credit card because they're using your CRM or they're investing quite a lot of time in like building out.
You know material and content.
And so by the time that window drops, you actually feel oh man, I probably should keep this and start paying.
I think for a lot of consumer products, it's a little bit harder for that to work.
And so I've typically seen more just normal free trials be the norm.
Let me follow this thread of just consumer subscription products.
I feel like this is the category that every indie developer dreams of building a product in, because it's easy to build.
Cool, I'll build an app.
I'll add a paywall.
And then they realize this is a lot harder than I thought from a perspective of distribution and cax and growth like that.
Is that the biggest missing piece that people don't get about building a successful consumer subscription product.
Yeah, I mean, user retention is gold for consumer subscription companies.
If you don't retain your users, then a lot of the onus is on getting them to pay on like day one.
That's super hard, right?
Then you're dealing with totally different business models, where you're paying for users.
You're trying to like aggressively upsell them before they, you know, hit any sort of habitual usage patterns with your product.
A lot of apps naturally do that, because that's how they break the mold and get their first users to do it.
But I don't know.
I've been fortunate to join companies sort of after that initial phase.
But especially like Duolingo and Chesscom, these are organic word of mouth kind of driven businesses.
And in kind of both ways, they grew the market from a much smaller market as opposed to...
It being a very competitive space where you're kind of competing and taking market share from others and bidding for higher terms and stuff like that.
So I don't know that there's something to that.
So what I'm hearing here is you need to find a way to grow through word of mouth.
For this to have any chance of success, and also retention needs to be very high.
Do you have a heuristic of what retention needs to be for you to have a chance building a successful consumer subscription business?
I think consumer companies tend to track essentially two main types of user retention.
There's more of the new user one, kind of D1, D7, et cetera.
I think when you have your D1 retention somewhere around the 30 or 40 mark, that's quite solid, I think, for a consumer app.
If it's much lower than that, then sometimes I might question, like the intent of the user or the ability for that you to, I guess, acquire just mathematically acquire enough users such that you can grow a big enough daily active user base.
That's surprisingly low.
Yeah, So it feels achievable.
It's achievable.
It's achievable in theory, but there are so many options out there in the market and people are feeling a lot of like app and product bloat.
And so just to be clear, you're saying 20 to 30% of people come back the next day.
Yeah.
30 to 40, 30 to 40, 40%.
I think you're in an okay place.
I think even more importantly and you know you mentioned Jorge to kick this off but, like you know, he wrote that very, very popular article uh, about the growth model right, and how, like current user retention rate was the biggest thing for them.
I think, especially if you have a product that has daily frequency, like that's actually the retention that matters the most is that, like of your existing user base that has developed a habitual pattern.
How sticky is your product and it's that retention rate that really compounds and build, that builds that daily habit.
So over time, especially when companies mature a little bit, you actually focus most of your energy on the existing user retention mechanics.
You find that that's a much, much bigger lever.
One exception is that Grammarly was a different type of product in that you install it and you don't proactively open it every day.
So that was kind of interesting to me, because I assumed that you should always just focus on existing user retention.
But for a product like Grammarly it's actually the activation installation aha moment.
That's really really critical and will carry the user for a very, very long time.
That makes sense.
Like yeah, the stats would show someone's a daily active user because they're typing things and that's not an accurate stat for Grammarly.
The other interesting trend I've noticed across successful consumer subscription products is they always start very scrappy and very cost efficient and spend efficient because I think it's because it takes them a long time to find something that's working and they're surviving on that margin of retention to growth cost essentially.
Yeah, that's right.
Yeah.
And the retention piece, that's such a good point.
It's like my newsletter is very much along these lines.
It's just like, how many people are joining every day?
How many people are leaving?
And it's a difficult treadmill to be on because people, you know, they want to save money.
They want to spend on Netflix and things like that.
So as amazing as you are, people are always going to leave.
So the trick is, how do you find more people coming than going?
Yeah, and i think um, just to take chesscom an example like i think, probably 80ish percent of our daily or weekly active users i mean, i'll check the numbers, but something like that would be like a current, a current user or an existing user and then a new and a like reactivated or resurrected user.
Those are actually about similar size for a company of our, our sale.
So even though there's a lot of attention on that new user experience, it's actually like pretty interesting that components of your active user base are actually not heavily weighed in the new user set after you mature to a certain degree.
Can you explain that a little bit more?
Yes, so after some period of time you kind of stack up a lot of inactive users in your product right, and you also stack up sporadic users right, people that may not have a daily habit but they will use it, you know, once or twice a week or once or twice a month type of thing, and so eventually that math sort of adds up where you have, let's say, hundreds of millions of kind of dormant users that are coming back.
And it's actually worth spending some time making sure that that kind of resurrected for lack of a better word experience inside the product is really excellent and that you find novel ways to try to bring them back.
Duolingo as an example, they did a good job of using social notifications.
And so if people would use like contact sync or something, you might get a push notification that one of your like best friends just started using Duolingo and that might encourage you to come back and resurrect into the product.
And when you resurrected in the product, it might be the case that your proficiency of the language you were learning, Like you were learning French three years ago, but now you like, forgot most of it, right?
And so when you open the app again, it encourages you to essentially replace yourself, like do another placement test and put you in the right spot.
And so some of these types of mechanics for a more mature company can lead to pretty good ROI, I guess, is what I'm trying to say.
Got it.
Like essentially so many of your, so many people have already tried in the past that To grow, you need to resurrect people that have been there.
And so thinking through, it's almost like a user experience for resurrected users.
Exactly.
Okay.
Let's zoom out a little bit.
You've worked at three of the most successful consumer subscription products in the world.
What is the difference between how these three operate?
I think there's many ways to be successful.
It feels like these companies are very different.
What's kind of the gist of how they operate?
Well, first of all, there's obviously a lot of similarities, but I'll just focus my answer on the differences.
So I think Duolingo, what struck me most working there is they're very particular.
They have an approach of product development that is infused across everyone in the company.
And they tend to, they actually wrote a playbook about this.
It's called The Green Machine.
You can look it up.
I tweeted about it.
It was one of my most successful tweets ever.
I just tweeted something about it.
Duolingo just released their playbook and I screenshotted the owl's butt page and it was like 5000 likes.
That's hilarious.
Yeah, so yeah, keep going.
But yeah, I mean the ethos of the company.
I mean they hire a lot of intelligent, energetic people out of college, basically.
And they give them a lot of amazing experimentation tooling.
And they care a lot about like the clock speed of the company, right?
So it's a lot of creativity, a lot of ideation.
The product experience of Duolingo actually like changes multiple times per day for each user, which is pretty shocking.
And so I'd never worked in a place like that before.
But it's really struck me about how consistently the company operated and they had specs and processes for doing like each of those steps in their product development cycle.
And they were really, really tight about it.
Okay.
So that's Duolingo.
Yeah, that's Duolingo.
Grammarly.
You know this is an interesting company because they started as a paid product oriented at students.
Then they expanded into more of a freemium model tailored to everyone, gradually focusing more on the professional side.
And then as they accumulate a lot more professionals, they realize, hey, there's patterns, right?
We're seeing that a bunch of marketing teams or a bunch of sales teams or a bunch of customer support teams or whatever, right.
Particular functions within particular companies were really adopting Grammarly at scale.
And so they were able to then layer on much more of a managed kind of enterprise-y motion.
And while I was there, I was focused on the consumer self-serve motion, but they weren't siloed right.
They were... they were intermixed with each other.
And so a big part of my job was not just to grow like the self-serve revenue and self-serve active users, but it was also how do you uncover kind of the right teams, the right functions, the right companies for like demand gen and sales to go reach out to.
So that was a very interesting, it's kind of product led sales work, right?
And it's really fascinating thing for me to learn.
And then, on top of that, with all the transformation going on with generative AI and even recently with them acquiring uh, coda and superhuman and becoming more of a productivity suite like the company is just evolving pretty rapidly.
It's a really exciting uh thing for me to be a part of and to see from the sidelines.
But that just made it at its core, kind of a different growth job than um than duolingo for sure.
Essentially a b2b business versus a very consumer business.
Yeah, and a lot more meaningful strategic decisions as well.
And then the core product team also.
You know I'm used to in growth, like laying out the entire user journey that a user go through, you know acquisition activation engagement, so on and so forth.
And typically...
Growth teams.
If they're well-resourced, they can do enough to move each one of these various levers right.
And it's just a matter of the sequencing of them and what you want to prioritize first.
But Grammarly was kind of unique in that the core product experience itself was what drove repeated activity right.
It's that...
I previously mentioned that current user retention thing.
What most drives, that is the frequency and the quality of the suggestions that you get every day right.
And so it was an interesting learning in that I staffed up a growth team, tried to work on this metric, and then I realized actually I'm kind of just getting in the way.
This is really a thing that the core product team most influences.
Let me have a conversation with the core product leader and then shift that over to them.
So yeah, just a super interesting experience. and then chess.com.
The thing that's most unique about chess.com is that they are super fanatical about chess.
Makes sense.
Crazy.
I mean, you shouldn't be surprised.
Obviously, the name of the company is like this, but they've always hired people from around the world.
The company's always been globally remote.
They just hire people that love chess.
They play all day.
They watch the streams.
Our Slack is always blowing up with people's chess moves and games and whatnot.
You know, I think I want to say this a little bit delicately but like Duolingo, even though the product they're providing is around language learning, I think the original ethos of how to start the company was really around motivation, right.
The hardest thing to its habits, right?
How do you build that daily habit?
And I actually, in many ways, see language learning as like their first vehicle, right?
And what they have a superpower in is that, again, the motivation, the habits, et cetera.
So that's kind of Duolingo.
And Grammarly actually kind of similarly, right?
Like, people know them for the spelling and grammar corrections, but what's really unique about them is they're integrated across tons and tons and tons of applications.
There's not many products that work like that.
That's really unique.
And so now, if you hear like to share their new CEO talk about like the AI superhighway and all that type of stuff right, they can now use that technology to provide a lot more than just grammar writing.
And so my point is just that like chess is about chess 100%.
It's in the ethos.
People are crazy passionate.
That just means we're always dogfooding the product.
There's just an amazing energy in the company to just use the product all the time.
Come up with ideas.
And I love that environment.
I think that's fun for me.
That is so cool.
And what I love about what you're saying is there's no right or wrong answer.
All of these companies are killing it.
I think Duolingo is worth like $10 billion, something like that, and keeps growing.
I'll look it up in a second.
And Grammarly is worth a ton, and then chess.com is doing super well.
So I think that's a really interesting takeaway here is you can succeed in a lot of different ways.
What's really cool about Duolingo I was just thinking as you were talking is it's just interesting that this very structured, methodical way of building is working so well.
Because you could listen to that and be like, I don't want to work like this rigid way.
But the fact that it is killing it tells us this actually works really well.
If you find something that works, lead into it.
That's right.
Yeah, the structure is rigid, but the ideas are the farthest away from rigid as possible, right?
Like you have seen their I don't know Super Bowl commercials, their memes, their gamification tactics.
Like it's a super fun, creative environment.
So like rigid is the farthest possible word to use.
But what I just mean is they're consistent.
They have templates for everything.
And like their product reviews are like 10 or 15 minutes.
It's just people go in and out.
So it's just kind of a surreal environment about how rapidly and consistently they work.
Awesome.
And they're worth $12 billion.
And they were much higher, actually, not too long ago.
They're coming down a little bit.
So, speaking of Duolingo, when people think Duolingo they think of the brand and the owl and the success they had on TikTok and things like that.
I'm curious to get your take on as a very growth-oriented person watching that work and your take on growth experimentation data versus marketing, viral TikTok videos mascots, things like that.
I used to think it was versus, but now I realize that they combine really well.
It could be rocket fuel for your growth.
Being a product person, I joined a lot of these companies because they're literally on the home screen of my phone.
I like using them.
I consider myself someone that's not easily swayed by ads or TV commercials telling me what to buy.
So I always had an element of skepticism on the marketing side for much of my career.
But then yeah, you join a place like Duolingo and you see how Duo the Owl has developed a personality through the push notifications and the product experience.
And then seeing the marketing team Leverage that personality in their TikTok and in their YouTube and all throughout social media and just feed into those like memes.
And then we would track back in the product experience.
Like, how did you hear about us and put all those channels in there?
And some days it would be like holy shit, like it's bringing in 20, 30 percent of like our new users in any given day.
So those two things really go hand in hand.
And that feeling has only been reinforced by Chess.com over the last five years.
The first 15-ish years of this company was really under the radar.
800 million people play chess around the world, but most of that is over the board.
Until recently, there wasn't actually that much online.
But five years ago, everything changed.
You had the pandemic.
You had Queen's Gambit.
You had a lot of like YouTube and Twitch streamers.
You had a bunch of kids playing it in school, et cetera.
And so it's really the combination of those two things that make it take off.
And it's like the growth experimentation is more the slow and steady or fast and steady, I should say approach, where you're just continually iterating.
You're making the product experience better.
But then every so often, right, there's a big wave that comes in.
You can quadruple your registrations overnights and you'd be a fool not to take advantage of that.
I was actually speaking at chess.com and playing chess.
I was at a coffee shop this weekend.
There's like a family a dad and mom and a daughter ordering, and the dad's sitting at the table and he's just like on his phone, just like opened up chesscom secretly and just playing while he's waiting.
Oh, man.
I will not admit or deny that I've done that before, but it's, you know, it's like that is.
If i could think of anything uh, more wholesome, i i can't like that's an amazing thing to be doing while you're just.
You know, my four-year-old can actually set up the pieces, which is pretty great, so he enjoys the game quite a bit.
Oh man, this four-year-old already a pianist playing chess.
That's right, what a stud.
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Okay.
You talked about AI a little bit here and there.
I want to follow that thread.
As a growth person, I imagine AI informs chess.com in a lot of ways.
It does.
So there's kind of like two buckets here.
How is AI changing the product, say chess and other places you work?
And then how is AI impacting your work as a growth person?
So pick one or both buckets.
Yeah, I'll tackle them in sequence.
I'll start with the chess one, just because I have maybe a slightly unique take on that one.
So Chess and AI, they've been intertwined for almost a century.
Like some of the early computing pioneers.
Like they just figured yeah, chess is an interesting game.
We can test machine intelligence and write some algorithms.
We're not.
And then fast forward to like 1997 and you had IBM.
They had their deep blue application.
Who actually beat the world champion back then, which was Garry Kasparov?
That was like a huge moment of like, of shock and reckoning, of like, oh man, is AI going to take over humans or we're going to have jobs?
And like all this stuff.
And this is, you know, 30 years ago, and thankfully we're all still here and more people are playing chess than ever.
Right.
And so the game of chess, and chesscom specifically, have learned how to augment, I guess, the human playing experience with the power of chess engines, which you know are definitely a powerful form of AI.
It's not LLMs to be clear, but.
There's engines like Stockfish these days that are just dramatically better than the top grandmasters in the world.
Is that where we're at?
I remember when it beat humans, and now it's just dramatically better.
It's dramatically better.
Wow.
Yeah, I think there's a rating system that compares relative skill level and an average chess player, somewhere like 1000, maybe 1500 on the high end.
A top grandmaster like Magnus Carlsen is like a 2800, and then Stockfish and similar engines are like 3600.
Wow.
And so to put that in comparison, yeah.
Like at least it's not 10,000 or a million.
I don't even know if that's possible.
No, it's not 10000, but it's similar to like if the chess engine was playing without a major piece, like a rook or something.
They would still be competitive against the best players.
And this is the ELO score?
Yeah, the ELO score, ELO rating.
Magnus is what you said, about 2,800.
Yeah.
And then the stock fish is, would you say 3,600, 3,800?
Yeah, and really it's like it's because computing power is so amazing and there's so many techniques for how to do like deep evaluation on specific chess lines.
They can calculate 10s of millions per second.
So it's not realistic for a human to compete against that.
But yet, like watching some of these chess engines played, has opened up a lot of creativity, new strategies, new lines, new appreciation for the game.
And our chesscom approach is that we can bring this technology for every user, even people that have never moved a piece before.
I talked earlier about that game review product.
That's exactly what this does.
So behind the scenes, we're running chess engines to basically spit out evaluations for every move that you make.
And then we translate that and make that approachable to the user using their native language, in plain, approachable style.
And even with audio and things like that as well.
And that part of it, like the personality, the speech back to the user, that part is LLMs.
And so I guess my point is that, again, chess and AI have been intertwined forever.
But for us, what's most important is that we keep the customer at the north star of it.
We're not just applying LLMs just because it's the new hot thing.
You've got to apply the right technology for the right purpose.
You know feature to provide value to the user.
And so we try not to ever lose sight of that and let hype get too carried away.
It's just really surprising.
I think people would not have expected AI can outbeat any human alive ever.
And chess is at an all-time high.
People want to keep playing and are playing more and more than ever played.
Not unexpected.
Interestingly, LLMs themselves are quite bad at playing chess.
They hallucinate moves.
They look at patterns.
They're very good at pattern recognition, but not so good at going super super, super deep on a specific chess thing.
And if you've even tried to look at chessboard images on ChatGPT, a lot of them have the wrong number of squares.
They're not set up properly.
And so I don't want to be too dismissive.
I'm sure it's going to get much stronger at reasoning.
And actually Google recently sponsored a tournament where all the top LLMs played a tournament against each other.
So that was pretty fun to watch.
They're improving, but chess is specifically a game that having a trained deep, deep computing engine is just going to be much much, much more powerful than LLMs.
And not to go down this track too far, but AlphaZero famous for beating the top Go player.
I imagine that was that trained specifically for Go.
Obviously not an LM, but that was a Go-specific model?
Yeah, my understanding is that the documentary is incredible, by the way.
I don't know if you've watched AlphaGo.
It's amazing how they took something so technically deep and made it so emotional and human.
But I think that's the crux of how we feel, I guess, about AIs and the products that we build.
But to your point.
My understanding is that the way AlphaZero is primarily trained is that it just plays a bunch of games against itself.
And so through the neural network, it just gets smarter every time.
And because it can have that repetition times a billion or a trillion, I don't know exactly what number, but it's going to get pretty damn good.
Okay, let's go back on track to where we were going.
So this was how AI is impacting chess.com.
How is AI changing just the work of a growth person?
I like to describe growth as the job is to connect users to the value of your product.
And in order to do that...
What I like to do is think about that user journey again and essentially staff teams that are oriented around each element of that user journey.
And those teams have specific metric goals, they have roadmaps, et cetera, and then they go run against them.
So that's like how it's structured.
AI, I think can be applied to speed up some elements of that essentially experiment cycle that you get through.
So one example is in product discovery.
As opposed to core product, which tends to have longer timeframes and you might do thorough user research or market research.
It's more complex, more foundational, more for first principles, etc.
Growth is a little bit less like that.
It's like you're running a lot of experiments and you're the output of any given experiment.
Is the input like to your next idea right, and so historically i don't even, i don't even mean historically, but just a few months ago right, like we were operating in a it's got kind of history, i suppose yeah um, But you know there would be a lot of manual writing of these like analyses, docs.
You'd have to read them.
You'd have to understand what insight you want to kind of grab from them and then write another spec to translate that idea.
That's still happening to some degree, but I think that's a spot where even tools like ChatGPT are super helpful, right.
You can just plug in like an analysis that another person wrote and just have it summarized for you and give you advice on, you know, ideas to go try.
And so that ideation, that research cycle is much, much faster.
I talked a little bit about prototyping also just becoming much, much faster than before.
We have not yet gotten to the point where product managers themselves are actually shipping the code into production, but it's dramatically shortened the amount of time it takes to conceive of especially a bolder idea that you might have.
And so when I talked earlier about explore and exploit, a lot of the explore was harder to do.
But now it's a little bit easier to do.
You can take a broader concept and visualize it.
And when you can visualize it, send it around the team, get people to click around it.
That makes a world of difference.
So those are just a couple examples that come to mind.
Awesome.
I want to go back to this phrase right at the beginning of this answer that you shared.
That, I think, is really helpful that you see growth as simply.
Your job is to connect users to the value of your product.
Yeah.
Can you speak more to that?
Because I think that's such a nice way of clarifying what is growth's role.
Yeah, it resonates deeply with me because I feel like growth sometimes gets this reputation.
I guess that it's just pure like metrics hacking.
We're cold people that just are trying to move a particular metric up and we're going to do whatever it can to throw walls and pay walls and add friction in all these spots.
And even though that could theoretically work at a micro level on a specific feature or a specific metric, I think what's most healthy for a company and I want to work at durable companies is to think about the user holistically.
And When you take that framing of connecting users to the value of your product, that value can change for a user over time.
And that also lines up really nicely to the journey, right?
Like someone that's not even a user yet needs to understand about the value proposition is super different than what a habitual user of three plus years might need, right.
And so the teams working on them should think from that perspective, And then from there they ladder into specific problems to solve hypotheses, et cetera.
Following that thread a little bit more people listening to this are imagining how do I get better at experimentation?
How do I run more experiments?
How do we do this better?
What are two or three tips and best practices that you think people need to hear or maybe are not totally aware of when they think about getting better experimentation on their teams?
I think the first thing is just start somewhere.
I just read this Atlassian state of product report and it was like 40 of product teams.
Basically don't run experimentation at all.
And there may be some good reasons for it.
I mean, it could be philosophical or maybe you're more B2B oriented or whatever.
So I get it.
But I think for a lot of, especially if you work on a consumer product that has some degree of scale, some degree of frequency with your product, you can collect enough data.
And also I have found, I can pattern match all day long.
I've worked a lot of companies, right?
But I'm wrong all the time.
And I think consumer behavior can be very fickle.
And especially when you work at a company, you become a power user naturally.
So sometimes you may forget like what the actual user experience is for a brand new user.
And so you leave a lot of opportunities on the table if you don't even try to experiment.
So I just encourage taking that first step.
Just run an A-B test.
Find a third-party tool or something that you can integrate quickly, or even just work with your engineers to spin something up.
Just get in the practice of, you know, crawled and walked and run type of thing.
Do you have a favorite tool, by the way, just to throw out?
Is there a go-to tool for you?
We used StatSig at Grammarly, and I saw that they recently got acquired, so that was exciting news.
Duolingo and Chess.com both have an in-house experimentation approach.
Pros and cons to either.
Obviously, Duolingo is an experimentation machine, and so it's been a huge accelerant to have our own thing specifically tailored, to be excellent at that.
But no, I typically don't encourage companies to build experimentation in-house from day one.
You know, at a certain scale, it can make sense.
And some of these companies, right, they were started 15 years ago when these tools weren't out.
So it was just something they had to do.
Something that you mentioned to me at chess.com, your goal is to run a thousand experiments a year.
You said you're at 250.
Talk about just that as a North Star.
Yeah, so part of having team members that are fanatical about chess is that the company can get pretty damn far just like building for themselves, building for the community and not actually being very experimentation and data oriented.
The problem with that is that you can have relatively lumpy growth, right?
And so part of the kind of excitement of me joining the company was to help smooth that out and bring in that experimentation mindset.
So prior to 2023, the company practically didn't experiment at all.
Last year they did about 50.
This year they're on pace for about 250.
And then next year we have that ambitious target of 1,000.
Did I make it up?
Yes, absolutely, I made it up.
But it's still a target and a thing for the teams to think about.
And 1000 experiments by itself, Like if you just did that but you didn't learn, you didn't make an impact.
That's kind of a waste of time, right.
The whole point of setting a goal is that you can have conversations about what would need to be true to actually hit that goal.
And so that leads to insights.
Like, actually we need not just product management or engineering to be running these experiments.
We can experiment with lifecycle marketing, changing copy of push notifications and emails.
We can experiment with app store screenshots and keywords and stuff like that.
We have all sorts of content marketing teams, et cetera, right?
We could have engineering enable no code for specific screens.
Think about our home screen or our pricing screen, where we might want to do a lot of just tests that are configurable without engineering support.
We might want to just like track our progress and look at it from time to time and make sure that we have the right you know observability around this.
So anyway, that's the stuff that really matters, as opposed to the you know, hitting that goal itself.
So don't tell the team, but I don't actually care that much if we actually hit a thousand.
But I think if we get pretty close and we accomplish some of these things, we'll be in really good shape.
We'll make sure none of them watch this.
I think chesscom is, and this is just such a cool example of a culture shifting dramatically from zero experiments to sounds like, two years later, a thousand, which is like three a day.
Like you know, that's you know there's many teams running experiments in parallel, but that's a lot.
What has helped you most shift that culture?
Is it just the CEO being like, this is the way we're going to go?
What have you learned about helping shift a culture from no, we're not doing experiments to a thousand experiments a year?
Yeah, I mean, definitely a lot of credit to the CEO and co-founders like Eric and Danny.
They're amazing.
It's not their intuitive way of thinking about growing companies, but their mental flexibility and encouragement to evolve and add this as a tool for the company has been awesome.
And they've been on the front lines preaching product-led growth and experimentation just as much as I have.
So I'm glad that you brought that up, because I think that is critically important for me joining a company to not be at odds with the co-founders and the existing approach of the company.
I think that's absolutely, absolutely critical.
I started this podcast with the example of the game review and the positivity and how that was shared.
I mean, I think those types of things are really what... motivate people, right?
They need to see this working in practice.
Wins.
Yeah, you need wins.
You got to celebrate them.
People feel good about the learning.
It's applied across the board.
Who's not going to be energized by that, I think, right?
So you can't just set goals in a vacuum and create it from top, right?
People have to see it working.
And when it works, the metrics move and you learn faster and you ship faster.
And that's a great environment to be part of.
What was the first experiment you guys ran?
Do you remember?
I don't know.
Before my time, actually.
Okay, okay, got it.
So they were already going down this track before they brought you in.
They had run some experiments.
Okay, sweet.
Are there any other key lessons that you think people need to know to be successful running experiments at scale?
The system matters just as much as any given experiment, probably even more, right?
I think starting with a growth model so you have an understanding of how your company grows in the first place and which channels you're going to leverage is critical.
You need to make sure that you are instrumenting your product in and out, otherwise you're gonna run experiments and have wonky results.
I won't name which company, but I was part of a company that had an in-house experimentation tool.
It's about three months into the company.
We're running like some experiments and we realized that user retention was actually configured backwards.
So all positive results were negative results.
So that was kind of embarrassing.
And that will never happen again.
Undo all those experiments and just drive up retention.
It's kind of weird.
Like we're seeing people use the features a lot more.
Why is user retention going down, going negative?
Um, so i have plenty of horror stories around that type of stuff.
But, oh my god, on the flip side of horror stories, you've shared a bunch of cool examples of experiment wins.
Is there another that comes to mind, if one you're really proud of or that was really trajectory changing, either at duolingo or grammarly or chess the?
So i already shared one at chesscom and one of grammarly.
I mean i could talk a bit about about duolingo as well.
Yeah um, Dual lingo.
And you had Jackson on the podcast, right?
Well, you talked about the streak.
Yes.
I also don't want to steal his thunder because I was going to think about that.
But the amount of learning through commitment and putting streaks on a calendar and just getting people started.
Right.
As opposed to achieving some large milestone.
That was huge.
I think we did something interesting.
We spun up a virality team.
And virality is this like really amorphous thing to me.
I think it's really hard to generate virality in your product.
But Duolingo is a product that is shared quite a bit.
And so we invested actually in some time to essentially add screenshot tracking for like a brief period of time in the app, just so we could find out the hotspots of where users were doing screenshots.
And you see this in other apps, too.
It's not necessarily some horrible thing.
But we did this for some period of time.
And we were able to basically articulate and say, OK, streak milestones is an obvious one.
Really funny challenges that you get in the Duolingo experience is also super highly shared.
Advancing in the top three of a leaderboard is another thing.
Anyway, so you can find these different moments where that's the case.
And then we staffed those moments with illustrators and animators and created these really delightful experiences around them.
And that worked amazingly well.
So, as opposed to going against, I guess, human intuition and trying to get them to share stuff that they otherwise wouldn't, on the margins, want to share like, lean into it more.
Actually like grab the moments where users are already organically screenshotting and make those much much, much better.
And you can kind of 5x or 10x and drive a lot of growth that way, too.
So that's not so much an experiment.
That's more a core product thing.
But, you know, it just resonated with me that that was interesting.
Well, it connects to your explore and exploit methodology.
Just find where, explore where things are happening and then try to exploit in a nice positive way.
You got it.
Speaking of that, you mentioned this with Duolingo is just very good at habit formation and motivation behavior.
Feels like Chess is good at this too.
You've worked at both these companies.
What have you learned about how to motivate people, how to create habits?
Again, Duolingo would not have started without this insight from day one.
They aim to focus on motivation and build a lot of these tactics.
Jorge actually had this model of gamification patterns having essentially three pillars to it.
You have the core loop, you have the metagame, and then you have the profile.
And so we actually thought about it that way too, where your core loop is your lesson that you go through.
You do a lesson, you get some rewards, you extend your streak and then the next day you get a push notification.
It's kind of the core loop of the product and making that really tight is super important because people need a habit to stick to.
Then you need a metagame, which for Duolingo is kind of like the path, but it's also the leaderboard achievements, kind of long-term things that you're going to strive to, such that you have, like long-term, I guess, motivation to continue doing the thing.
And then the profile is also critical because you build up a profile over time.
It's a reflection of your investment inside the product experience.
And so when you nail those three things, you can end up with a long-term learning journey that can be quite successful.
And then to flip over to the chesscom side, like what we see is that over 75 of our new users they classify themselves as, like I'm completely new to chess or I'm a beginner.
And unfortunately, if you're new to chess and you're a beginner, you're not going to have that fun of a time playing live games.
We see this in the data.
It's like less than a third of those users actually win their first game.
And when you lose a game, user retention is 10% worse than when you win a game.
That's not so bad, but at scale, that's bad.
Yeah, and it could be worse.
That's true.
And so typically, what a lot of mobile games will do is they'll just create a super simplified version of the game.
It's harder for us to do at chess.
And so, without changing the rules of that, I think that I don't know.
It's just very eye-opening to me that When you're trying to learn something, whether that be language learning or chess or whatever, usually those first steps are fraught with a lot of self-doubt and reinforcement that you're not good at the thing.
And so it pays to be very intentional to craft experiences that guide the user around that.
Well, I can't help but ask, is there anything that helped that along?
Yeah.
So like something we're experimenting right now is just like clearly, if you say that you're new to chess, we're going to craft a more delightful learn how to play experience as opposed to dropping into a live game.
That's an example.
Another is like hiding your ratings for the first five times, such that you're not seeing your rating kind of plummet.
So there's a lot of tips and tricks you can do.
I'm just imagining a little guide that's like, here's how you win.
Yeah, or play against a coach, play against a friend, play against a bot.
There's a bunch of different avenues you can take.
What I'd love is play against someone real and then here's where you should move.
Just like here.
We're going to help you win.
Like a hint in real time?
Yeah.
Well, I don't want to be playing you then.
Okay, let me ask you a couple more questions.
One is just zooming out a little bit.
What's the most counterintuitive lesson you've learned about?
Building products or building teams across the many companies you've worked at?
Yeah, I've talked a lot about products, so maybe I'll flip to the team side for a bit.
I think the standard way to hire and build a team is you fill out a JD.
It's got a whole bunch of different characteristics that you're looking for.
You typically will find... you know, a short list of companies that are kind of similar to yours.
And then you try to hire for that.
Right.
I think that's kind of the typical default path that, that a lot of companies take.
And I was really struck by you know my experience working at, you know some smaller startups or you know, take Duolingo as an example where um, Over and over and over, I saw some of the highest performers just being people that had very high agency, had that clock speed, had that energy.
Yes, they cared about the mission, but they didn't necessarily need to have deep experience on that matter.
And in fact, sometimes that experience could be a crutch in certain ways, especially in this world where the grounds are shifting so fast with AI.
A lot of your learned habits actually need to be intentionally discarded.
You need to have a beginner's mind on this type of stuff.
So I think this is more true than ever like looking for people that respond and move quickly and think you know just faster and move faster right.
I think speed the fastest speed of learning.
Those types of companies are the ones that I want to bet on, because I think those will end up surviving and thriving.
Yeah.
So just to double click on this, this idea of high agency is very trending these days of just like higher, high agency people.
To unpack that a little bit, you mentioned a few of these traits.
So let's just help people see what you see.
So one is clock speed.
Just, they think fast, they move fast.
Uh, they learn fast.
What else?
What else do you look for that helps you see that they're high agency people?
Yeah, i mean, a lot of it actually happens outside of the interview process, interestingly.
So a lot of it is uh, you know the types of questions they asked.
Have they actually tried your product and gone deep into it?
A lot of it is the you know.
It's the references.
It's the communication that they have to even set up your interview.
It's the energy they bring into the conversation.
You can actually pick up a lot of soft signals on some of these traits.
Yeah, over time, you kind of pick up on some of these patterns.
I don't know that I'm perfect at it, but I've learned to balance those things quite a bit more than I did in the past, when I would just purely read from my questions in my rubric and not care about anything else.
Yeah, there's like a vibes component to it.
This is also kind of support for the work trial way of interviewing versus just a talk interview where you have them actually work with you for a week or whatever.
It's a great point.
Okay.
One other question I wanted to ask you.
You've worked at a bunch of different sizes of companies from startup to Grammarly.
I don't know if you'd call it a big company, bigger company.
Duolingo's.
Duolingo's.
I don't know.
How big is Duolingo's?
They're about a thousand people.
Okay, cool.
Yeah.
But I worked at Google too to start my career.
Oh, right.
Okay.
What have you learned about just the size of company that makes you happy?
What have you learned about just helping other people that you talk to decide what size of company is good for them?
I definitely believe that everyone has a company stage that they shine best at.
I've personally gone through this journey of big tech to like tiny tiny, tiny startup, then landed in the middle, which I consider like my own goal lock zone.
I talked earlier about, like what actually gives me personally, a lot of energy is seeing across a company's efforts, but also the company being small enough that i can get into the details.
I can work with the specific teams, i can read experiment, you know results, i can look at the pixels, and so i find that the balance, those two things tends to fit best with medium-sized companies, but that's me right, i think.
At big companies uh, like a google, You're dealing with immense scale, which is interesting by itself.
You learn a lot of best practices from your peers.
They have all the kind of tools and functions that you would possibly want to go learn from, but they can tend to move slower and it's harder to kind of ship things and get them out the door, which eventually drove me nuts a little bit.
On the flip end of the spectrum, these tiny startups, they move incredibly fast.
But I grew all my gray hair from those tiny startups because no one knows about your company.
And so you're recruiting people one by one.
You're trying to get users one by one.
So yeah, you can learn fast and ship a lot of things.
But if you're trying to make a big impact on the world, it can be actually pretty grueling to do so at really really, really small startups.
Now, some of them do hyperscale and make it out.
And obviously...
I'm not one to trash that because that's the path that I tried for quite a while.
But for me I really like the zone where I can contribute at scale but also execute at a pace that's more on the daily and weekly scale as opposed to monthly and quarterly.
And when you say medium, what size of companies is that roughly?
Yeah, so these companies that we've talked about in the podcast are about 500 to 1,000 people.
Typically, these companies will have been around, let's say, 10 to 20 years.
They're durable, ideally profitable.
They have a good leadership team.
But there's still a lot of dimensions to go figure out.
A lot of them are in key inflection points.
So they're certainly not stagnant, right?
You need to find a place that's dynamic, too.
Interesting.
10 to 20 years old.
I don't know.
Not many people would feel like that's where I want to be.
I love that you found a number of companies like that that you enjoyed working at.
Last question.
And this is going to be taking us to a recurring segment on the podcast that I call Failed Corner.
People hear all these stories of all these experiments on all these companies that worked out.
They're all killing it up into the right.
In reality, you've touched on this.
A lot of things don't work out great.
So can you share a story when something went wrong, when you failed and what that taught you?
First of all, in the growth world, you're failing all the time.
So I'm not going to pick a specific growth story because those don't actually get my ego too much.
But earlier in my career, I did a lot of core product work.
I worked for this startup called Chariot.
I don't know if you ever lived in San Francisco.
Yes, it was like the bus.
The blue commuter shuttles, like 15-person shuttles.
They would essentially drive from... various neighborhoods into downtown San Francisco.
It's kind of a commuting use case across between the public bus system and an Uber and Lyft.
So I was there for some time.
I led product there.
And the core service was really loved by its users.
It was reliable and fast and affordable enough.
But we got pretty interested in this idea that maybe we can improve utilization.
Maybe we can make the service a little bit more innovative if we offer dynamic routes more similar to Uber and Lyft.
How could we...
Like the drivers are driving these fixed routes, but you know, if they have spare time they can go out of their way.
Go pick up somebody, like at their house or something, and like, keep going.
So we tried this.
We called the chair direct.
Really interesting attempt, but I learned a lot of lessons there because ultimately it didn't work out.
One lesson is, like, this was kind of a solution searching for a problem.
Like you never just purely want to chase a.
Like you know, wouldn't it be nice if we did this as opposed to?
You know, this is our user and this is the problem that we're solving.
This is why it's going to delight them, et cetera.
That's kind of one.
Second is...
You got to consider, especially in these more like marketplace type businesses, there's more than just one end user.
And we focus so much of our attention on the writer app without realizing oh yeah, the drivers are carrying a lot of the brunt of this experience.
And our operations team is as well.
Right.
And so when the drivers are confused or disgruntled, that can lead to a challenging experience.
Uh, overall experience for for the product.
Right.
So that's definitely another one.
And the third one is like we did a lot of actually PR uh, prior to the service going out, just to get the word out.
Um, and you know, PR has had this time and place.
Uh, but I think doing it before you have validation that customers definitely want the thing is quite risky.
It can lead to a lot of sunk cost once you get it out, because you're just you know you need to see it through.
You want to see it succeed.
So yeah, this is a decade ago.
Honestly, I had a great time at that company, but I still remember that vividly because it contained, you know, three or more kind of key lessons that carried forward as I have built many products since then.
Yeah, it feels like you went to the complete other end.
Run experiments of everything before you tell anyone about it.
That's right.
I remember the chariot bus showing up at the Airbnb office and people getting them like what the hell is this?
That's right.
Very cool.
I didn't know you worked there.
Alpert.
We've covered so much ground, everything I was hoping we'd cover.
Is there anything else that you wanted to cover?
Anything else you want to leave listeners with before we get to a very exciting lightning round?
No, this is great.
I hope it was useful for your listeners.
I will say, over the last few days, as I was prepping for this, I was honestly a little bit anxious about do I have enough deep independent frameworks that I need to come up with?
But just being authentic to my actual experience at these companies, you know a lot of my lessons learned have been off of the backs of other people that have tried, you know, similar things and have succeeded or failed.
And I think what's important is that you have that, like, your mental sponge, right?
You can try a bunch of different things.
You can absorb them and then put them in practice right away.
Discard the things that don't work, right?
And evolve them for yourself and for the company's needs.
Um, and so I, I don't know.
I think that was just a realization that I had as I was, was thinking through this podcast.
And I think that's partly why I haven't done too much public speaking.
I know exactly what you mean.
I was just like, what have I actually learned?
I've never just sat down and thought about here's the thing I've learned.
And that led me to writing this medium post.
That did really well what i learned at airbnb and then that basically led to what i do now.
So there's a lot of power and, like i love that, this is the excuse to make you think through.
What have i learned concretely that i can share?
That's right.
Thank you for that.
So at the beginning of this podcast, before we started recording, I always like to ask guests what is your goal?
What do you want to get out of this conversation?
And usually it's like we're hiring, we want to make sure people know about our company or we want to get the users.
And your answer is just, I just want to give back things I've learned, which I love.
That's it.
And you've done that.
With that, we've reached our very exciting lightning round.
I've got five questions for you.
Are you ready?
I'm ready.
What are two or three books that you find yourself recommending most to other people?
Yeah, the truth of it is I have not just a four-year-old, but I also have a one-year-old.
So most of the books that I'm reading these days are kids' books, trying to make them laugh and all that.
Wait, any favorite kids' books?
Because I have a three- or two-year-old, sorry.
Well, you said that you started singing.
There's a book called Snuggle Puppy that has a song in it that just makes my daughter crack up.
Oh, my God.
That is heartwarming for me.
Okay.
No, like a book that I recommended recently at work is Ogilvy on advertising.
Do you know this book?
I don't know the book.
I've seen his like tenets of marketing.
Yeah, it's interesting.
So it's 40 years old, but it's just packed with a bunch of different practical examples about copy and creative that work in.
You know these are old school ads, right.
But...
He took a very experimentation-oriented approach to just try a lot of things.
I think in the book it makes a good reminder that what ultimately matters is to compel your users to some action.
For him, it's like buying a product, right?
It's not about just creating clever ads or sexy creatives.
It's to do things that compel that action.
I think that's very true for many of our product and, you know, lifecycle teams.
And so I shared that around as an interesting recommendation.
Is there a movie or TV show?
Sorry, were you going to share another book?
Yeah, actually.
So yes, please.
My our co founder at chess.com.
His name is Danny wrench.
And he is quite well known in the chess circles.
He's releasing a memoir called dark squares.
And it is super fascinating.
He grew up in an abusive cult and was a chess prodigy.
And so it is just this like, Unbelievable story, and I'm about halfway through it.
It's a reminder that sometimes the people that you work with you don't realize how deep their pasts go.
But this is something else, and I think it should be out by the time this podcast releases.
And it's called Dark Squares.
Dark Squares.
Which is a reference to the chessboard and also, I imagine, the difficult past.
Exactly.
Wow.
How cool.
Okay.
Are there a movie or TV shows you really enjoyed that you recently watched?
I mean, these days it's football season.
So I'm consumed by all the hot takes of my favorite teams that I love and the teams I love to hate as well.
Who's your team?
The 49ers.
I have season tickets and I go all the time.
We had a rough season last year, so hoping to turn around.
Okay, very cool.
Okay.
Is there a product you've recently discovered that you really love?
Yeah, so last 20 years of my life, roughly.
I've moved around a lot, but I've always been within walking distance of a coffee shop.
It's just like a ritual that I go and get coffee and it starts my day right.
Two years ago, I bought a house.
And for the first time ever in my life, I'm like not by a coffee shop.
And I was so depressed about this for a little while.
So my favorite product is the Bread Bowl Barista.
And it just starts my day off right.
I like making horrible latte art with it.
And I think it's just a reminder.
I don't know.
Like the products that most impact me, I guess, are the ones that I use all the time.
And it's a daily habit.
And then the most caffeine.
You got it.
Amazing.
Do you have a favorite life motto that you find yourself using in work or in life?
As I was thinking about my piano stories, I also remember that my mom used to have a quote that she just said like nothing is more important than your reputation.
And she used to say this, and I think the charitable understanding of this is that a lot of the small decisions that you make each day, how do you treat people?
How do you show up?
What's your character, et cetera?
They can compound and they open doors for you and many surprising and amazing ways, right?
Like a lot of these companies that have actually joined, have come through relatively light connections and even just being on this podcast right.
I think I've seen a number of folks that I've worked with before be on this show.
And so I think You know doing the right thing building a good reputation that can carry you a long way.
And the flip side of that is, you know, reputations are fragile, too.
Right.
So if you do the wrong thing, take a long time to repair that.
So I don't know.
It just stuck with me my entire life.
I thought that was a interesting life motto.
Last question.
York at chess.com.
How's your chess?
Terrible compared to serious, serious players, but quite good compared to the casual ones.
Yeah.
Okay.
My yellow rating is about an 1800 for rapid games and about 1500 for blitz.
Yeah.
But I play many times every day.
Blitz is like fast chess.
Blitz is like faster chess, kind of three minute games.
Rapid is more like a 10 minute game, which is still pretty fast, but.
And you say you play multiple times a day.
Do they make time?
They do.
Okay.
Like at Patagonia, there's a famous book that found a road called Let My People Go Surfing.
And the rule at Patagonia is you can go surfing if the waves are great.
Is that how it works at chess.com?
Absolutely.
Okay.
Chess is always fun.
So we play all the time.
And they even have chess coaches like on staff.
On staff?
Just like you can book.
You can book.
So I get biweekly lessons and it's helping me improve.
Wow.
Okay.
This is going to drive a lot of hiring for you guys.
Saved it for the end.
Albert, this was awesome.
Thank you so much for doing this.
Thanks so much for giving back and sharing all these stories.
Two final questions.
Where can folks find you if they want to follow up on some of this stuff?
And how can listeners be useful to you?
Yeah, thanks for having me.
This is great.
You can find me on LinkedIn or Twitter.
Not a super active poster, but I read it all the time.
If there's something that I said today that resonates with you and you just want to get in touch trade notes feel free to reach out.
And can they play with you?
Can they find you on Just.com to play?
They can.
Okay.
Do you want to share your username or you don't want that?
I'm happy to.
I just mentioned that I'm a 49ers fan, so my username is GoNiners.
Wow.
I'm sure I'll get a lot of game requests.
Here we go.
1800.
Okay, Albert, thank you so much for being here.
Yeah, thank you so much.
Bye, everyone.
Thank you so much for listening.
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