We don't want to become same as previous generation of of the editing suite.

So instead, let's solve it on the research level where it will know based on the voice exactly how it should speak with the speed.

To be able to cut to all those different use cases, you need such a big array of different voices, different languages, different accents, um different styles.

Um so we launched voice marketplace where you you could create your voice and then uh share it.

And when that voice is shared, you earn money in the return.

Today we have almost 10,000 voices. We paid $10 million back to the people in the community.

There's some crazy stories from the voices.

Just speaking through exactly the technology, showing the examples and kind of avoiding this initial knee-jerk reaction that AI is bad has has been has been tremendous.

>> Um, I'm excited to welcome our first speaker, Maddie, co-founder and CEO of 11 Labs.

All right. So, good to have you here, Maddie.

>> Thanks so much for having me here. Great great to see everyone and good morning.

>> And that that was uh the walkcom music generated by 11 Labs. Was it?

>> It was. We we we expand continuously across the audio space.

So we started with voices then created orchestration of how to build voice agents and now also create a fully licensed music model so can produce amazing music to go alongside with it.

>> Awesome. We'll talk about all all about that. I've um had the opportunity and also uh the luck to get to know from the very early days when 11 Labs got started and get to partner over the last three years to just see your execution everywhere from product launches to shipping new lines and models like you just mentioned everything from uh text to speech models speech to text and then we uh started uh doing music sound effects and now the AI agent platform um I'm very curious first I'm still in awe of the the shipping speed uh after all the three years.

Um but I want to ask how do you actually maintain both the speed and quality when you have such expensive product road map?

Um but I want to ask how do you actually maintain both the speed and quality when you have such expensive product road map?

>> So f first of all we we partnered almost three years ago and um so it's it's uh great to hear all the kind notes but also what they didn't realize when we partnered the infrastructure team was free people and of course now I'm 11 loves founder.

love number 11 and the company infra team is 11 people so we seeing the growth of the other side as well um and and I hear that the companies here raised $66 billion in the total fundraising so the number 11 is everywhere here

I hear that the companies here raised $66 billion in the total fundraising so the number 11 is everywhere here

but the I think that to start off I think first piece I have a I think the smartest person I I I got to know as my co-founder P who who has been the research brain for creating a lot of the the models and then being able to assemble what who we think are the most incredible researchers in the voice space to really create the first text to speech model that could understand the context in a better way and turn that into the emotion inonation.

Then find a way to um capture the characteristics of the voice.

So you have the voice sound uh uh with the right style with the right age with the right gender dialect everything in in one.

Um and then the researchers across of course now expanded that to speech to text music and other work.

So that's our foundation and then the way we structure it to be able to ship quickly especially with so many things uh happening in AI space is a lot of small teams.

So today we have roughly 20 product teams each of five to 10 people size which with full independence can go ahead and ship products.

Of course that carries some of the um uh um sometimes issues of duplicative work or sometimes uh people going uh um at different speeds.

But at the positive end, the ownership of each of the teams is extremely high.

So people know that this is down to them to really deliver and ship.

Um and it allows us to move extremely quickly.

Um we bucket our work into creative space.

So creative platform where we help with narrations, voiceovers, dubs for for creatives and creatives in the in the media entertainment space.

Um and um and then on the agent side where we help people recreate voice agent experience, conversational agent experience across customer experience all the way through to immersive immersive media.

>> Great. 11 Labs has a labs name in the labs in the name uh very similar to many of the other big labs which means you're doing your first party R&D and model development but also building all these 20 products.

How do you think about balancing both like keep progressing on the model research but at the same time not delaying sort of the product launches?

Yeah, it's very tricky.

I'm sure many of you have the same thing like do you do you build a product while uh when you don't know if the research innovation will displace the product you just built.

We had this in the early days too.

So one of the simple examples was we um we we had a model at work and one of the most common requests was could we do a different speeds for voices.

So could you have additional slider to modify the speed of how audio gets generated and how quickly it speaks and we are very against this idea of like no we don't want to do any sliders any toggles we don't want to become same as previous generation of of the editing suite.

and we are very against this idea of like no we don't want to do any sliders any toggles we don't want to become same as previous generation of of the editing suite.

So instead let's solve it on the research level where it will know based on the voice exactly how it should speak with the speed and um and we resisted this for I think good amount of nine months and we couldn't solve it on the research side and then the product was super simple solve that got all the all the users across and now the approach we take and like looking at this is if we think the research work will take more than three months then the product is um can do any any anything they want to to start um adding other models adding some of the extensions.

and we resisted this for I think good amount of nine months and we couldn't solve it on the research side and then the product was super simple solve that got all the all the users across and now the approach we take and like looking at this is if we think the research work will take more than three months then the product is um can do any any anything they want to to start um adding other models adding some of the extensions.

and now the approach we take and like looking at this is if we think the research work will take more than three months then the product is um can do any any anything they want to to start um adding other models adding some of the extensions.

Of course, sometimes the timeline is is tricky to predict, but roughly the guidance we have from our internal research team. What are the initiatives we hope to ship this quarter? What are long-term initiatives?

And then for anything long-term, you can use any any other work to close that gap and make it and make it better.

I guess first uh you kind of have to figure out if the research commitment is going to meet the the timeline first and then go on to align with the the product teams.

Um that that make a lot of sense.

Um, as everyone is moving to San Francisco and building in person and locked in, uh, like in the same space, 11 has always been building globally and having people more distributed, but you now have centers, I guess, in different locations from London, Warsaw, San Francisco to New York and and other places.

um how do you think about building this global expansion and finding talent um globally versus I guess the trade-offs of building at in the same place?

>> Yeah. And we so we started uh so me and my co-ounder Polish we started between Warso and London at the time.

Um and we we we I think 11 Labs wouldn't have existed if we weren't starting from Europe.

Uh it's a very peculiar thing but in Poland if you watch a movie in Polish language like a foreign movie in Polish language all the voices whether that's a male voice or female voice get narrated with one single character no emotions no inonation as you can imagine it's pretty terrible and it's still happening today for most of the content out there and

>> I've had a similar experience growing up in China that we have a lot of western movies dubbed in Chinese monotoned

>> so bad so bad and it's like in in Poland

of course postcommunist country, it's a cheaper way to do it.

You don't have to hire as many people.

You have one monoton um audiobook reading of of a movie.

And um and that was kind of where the company started.

And we started initially in in Europe.

And we realized that if we want the best people to solve what was a research problem at the time, we need to hire wherever they are.

And um we couldn't lock ourselves to just San Francisco or uh or or look at the West Coast.

we we knew that we need to find them across Europe, across Asia and bring them into the company.

So we started fully remote and um and started looking at those those those people and then on engineering we also were very against this uh traditional hiring method of looking at LinkedIn looking at traditional uh traditional background and trying to figure out could we go and and and figure out a different method to to hire people that led to some very interesting hires.

and then on engineering we also were very against this uh traditional hiring method of looking at LinkedIn looking at traditional uh traditional background and trying to figure out could we go and and and figure out a different method to to hire people that led to some very interesting hires.

So we hired a person that uh had a incredible open-source texttospech model and was working in the call center at the same time as a recipient of the calls to make money.

>> Wow.

and uh and he's now in the team, one of the most brilliant researchers we have uh doing all the data processing.

But um the the the same pattern kind of followed and of course the early team was very distributed and then as we started scaling so beyond 30 people we realized that the new people joining there's benefit of them having a space to um to be next to others to get deeper into the culture understand how what are all the projects that are happening in the company.

We started the hops where you can go into London and Warso and San Francisco where you can work with others in person and that's how we try to like marry those two.

If you are early in your career you can you we we we try to hire you in the hub so you can immerse yourself in the company.

If you are used to remote work completely fine uh but then if you want you can always come and join us in the in the hub and that worked really well.

Currently we continue like hiring very untraditional backgrounds in some of the place of the company and then fusing that with very traditional backgrounds which can teach the others and u and in sales for example we we we we've done some of those experiments too uh where that combination worked really well

and in sales for example we we we we've done some of those experiments too uh where that combination worked really well

>> the lesson is you can really find talent everywhere uh it just how hard and how you look for them

>> and I think in Europe also people like this was a interesting one in US people are very keen and excited to to to work and if you if you go for any social event it's it's like you want to talk about work and in Europe I didn't have this feeling where it's like most people don't want to do that it's like the cultural piece is different but then you do have the pockets of people that actually strive it too they just don't have the companies where they could do that in so I feel like our our team from Europe is is the most motivated and passionate set of people that that that that uh that we are lucky to have.

in US people are very keen and excited to to to work and if you if you go for any social event it's it's like you want to talk about work

and in Europe I didn't have this feeling where it's like most people don't want to do that it's like the cultural piece is different

but then you do have the pockets of people that actually strive it too they just don't have the companies where they could do that in

so I feel like our our team from Europe is is the most motivated and passionate set of people that that that that uh that we are lucky to have.

>> Yeah, I can attest to that given I've met some of them.

uh very hardcore, very good work ethic for sure.

And you have also maintained a pretty flat or structure um and have people own quite laterally a lot of um responsibilities.

Can you talk about the rationale behind that?

And I guess there was also a no title policy.

>> Yeah, so we removed titles a year ago and then um and it's it's going well.

it still works and and I do think we you know we I we we said we did it but I thought a lot of AI companies kind of do it too already with member of technical staff being like a the usual piece you have for engineering and then in a lot of the go to market you are just go to market not VP of sales or other roles so I think it's it's actually a a pretty common pattern but in our case we we we had a small team approach where you have extremely small amount of people usually the five to 10 and um and we wanted to make it very clear that every team we we we create those teams.

so I think it's it's actually a a pretty common pattern but in our case we we we had a small team approach where you have extremely small amount of people usually the five to 10 and um and we wanted to make it very clear that every team we we we create those teams.

You have six months to prove it.

If it's proven, that team will stay and continue working.

But it really is that the moment you join, you can have any impact on the company.

So you can have any role in that team.

The tenure will not define your position in the kierarchy.

If you are smart and quick and passionate, you can you can you can elevate yourself very quickly, which this this really this really helped.

And also it's a common um common layer to the external world where everybody looking at 11 labs knows that we are the go to market team is go to market team.

There's no like uh positioning to to the to the same extent.

What this allows us to do is I think when we speak with a lot of our partners with a lot of our customers um they also know that uh that they are getting the the best people um and always and and we can also send people to to different conferences different events regardless of that that positioning.

Um I think the tricky thing in the flat structure is not only positives in the way we currently have it's it's a set of leads effectively for the subdivisions.

So the research creative work agents work go to market self and salesled um and of course ops um

only that's the layer of leads and then under that there's pretty flat small team approach across across the world.

Um but then you really want the leads to be able to carry the complexity around the team.

So suggest things between one team to another if they see that there's something valuable between them happening.

Um so I think picking those those people that can context switch between is super important and then letting the team fully focus on on that.

Um and then having which is uh which was interesting learning where if you if you put a person into all the Slack channels and give them transparency they actually get frequently distracted because then they read all the messages.

You can still choose not to read them but they still they still do.

So you kind of need to cut the access to a lot of those pieces to force the attention and that kind of works.

All those small things works work really well.

>> Maybe we can borrow some of that lesson too.

Um let's switching gear a little bit.

Uh you're you're on the front line seeing a lot of the creative work whether it's from uh art, music or advertising that are starting to adopt AI tools.

And in the beginning that was not the case. there was a lot of resistance and now we're just seeing the the adaptation and the the welcoming of using more of the generative AI tools including you know AI audio um and you have done some really smart things from the marketplace payouts to like working with these creative industries since day one actually um I remember how much you stressed like we have to find a way to work with them and sort of um observing sort of market shift over time so the question is um how do you actually adapt to these changes and find the ways um to to work with the industry in the infancy in the beginning and how did you navigate some of the challenges in that

and you have done some really smart things from the marketplace payouts to like working with these creative industries since day one actually um I remember how much you stressed like we have to find a way to work with them and sort of um observing sort of market shift over time so the question is um how do you actually adapt to these changes and find the ways um to to work with the industry in the infancy in the beginning and how did you navigate some of the challenges in that

um I remember how much you stressed like we have to find a way to work with them and sort of um observing sort of market shift over time so the question is um how do you actually adapt to these changes and find the ways um to to work with the industry in the infancy in the beginning and how did you navigate some of the challenges in that

so the question is um how do you actually adapt to these changes and find the ways um to to work with the industry in the infancy in the beginning and how did you navigate some of the challenges in that

and how did you navigate some of the challenges in that

>> the um so I think the first piece is is uh is actually spending time with the industry and trying to understand what are their priorities their incentives uh of course it's sometimes tricky sometimes you you then end up being starruck we had a honor and pleasure to work with Jared on on some of his incredible work and uh and learn from him on like what is important in the like which parts of the production process you can actually use AI which ones you want to keep um where is it actually helpful um uh uh and so so I think that's the super important thees across all the partnerships in the space in our case we we try to figure out how to do that on the on the voice space which is of course with that technology a how will the voice acting space look like in the future and then too.

of course it's sometimes tricky sometimes you you then end up being starruck

we had a honor and pleasure to work with Jared on on some of his incredible work and uh and learn from him on like what is important in the like which parts of the production process you can actually use AI which ones you want to keep um where is it actually helpful um uh uh and so so I think that's the super important thees across all the partnerships in the space

and so so I think that's the super important thees across all the partnerships in the space

in our case we we try to figure out how to do that on the on the voice space which is of course with that technology a how will the voice acting space look like in the future and then too.

Of course, to be able to cut to all those different use cases, you need such a big array of different voices, different languages, different accents, um different styles.

Um so, we launched voice marketplace where you you could create your voice and then uh share it

and when that voice is shared, you earn money in the return.

Today, we have almost 10,000 voices.

We paid $10 million back to the people in the community.

There's some crazy stories from the voices.

our one of our first voices was a a deep Spanish voice and the magic of the technology is that the same voice now is available on all different languages in the same way.

So it's 30 different languages at the time. Now it's 70 but 30 languages at the time

and we had the Spanish voice join us and it wasn't picking up on the Spain. Nobody really liked it as much

and then it picked up in an English-sp speakaking country that same voice because of that deepness and now it's our top three voice for all the use cases.

So uh hidden messages you can all register to our voice marketplace and maybe earn some money too.

Um the so that's the the I think the second important thing is like figuring out how we can be part how we can bring the industry together to disrupt together rather than just to disrupt and with labels I think I'm still learning how to uh interact the so we we've worked with labels uh the the Merlin and Cobalt so four of majors to bring their music into the music model so we can do it in a licensed way.

and with labels I think I'm still learning how to uh interact the so we we've worked with labels uh the the Merlin and Cobalt so four of majors to bring their music into the music model so we can do it in a licensed way.

so we we've worked with labels uh the the Merlin and Cobalt so four of majors to bring their music into the music model so we can do it in a licensed way.

So you can generate that and give commercial rights so you're fully protected.

Uh that was a hard process.

It took us 18 months to figure out the agreement that works.

And in the end I think the main thing uh was was adding set of forcing functions or forcing timings to um to find a a effectively a trigger of like okay this is when we do it and we either do it together or we do it separately.

and um and those forcing functions really help add urgency.

Then we we needed to move that forcing function a few times but but it still worked to to a large extent to to go after that.

And then two there's of course the you know the finding the compromise wasn't wasn't wasn't wasn't easy.

Um but then in our case working with the with the with the labels there was um kind of protecting what they are caring about and they of course also care about how um how they continue doing well by their members by their artists that they work with.

Um so we would spend a lot of time working with their members speaking about how we think about technology what's going to happen in the next couple of years and that really helped.

So just just speaking through exactly the technology, showing the examples and kind of avoiding this initial knee-jerk reaction that AI is bad has has been has been tremendous.

>> And maybe tying back to the earlier question as you are navigating like this landscape. um how do you think about like bringing the right talent that can head and lead some of these functions and these are mostly unknown territories of how to navigate it like where have you been seeing um success in bringing the right people?

these are mostly unknown territories of how to navigate it like where have you been seeing um success in bringing the right people?

>> So here for the spaces that are kind of completely new to us. So this and like legal is another example.

We would always kind of bring at least one or two people that were in that space that kind of have interacted with the same parties uh full-time in the past but then would actually um uh uh adjust that with a lot of consulting uh people that would help us in a specific conversation.

So in this case in music we had uh music lawyers that worked very closely with us that consult across a few of them and the good thing is that they know all the players and they effectively were this um uh bridging gap between between both of us.

and the good thing is that they know all the players and they effectively were this um uh bridging gap between between both of us.

So so we could speak the same language and um and then that was that was that was really helpful.

>> Yeah.

And you have had um a very specific taste for people that are risk uh tolerant enough and also understand the commercial business opportunities to you know help guide the right chain of actions in each of those domains.

I found that very fascinating

>> 100%.

I mean legal I'm I don't know how many of you are trying to find a first legal council or have a number of those.

For us, this was the I think one of the trickiest roles to hire for because you are um hiring into the space you don't know you know very little about.

And then uh and then we had a like the first couple of legal people that that were clearly not fed.

So we separated us. Then we hired a third person and that person came from like a a number of Fortune 500 companies and uh and they never worked in startup space, never worked in venture.

And what resulted is like everything every conversation was pointing out the risks that we see.

So like anything we wanted to do was like the number of risks that this could carry.

Um and it was really tricky to work because we it's like you kind of get risks but you got the risk advice of like okay and this is where we should draw the line.

Uh but everything was bad the decision

and now we hired a person working previously in the a number of companies as a council and don't poach them. They are amazing

and they understand the the the risk equation a lot better where uh where they are not only like a counterpart to figuring out um what the risks are but also like okay this is what other companies do this is what we should potentially do and then they're like a true thought partner and the tremendous change

>> for sure.

Um 11 Labs uh started as more of a creator brand um everywhere from the individual creators to the the creators that are building businesses

but now you have been having a lot of success moving into enterprise um not just started from the AI agent platform but you know even with the the the text to speech speech to text models um how have you been navigating that transition because that's one of the very common place where you know a lot of really great consumer creator brands fall down

um how have you been navigating that transition

because that's one of the very common place where you know a lot of really great consumer creator brands fall down

But you have had so far a pretty smooth transition.

>> So when we when we launched we had a lot of early inbound where when when we started the kind of the classic PLG a lot of inbound from enterprise and I remember speaking with A60Z team when when they joined us where our initial take was of course we want to be an engineering company we don't want salespeople we would like to reinvent that and have like engineers do the sales.

and I remember speaking with A60Z team when when they joined us where our initial take was of course we want to be an engineering company we don't want salespeople we would like to reinvent that and have like engineers do the sales.

Uh we we we did hire one traditional salesperson and one non-traditional salesperson like an engineer and we told them like do sales now and that really as you can imagine didn't work out in this specific case.

Um but we learned our lesson uh and we we now do invest in in a combination of that.

It's 80% sales 20% engineering.

Uh so still a little bit of that.

Um but this was like super important lever of understanding who are the customers, what they care about and working deeply with them to to bring it back.

Um and then that kind of working with them was kind of opening of what we need to actually do on the product and research side.

Um uh Munjal from Hypocratic is is is here.

He was one of the uh the earliest incredible use cases in the healthcare space where they would create effectively agents that would take inbound calls that are calling the hospitals to take and schedule appointments and beyond that they would do all the other parts of outbing to the patients to remind them about taking medicine or uh reminding them out the appointment that's happening.

And um and to be able to do that that's suddenly shifts from using a one foundational model into combining the speech to text the LM the text to speech to orchestrate them together.

Then the integrations you need to build then you actually need to deploy

and they were one of the earliest it was 2023 but then we've seen this repeated pattern across uh across number of uh other customers and customer experience space and uh and many others

and um and we decided to invest more into helping with the entire orchestration.

So instead of just doing text to speech we can help combining our research to make this whole whole combination of that more fluid.

But then if you are thinking about enterprise you do need to build um the combination of knowledge base inside a system you need to help deploy that with telefony providers with twillio zip trunking like how do you do that in a templatized and easier easier way and then of course the the biggest gap that's the most common it's easy to do a demo but how do you actually build it to production how do you test how you version control how you evaluate monitor over time fine-tune over time based on the results and and all of that is has been um a big big part and underlying all of that and we spoke a little bit with with Matt before coming here the the foundation needs to be there which is the security the compliance serving serving the the customers um across that will rely on that infrastructure that's something that we want to shine through OS 11 laps where if you are using the software it's going to always be reliable and always um uh the 49ths or 59s hopefully one day will be will be there which is tricky in AI space uh but the the that's the that's the goal of course the the difference between um the the one obvious difference between PLG and sales is the the cycle to work through and identify the right customers is much longer and um and I think that's where eagerness from our internal team was was was was interesting to observe where you had a lot of people that didn't work in a enterprise setting and then you had other side of the company that did and the side that didn't was very skeptic about going enterprise and like kind of waiting the six months or 12 months to results and in the early days we needed to shield them from that information and like trust us we'll do this and it will work.

like how do you do that in a templatized and easier easier way

and then of course the the biggest gap that's the most common it's easy to do a demo but how do you actually build it to production how do you test how you version control how you evaluate monitor over time fine-tune over time based on the results and and all of that is has been um a big big part and underlying all of that and we spoke a little bit with with Matt before coming here the the foundation needs to be there which is the security the compliance serving serving the the customers um across that will rely on that infrastructure that's something that we want to shine through OS 11 laps where if you are using the software it's going to always be reliable and always um uh the 49ths or 59s hopefully one day will be will be there which is tricky in AI space uh but the the that's the that's the goal of course the the difference between um the the one obvious difference between PLG and sales is the the cycle to work through and identify the right customers is much longer and um and I think that's where eagerness from our internal team was was was was interesting to observe where you had a lot of people that didn't work in a enterprise setting and then you had other side of the company that did and the side that didn't was very skeptic about going enterprise and like kind of waiting the six months or 12 months to results and in the early days we needed to shield them from that information and like trust us we'll do this and it will work.

how do you test how you version control how you evaluate monitor over time fine-tune over time based on the results

and and all of that is has been um a big big part and underlying all of that

and we spoke a little bit with with Matt before coming here the the foundation needs to be there which is the security the compliance serving serving the the customers um across that will rely on that infrastructure

that's something that we want to shine through OS 11 laps where if you are using the software it's going to always be reliable and always um uh the 49ths or 59s hopefully one day will be will be there which is tricky in AI space uh but the the that's the that's the goal

of course the the difference between um the the one obvious difference between PLG and sales is the the cycle to work through and identify the right customers is much longer

and um and I think that's where eagerness from our internal team was was was was interesting to observe where you had a lot of people that didn't work in a enterprise setting and then you had other side of the company that did

and the side that didn't was very skeptic about going enterprise and like kind of waiting the six months or 12 months to results

and in the early days we needed to shield them from that information and like trust us we'll do this and it will work.

Uh but they were very skeptic and then of course after 12 months it it it worked out but that was probably the hardest culturally of how you kind of still keep everyone jumping on the same on the same train.

>> That's exactly right.

Um a lot of companies actually at least I observed sort of slowed down after start adopting uh more of the enterprise sort of product launching and like building for the customers request um that started to Thank you so much. um to delay sort of the the product launches.

um to delay sort of the the product launches.

um is that something you're seeing or is there still like a good balance of like we still want to be able to put out demos and PC's and um early teasers quickly but at the same time we'll get to you know deliver a very robust and reliable product.

or is there still like a good balance of like we still want to be able to put out demos and PC's and um early teasers quickly but at the same time we'll get to you know deliver a very robust and reliable product.

So there are two parts.

The the first part is um so we have a like a difference on the team structure and then we have a difference on the kind of external product structure.

On the external product structure we we want to ship very quickly.

But of course if you are shipping to enterprise you you want to make sure that it's stable and reliable.

So we delineate very clearly what's alpha, what's not alpha.

Um and then we go through that transition through through that period.

Um and then as we work with the customers they can and then our partners they can decide whether whether they want the access to alpha in the first place

and when they do they that's clearly shown that this is an alpha product that might not be as stable

and so they get a choice and I think that choice has been the the most important lever like do you do do you want it or not

and and some are are are are um incredible on doing that that um innovation and and and and showing some of their work or experimenting with that work.

uh Deutsche Telecom with John here is is is is creating some of the incredible new podcast experiences and that came from like testing early models of turning a a text a text into like a more notebook LM style of a podcast with incredible voices that you can select for German speaking voices, English speaking voices that um that that sound good.

Um and then there's a second which is team structure piece and that's something that we didn't do until until later when we had uh more than hundred of us is that we delineate inside a company products that are pre-product market fit and postp product market fit.

is that we delineate inside a company products that are pre-product market fit and postp product market fit.

Um on the postproduct market fit you are working for the long term.

You test and evaluate a lot before you um you only deploy when when that's that's that's truly ready.

the pre-product market fit. Your mission is is to ship until you think we've hit the product market fit.

And usually we give the six months period of like proving it out.

If not, we kill the product and we've killed product in the past uh this way.

But that's like the the main important piece of like okay until we know there's a big potential user base, we we we will continue iterating.

>> I've uh been able to observe some of those uh I guess hard decisions in the moment, but it's the right decision later on to to let go some of products.

Um this is one of my favorite questions.

Uh my partner Martin Casado always say companies go through three phases. There is a product phase, there's sales phase and there's a scaling phase.

And given you have been through some of those phases, what has been the hardest transition for you as a CEO?

>> There's a lot of a lot of many ones.

Of course, I have my my co-founder next to me across each of those which is the I know him for 15 years. He's my best friend since high school.

say like the the most luck to to to have uh that combination of course uh you Jennifer and all the all the partners to help us through those transitions which is which has been incredible.

Um but I think the the the the recent like recent realization was when we we are now 350 people company and uh and of course that means our go to market team and the incentive structure around that has evolved pretty pretty strongly.

and what wasn't clear to me and now in hindsight it's obvious is that the um in early days everybody would just operate on a passion basis.

They would just operate what they think is best for the company.

As our go to market team enlarged, we realized that the incentive structure really matters if you are building that machine.

And um and that transition where you shift from from from a lot of a lot of the people that are helping create that machine are part of that machine. Those incentive structures will eventually drive the behaviors which might be slightly different to what you had in mind if you don't make it extremely clear.

And in some ways the the quat the commissions are a effectively a lagging indicator of strategy and then um and then strategy um uh is kind of leading of what will happen in the future.

So you need to find a way to resolve those two together where you want to make sure the quad end commissions and the strategy that you want to drive are closer together and they and the the kind of the disparity as close as possible.

And uh so so here the for me the biggest realization was that we are becoming a bigger company because there are clear behaviors that happen based on the commissions.

And then two to actually resolve those we need to be very upfront in terms of um of uh of making it explicit that sometimes even if commissions are just this and you think it's a wrong thing come back to us let's speak about it and just course.

So now we are explicit with all our sales teams that if they are seeing a deal that let's say might be competitive in nature and our pricing table would suggest that they can go very low and earn higher commission but they think it's wrong it's better to come to us.

We are happy to still grant commission but kill the deal and um and and go.

We had this case recently where one of our foundational level competitor came to us wanting to license our models for demos.

And um and of course the incentive would suggest that you should sell to them but luckily luckily we didn't.

>> Yeah,

>> you granted commission though.

>> Yeah, in early days you can definitely

>> and adjusted that now it's in the policy so you cannot sell to the foundational model companies.

>> So it's clear clear to to all the internally.

>> Um that was incredible Maddie.

Thank you so much for for sharing all the lessons and learnings with us.

Let's give a round of applause to to Maddie.

>> Thank you.