Benchmark’s Chetan Puttagunta on the Past, Present & Future of Software

Well, cool. Nathan, welcome to the show.

>> Thanks for having me.

>> I want to jump right in. First thing on the docket. So, you're coming off, you

the docket. So, you're coming off, you invest in a company called Manis.

>> Yes.

>> And I saw your partner Eric, who I had on the podcast this summer. He tweeted

something about it was like a thousand% IRR something. I forget exactly what he

IRR something. I forget exactly what he said, but it was like, "Oh, that's a pretty big number." What's kind of the story behind Manis?

>> Absolutely. And you had a great episode with Eric, too. And you know, Manis was just one of the unique consumer AI

agent products that really spiked and obviously the Meta acquisition had been announced and you know it was a was an incredible journey to be on that on that

product journey with that team.

They're just the the six founders, which is a large group of founders, but I think that's seems to be a theme in AI, which is like to have a lot of founders.

>> The more founders, the better. Yeah. So,

if I meet someone with 10 co-founders, it's instant check.

>> That's right. Um,

>> you know, are just some of the most resilient, brilliant, and kind people that you'll meet. And you know the story really starts with

you know these dates are directionally accurate if not precise but you know first week of March they posted a

YouTube video with a demo of Manis and >> you know I saw it in the first couple of hours of them posting it. Somebody

somebody I follow on Twitter posted it on Twitter saying like I saw this cool demo of an AI agent. I clicked into the YouTube link, saw the video, and then went to the website and signed up for

the the beta. And I think a couple hours later, I got beta access to Manis, and I used it. And I was thoroughly wowed by

used it. And I was thoroughly wowed by the experience. And the reason I was

the experience. And the reason I was thoroughly wowed, so this is March of 2025.

Obviously, we're well past the chat GPT moment.

You know, I was a user of ChattyPD, user of Claude, user of Gemini, user of Cursor, etc., etc. But what they were

presenting was an agent product that could actually get further on task than any other AI product had at that point.

And it really felt magical when I first tried it. And immediately I texted all

tried it. And immediately I texted all my partners and I said, "Sign up for the beta of this thing. It's really

magical."

And then I just reached out to the founders because I wanted to know who they were and just wanted to know about like what they were working on and how Manis

worked. Like how did Manis get so much

worked. Like how did Manis get so much further on task than a regular AI chatbot. What were they doing? What was

chatbot. What were they doing? What was

so what was like their key breakthrough?

what was what what what did they learn to like get there?

>> Yeah. What was it?

>> And so what they had figured out was, you know, I think at that point people at the application layer had learned that you could use multiple models at

once to do more with a task. What I

don't think people had quite tried was breaking up a task into like a thousand little tasks and then for each ta like subtask

using multiple models in parallel trying to get past that substep.

>> And so they had just taken the idea of breaking a task into subcomponents and using lots of models to solve subcomponents to like such an extreme degree. I don't think anybody had tried

degree. I don't think anybody had tried that yet.

>> [clears throat] >> And so we reached out and they got on a Zoom with us on a Monday and explained to us what they were doing and it was super

compelling. And at the time for some

compelling. And at the time for some reason the beta had like gone >> really viral in Japan. And so the team was in Japan

>> trying to figure out, you know, like why it was going viral in Japan.

>> So you had to go to Japan to figure that out apparently.

>> Yeah. Yeah. So, they wanted to just talk to users because they wanted to be like, "Why is this breaking out in Japan?"

>> Yeah.

>> Interestingly, I don't know if you know this, but like when Chat GBT first launched, Reddit Japan actually was one of the places that went viral early.

>> And so, there was something about consumer chat bots and consumer AI and the Japanese market that seems to be an early signal of like things that can go

viral in consumer AI. And so the team was in Japan talking to users, running a bunch of user meetups because they wanted to know why it was going so viral

in Japan. And so, you know, they

in Japan. And so, you know, they we did a Zoom on a Monday and I wanted to meet them in person. And so they were like, "Well, we're in Tokyo, so you're

welcome to come hang out." And so I think that Friday I flew out and before that a couple of the founders were in San Jose at the

time and so I met them you know in San Jose and then on Friday I got on a flight Friday night I think I got on a flight to go to Tokyo and then Saturday night Pacific time they presented to the

partnership and then >> over Zoom >> over Zoom and I was there in person with them in Tokyo >> and And after their presentation, Red,

who was the one of the founders and CEO, Red and I went for pizza and beer and got to a handshake. And then that was like the start of the relationship. They

>> they ended up launching the product in general availability first week of April.

And so they basically ran a one-mon beta with sort of like a closed beta where you had to sign up and and then they would let you in. And then you know like

the the product exploded and you know on in December they announced that they had gone zero to 100 million ARR in eight months and 100 million ARR and then if

you counted the consumption revenue they were generating it was like 125 million run rate and I think that's the fastest company to have ever gone zero to 100. I

mean, eight months is just an outstanding, you know, speed record to go zero to 100 million.

>> Yeah, it's pretty good.

>> The interesting thing about about this product was where it was being used and how it was being used. And so the three primary use cases that emerged

were deep research, coding, which was like a fascinating use case, >> interesting, >> and three was slides. And so the three primary things that you know consumers

were using Manis for was was those three things.

So if you dig into each of those components, Manis was getting further on deep research and writing further like more detailed reports than

than other AI chatbots on coding. It was interesting that Manis

on coding. It was interesting that Manis was being used by non-technical people to code websites, applications, prototypes, mobile apps,

whatever.

And they were basically using it as like a technical companion and largely by people that were not technical.

And something about the user experience, something about the UI, something about how far Manis would get on a prompt with a website or developing a mobile app

really attracted a lot of like consumers proumers.

>> And then finally, the third one was slides. And that made that of course

slides. And that made that of course makes a lot of sense. If you're really good at at deep research and people are doing a lot of deep research, they want to turn the deep research into a bunch of slides that they could use [clears throat] for work or whatever.

And so those were the three primary use cases that emerged and and yeah, they just kept building features based on consumer pull and then obviously it

caught the eye of meta and they acquired the company and couldn't be happier for the founders like they're incredible group incredible technologists. I think that they had

technologists. I think that they had built a product that really blended multiple AI models, the three AI models. So they

exclusively used anthropic, open AAI and Gemini models and and they had just created a way to blend the APIs of these three models to just get further on tasks. And I think

it's just a from my perspective, Meta is acquiring a team that's like very deeply knowledgeable about how these APIs work

and how to get further on a task depending on the kind of task >> with a certain set of APIs. And

>> you know, I think if you just project out the consumer market the next couple years, I think like you're going to see more and more consumers and proumers want to just get things done. and this Manis team

things done. and this Manis team certainly has figured out a way to do that.

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of spend. Thank you, Flex. And now,

let's jump in. So, you got some heat on Twitter when you invested. What was

going on there? People are welcome to say whatever they want on Twitter. And

we had a great deal of conviction that this company was a great company founded by a great set of entrepreneurs. And you

know ultimately the company at acquisition was about you know 105 people roughly maybe a little bit

bigger. And the team was 95 people in

bigger. And the team was 95 people in Singapore a couple people in Tokyo and a couple people in the Bay Area. And the

company was like pure technologists, pure product engineering people building exclusively on American models like anthropic GENI openAI models. They

weren't fine-tuning or post- training or building any of their own models. They

were using APIs from American models and their product was you was hosted fully on American clouds. So like they were using largely Google cloud, AWS and

Azure to host the product. And so if you just looked at what they were doing, they were delivering delivering incredible consumer value for a great price and had built an

incredible business. the founders happen

incredible business. the founders happen to be of Chinese origin and for a company headquartered in Singapore and for us like we want to invest in

great people and I think that if you just looked at you know for example this has been published on Twitter or X

a lot which is like rosters of great AI scientists and AI research scientists >> they're all Canadian [laughter] a lot of them are Canadian a lot of are

American and a lot of them are of Chinese origin and you know Jensen Wong of Nvidia you know recently said something like half of the world's AI

scientists are Chinese and half are American and you know so I think like there's a large population of AI researchers AI

engineers and AI product people are of Chinese origin and I think that to me I want to back the smartest people

building great consumer products and this was a great consumer mark product targeted for the world market. The

primary users of this company were in the US, Japan, Europe, Brazil, India, etc. The product wasn't available

in China. Manis couldn't be accessed in

in China. Manis couldn't be accessed in China. Like they didn't have a business

China. Like they didn't have a business in China. It was like a worldwide

in China. It was like a worldwide business. And

business. And to me, those are the kind of businesses you have to you have to back as an as a generalist investor, especially as somebody that was looking for consumer

AI. Like for me, you know, going into

AI. Like for me, you know, going into 2025, it was pretty clear to me that somebody was going to build a consumer AI agent

or a consumer AI product that was going to allow people to get tasks done. I

mean, this was pretty much the thing that everybody was talking about. If you

remember, towards the end of 2024, they were saying 2025 was going to be the year of the agent.

>> Yeah. It was kind of starting to piss me off a little bit. [laughter]

>> People couldn't shut up about it.

>> And so, if you just were paying attention to like what everybody involved in AI was saying was that 2025 was going to be the year that we were going to start to see agent products really come alive. Mhm.

>> You know, as soon as I saw the Manis demo on YouTube, it was pretty clear to me this was the instantiation of like exactly what people were talking about.

And you know, you just had to due diligence on the company and the founders to realize like this was a great company, a great product, and was

going to really do a lot for consumers and provide a lot of value. So, we had all the confidence that this was a great company and this was a great great set of founders and if people want to say a

bunch of stuff on Twitter, like that's their problem, not mine.

>> I feel like you've gone through these waves of like being active on Twitter.

>> Sure.

>> I feel like when I first met you, it was maybe one of your earlier waves and you didn't >> because I actually have I realized I had notifications turned on for your tweets.

>> Oh, >> and you still have them?

>> I do. Yeah, [laughter] I do. I don't

know if you noticed. Sometimes I'll like like or reply very quickly.

>> Yes.

>> But there's definitely a period where you like didn't I feel like so like how do you think about your being more active on social or pulling back a little bit? So I sort of ramped up my

little bit? So I sort of ramped up my activity on Twitter a lot starting in 2018 2019 primarily because you know at that time

there were a lot of companies in software going public a lot of public activity around software and then we entered the pandemic where you know I

was in front of a computer basically all day like most people so I was on Twitter and X a lot and so and then software as you know at that time experienced an

extraordinary moment in time of exponential growth.

And in that moment, you realized that software evaluations that were that were in the public markets and private markets really did feel unsustainable,

but you kind of had no choice but to accept the unsustainable nature of those valuations because you had to like play the game on the field. And I think that you know other venture capitalists

including Bill have talked about this which is that in venture you only have one strategy which is like you can only go long. [snorts]

There's no in our asset class there's no such thing as like shorting something like that's just not a thing. just like

selling maybe like if you own the shares like you sell >> maybe but like even that like if you're an early stage investor buying 15 20 25% of a company like >> 25%

>> right like you're just not there it's not a highly liquid market for that and so you're going long only and so you're like reacting to the market constantly.

Um, and so in that moment, it just really felt like we were in a in a really wild time and the kind of growth that software companies were

experiencing, the kind of like earnings acceleration software companies were experiencing at that moment in time, the like amount of spend that was going into clouds, cloud infrastructure. It was

just a really fascinating time. And I

decided to just talk about that cuz it was something that was like super fascinating to me.

>> And you know, I just started at that time just starting to listen to like every earnings call >> as one does in their free time.

>> Yeah, that's what I was doing. And you

know, I was just summarizing and highlighting the things that I was learning off these earnings calls. And

for some reason it found a niche large niche audience on Twitter. And

yeah, I mean it it ended up building a pretty sizable audience which was frankly surprising. I was quite

frankly surprising. I was quite surprised that you know that many people were that into into software companies and software.

>> It's such like a simple thing too. Yeah.

>> And it's like something where >> you arguably would have done that anyways. You just and maybe you would

anyways. You just and maybe you would have also summarized it just instead of sending it to a friend or a couple co-workers, you just put on the internet.

>> That's right.

>> And there's a big audience for that.

People want to get smart. People want to learn things.

>> So that happened for a bit and then >> I think May of 20 21. So these are again I think this is when like we reopened

our office, the benchmark office in San Francisco.

And that's when you can clearly see my Twitter activity start to fall off. And

so once we reopened our office in San Francisco, the number of people that wanted to just meet in person went through the roof.

Like I think there was just, you know, we had we had basically been meeting everybody on Zoom for over a year.

And I think there was a moment where like as people started to move back into San Francisco, they like the early stage entrepreneurs just wanted to do everything in person. And so we saw

probably around the summer of 2021 a lot of activity move to in person >> and a lot of people just wanting to meet in person and you know reconnect

>> in person much like we're doing now. And

I think that's like when we may have also met for the first time around that time or maybe we met in 2019.

>> I think we met before co. Okay. I'm

trying to remember.

>> And then we met again cuz I think you made a trip out after.

>> Yeah, there's one we got breakfast. Was

it like at the the Rosewood?

>> Yeah, in Park. That's right.

>> Yeah.

>> Maybe the second time we >> That's right.

>> met. Maybe that was the first time actually that we met in person. I can't

remember.

>> I cannot Yeah, because we met a bunch of times on the Zoom >> while we were in the pandemic. Anyway,

so I think like as a result of like my schedule just going back to mostly in person, I stopped interact I just stopped tweeting and

>> I just thoroughly enjoyed meeting people in person more than listening to earnings calls and stuff. And so I just stopped listening to earnings calls and then >> you know if you stop listening to

earnings calls there's not a lot to summarize. And then

summarize. And then I just sort of stopped paying attention to public software companies as a result and you know stopped you know you always still pay attention to public software

companies in your own personal time and like to understand the industry and stuff like that but I wasn't paying as close of attention as other people were and I wasn't sort of like looking at it

first and offering first impressions.

So, like, you know, it started to go back into the standard practice of like I'd read earnings releases maybe like two weeks after they came out or I'd read like analyst report two weeks after it came out. And whereas in the

pandemic, I would read it like the day of.

>> It was like your entertainment, like your event was like, "Oh man, I think 1:30.

>> That's right." Like that's what I was doing. And so once we came back into the

doing. And so once we came back into the office, it was just delayed again. And

then I think like if you didn't if you didn't tweet about it right at the moment, I think there was like a a moment in time interesting too which I thought was interesting that revealed itself which is like I tried a couple of

tweets that was like on earnings that were like a month old or two months old and it was basically like people were like I already knew this. Yeah, you usually need a chart like you need like a good chart that showed like how

>> Yeah.

>> AI adoption maybe was like changing in Salesforce or something, but you run the rest of like the day of earning.

Somebody probably grabbed that and >> That's right.

>> tweeted.

>> That's right. And so like there was a a timely nature to it. So I didn't I didn't do that. So I stopped doing that.

And then I think I'm back more on Twitter now with all this like AI stuff happening. And I think the the reason

happening. And I think the the reason that I've started to re-engage is >> watching launch videos. [laughter]

>> Yeah, sure. It's pretty launch videos are are really impressive now.

>> They are very good.

>> Very high investment.

I think the the thing that I'm starting to, you know, tweet more about is just that the stuff that's happening at the application layer with AI is a

fundamental shift. And I think that it

fundamental shift. And I think that it is a fundamental shift on on the same scale of on-prem to cloud and on-prem to cloud took a long time.

>> Can you maybe give us like so Everett on your team was like, "Oh, >> you got to ask Jason like give us like a like a history lesson on like the eras of software. Can you maybe walk us

of software. Can you maybe walk us through like as early as you can, you know, recite and talk us through all the way to today?"

>> Go all the way back to the advocacy.

[laughter] So King Nebuchadnezzar back in the empire, >> right? That's right. So we came up with

>> right? That's right. So we came up with the number system. No. And then look, I think it started it started with mainframes. Like mainframes is when we

mainframes. Like mainframes is when we started to get real application software level stuff. And

level stuff. And >> we was like massive machine that weighed 50,000 >> pounds. That's right. And like you would

>> pounds. That's right. And like you would create that's when people started to create custom applications to automate stuff. And whether it was for like the

stuff. And whether it was for like the defense sector, whether it was for the public sector, whatever, for like private enterprise, that's when you started to automate things using mainframes

>> that then transitioned to you know client server and then you had the internet show up and then the internet you know obviously changed everything with regard to application software. So

all of a sudden you had consumer applications and then you had B2B applications.

Internet also helped centralize servers and how like clients were served. It

created edge networks.

>> How's why is that all important? How

does that change kind of the business model?

>> It allows there's two So every new wave two things happened. The number of applications created exploded each time.

>> Every time.

>> Every time. Like the number of applications created to serve either businesses or enterprises exploded each time. Is this like it increases like an

time. Is this like it increases like an order of magnitude like 10x is it?

>> Yeah. Yeah. But the internet was like 100x so it was like two orders in magnitude. So each time there's like a

magnitude. So each time there's like a big jump in the number of applications and then there's also a big jump in the number of companies creating applications for either consumers or businesses.

>> Okay.

>> And so these are enabling technologies and then you had internet. internet

showed up and then you went from client server and then you went to basically the cloud model which was you know you centralize where all the servers are

hosted and then you serve people by the internet the browser and that ended up being sort of the the genesis of cloud and then the cloud thing ended up being

extraordinarily transformational.

>> What was the biggest kind of transformation? I think number one of

transformation? I think number one of all was Amazon. And so Amazon when they released EC2 and S3 which was like you

know cloud computing and cloud storage you know in ' 09 10 and 11 is when you started to really see that become very very significant. And if you were

in you know around the Bay Area around that time what you noticed was a dramatic shift in net new companies.

They were all using Amazon. So, if you were an app developer when the app store first came out in '09 and your app was like really working, you still had to go

get server storage in downtown San Francisco.

>> Like, you had to buy computers. You had

to buy a rack and then, you know, you could go to like Equinex and they'd sell you space and then you'd have to rent servers from them

>> or you'd have to go to like Dell or HP or like you could go to like Quanta like if you really wanted commodity boxes and they would sell you these servers and if you bought your own you'd have to like

come put them in and then install them and wire them up or you could get like the person that ran the facility to like you know you could rent the servers from them. We could buy the servers from

them. We could buy the servers from them, >> but you had like this is what it required. And this wasn't the crazy

required. And this wasn't the crazy thing is that not that long ago. This is

2009, 2010. Like it's not just it's just not that long ago.

>> Yeah. That's like relatively speaking. I

mean, I guess I'm almost 35 now. So like

I was graduating high school. Like to me that's not that long ago.

>> Not that long ago. And that was that was like really the cloud. And it just so happened that it coincided with the launch of the app store which came which was in 2009. And so you had this cloud

thing happening and you had mobile devices about to explode worldwide. And

so the total demand for applications from both consumers and businesses was about to go like it was going to like

have two factors that were going to like increase orders of magnitude. So it was like 10x multiplied by 10x. So it was like you had cloud happening which basically meant like more application

developers could develop applications >> just easier to like get off that to off the launch pad.

>> That's right. Like you didn't need to think about servers. You didn't need to think about renting space. like the if you were running sort of like demo apps

or prototypes, the standard way to have done it was to like bought a server and plug it into your apartment and that's where you'd like host it and then you know if you were like a solo

entrepreneur this is how you do it and then you'd be like go ship it once you were ready to launch it you like go get some servers plug it in and like hopefully it would work and like >> yeah [snorts] >> this thing was just it was one capital

intensive because you had to like you had to go invest the capital on compute and storage.

>> You also had to figure out how do I run this server thing >> 100%. And it was like it was expensive.

>> 100%. And it was like it was expensive.

And so the barrier to entry was high.

And again this is the other thing about each wave of technology like each wave the barrier to entry for developing an application went down. And so

>> with cloud and mobile it was one the cost of like deploying a prototype or an initial version of application went way down because you could go put it in the cloud. And then two, the cost of getting

cloud. And then two, the cost of getting to an end user also went down because like you had this amazing distribution mechanism through the app store.

>> And so >> just like a kid in the class would be like check out the Snapchat thing. Just

all these kids just downloading it and using it.

>> That's right. And like and then you could pay Amazon on a credit card and it would be consumption based.

>> So you technically didn't even actually pay upfront. Like you could That's

pay upfront. Like you could That's right.

>> you could not pay your credit card for 30 days or if you got a loan. Yeah. you

got a 30-day loan essentially like it was like and so >> it was a remarkable unlock and you know you saw an absolute explosion in app

layer like obviously mobile apps like anything from like Instagram, Twitter, Snap like that all was unlocked there. Uber,

Airbnb.

>> You're just listing off benchmark portfolio.

>> Correct. That's right. That's right.

Airbnb. Maybe not Airbnb. Yeah, Airbnb

wasn't. But

>> did you guys have like a Did you guys have a bet in the space?

>> I'm trying to >> of like rentals, >> home travel type of >> This was before my time. So, I don't remember.

>> You're not an expert on the portfolio.

No, >> I should be, but I'm not, unfortunately.

>> Bill's going to be listening this like, come on. [laughter]

come on. [laughter] >> We may have had something, but I think they did great. I think that, you know, the 2011 fund is legendary for having just gotten mobile and cloud like

perfectly right.

>> Even we work. We work wasn't even mobile or cloud. How did that even get in

or cloud. How did that even get in there, >> you know, like that was Bruce and so, you know, you're going to have to go to him for the history on that one. I don't

know.

>> Yeah. What was that fund like? What's

the performance on that?

>> Benchmark 7 and you know, we don't publicly talk about our performance. You

can you can find it on the internet.

It's like plenty of >> Okay. What's the number on the internet?

>> Okay. What's the number on the internet?

What does the internet say?

That's a good question. I don't know. I

think the last thing it said is Yeah, you should Google it. I'm googling it right now. Benchmark 7 and I'll tell you

right now. Benchmark 7 and I'll tell you if it's like high or low. Said it was a around $550 million fund in 2011. Mhm.

>> Says it was roughly 25x before fees.

>> There you go. Sounds pretty good.

>> Directionally correct.

>> Correct. Directionally correct.

Then you added in your manus thousand% higher. Yeah, pretty good.

higher. Yeah, pretty good.

>> So, so the that really unlocked the number of applications, ease of deployment, lowered the capital required. So that was you know mobile

required. So that was you know mobile that was cloud we've been on that wave basically until you know call it 2018 2019 I think what you

>> co was like an incredible period also right like huge accelerant >> and almost like foot on the gas even so >> a huge accelerant through 2020 and then >> the other thing that was happening

though was that the SAS companies incumbents were starting to get really dominant like the control distribution and they would start to eat up not only their core category but would start to eat up adjacent categories and so this

is how Salesforce went from being just CRM they did CRM service cloud integrations etc etc like it became huge same thing with service now they started

with ITSM and then it expanded into like HR sales and all this kind of stuff like these SAS companies started to get really really big and if you just looked at it

from a startup perspective it was actually harder to get distribution ution >> in like 2020 2021 because the incumbents had gotten so big and so powerful and

like basically penetrated every enterprise account and if you were a brand new startup you would show up and be like well I could do this like nichier thing 15 to 20% better and then

your buyer would be like well I could just go to this Salesforce thing and ask them for a discount and they probably just give me 5% off and that's probably

easier and So it was frictionful like having a [clears throat] net new company to find its sort of niche and to be able to like really go after a big horizontal

category was really hard. And so what you ended up seeing 2020 2021 was like a lot of hypervertical application software companies get created

>> like give me give me an example for someone.

>> There were a whole bunch of companies that were created for the construction vertical as an example. There were a number of companies created for the compliance vertical. Um, some of them

compliance vertical. Um, some of them are doing really well now, of course.

>> So, it's basically like a what are things that Salesforce isn't doing or won't do.

>> Salesforce, workday, service now, what are they not doing? There's still

opportunities. Then people would like go attack them and some of them obviously became successful. But you didn't see

became successful. But you didn't see the same Cambrian explosion of like applications that you saw in ' 09, 10, 11, 12. like it just wasn't

11, 12. like it just wasn't this extremely fertile ground to create new startups especially at the application layer.

>> It was mostly just a big kind of like got most of the value or absorbed most of the value >> and obviously things dramatically shifted starting in 2022.

>> Yeah. So how do you kind of dissect what was going on in 22 23? So you know GPD APIs were available in 2022 and

this was the earliest signal we had gotten at benchmark that people were thinking about developing new sets of applications. So we would meet

applications. So we would meet entrepreneurs that would be playing with these APIs and saying like hey have you guys seen this thing like it generates

like you know you can make this API call you could do this and that it's generating all this interesting stuff >> maybe had like Jasper was maybe like a break at that time >> there were a bunch of like people doing

like copywriting as sort of the first use case because you could only interact with these things through APIs And

in 2022, November of 2022, obviously, Chad GBT comes out, you know, I think everybody that used the product that moment in time thought

it was like the most magical thing. And

I think feeling like it was magical wasn't a particularly unique insight. I

think everybody thought it was like obvious.

>> Yeah. It was like, wow, this is incredible.

>> Yeah. I think what we as a firm did at that moment in time which I you know look back on was was particularly insightful was to then say like this is

obviously the way of the future like this is clearly what the experience of the future is going to look like >> which is there is going to be some

automated system at the back giving you the answer or finishing the task and it became pretty obvious to us around the table that that was the way of the

future. And so what we decided to do at

future. And so what we decided to do at that moment was focus heavily on AI applications. And I think this is one

applications. And I think this is one where if you've just been investing in software like I had for, you know, at that time

probably over a decade, you know, this is like the best thing that could happen to you as a software investor because now as an early stage software investor,

you're a late stage software investor or like a private equity software investor, you're like now have a lot of like portfolio companies that might be threatened. But if you're an early stage

threatened. But if you're an early stage software investor, if you recognize the catalyst of a shift change and you think this is the next >> shift change.

>> Yeah. Like as next catalyst as big as a cloud, you've it's like incredibly exciting to go all in on software applications again at that moment. So

then it became pretty clear to us that every large horizontal category was up for grabs again and it was a good time to go back into it. And

>> so the Salesforce, the workday, the service now, >> like all horizontal categories like were up for grabs and all we needed was entrepreneurs that saw the future by

playing with like TGBT and the OpenAI APIs.

And then there were also like net new categories to be created like you didn't only have to go after incumbents, you could like create brand new categories and sell software into areas that hadn't

bought a lot of software before. And

that's because the software could solve new problems for you that couldn't.

>> That's right. And so this is where like coding assistants and legal software very specifically >> like those are not categories of software that are pre-established >> like legal as

>> you know >> that was a dead zone >> touch. It wasn't great. I mean I had you

>> touch. It wasn't great. I mean I had you know to be fair I had one successful company there a company called logical which was an eiscocovery which I invested in and had a very

successful outcome. It was acquired by a

successful outcome. It was acquired by a PE firm of course because like >> you know it's like once you get eiscocovery customers there's a lot of cash flow that shows up as a result.

>> What is eiscocovery? This is like the process of learning more about the case and like collecting evidence or something like that.

>> Yeah. So basically whenever you have to go through like litigation, you know, you have to go discover a bunch of facts against that case that shows up in like emails and documents.

>> So this is like when someone says when when we see those posts about like the Steve Jobs like an Apple like it was through the discovery process that >> that was like an eiscocovery software found that found that email

>> and so that those companies ended up being pretty interesting. There was a company in Chicago called Relativity that ended up becoming big. There were

like some companies in Europe.

>> This is basically just like a SAS document storage. It was like

document storage. It was like virtualized for legal. Yeah. Okay.

>> And so that was like the only category in in legal software that worked. But

>> you know when AI shows up, you now can like start to meet entrepreneurs that are dreaming big again in horizontal categories. And that also allows you to

categories. And that also allows you to invest in AI app enablement. And so it's like all of the frameworks, all of the like new cloud infrastructure to host

all these AI things like these are all these all open up as opportunities. And

so if you look at our investments on 2022 onward in AI, there's a one central theme to it all.

It's like all AI applications and application enablement. And that was

application enablement. And that was very much on purpose because we just found that there was tons of opportunity there.

In our view, it was also opportunity that a lot of people weren't paying attention to in our industry because I think people

at that time saw the magic of TAGPT rightfully and then you know a lot of AI labs ended up getting funded at that moment in time in 2022.

>> How many did you guys invest in a benchmark?

>> We invested in one open-source one.

>> That feels like a classic benchmark approach. is the open- source version of

approach. is the open- source version of something.

>> Oh yeah. I mean we you know as you know from our history we deeply believe in open source and in this case it would be

open weights and you know there were a lot of open weights models including llama models but then more recently if you look at you know models that are coming out of

China that are open weights like these are these are really unlocking a lot of additional models in in the US that people are like borrowing techniques from these open

weight models. They're borrowing these

weight models. They're borrowing these open models themselves and then, you know, customizing them, crafting them, fine-tuning them, applying, you know, RL

to it, all that kind of stuff.

And so open weight models in general are are a category that I fully believe in.

And I hope that, you know, llama continues meta with llama models continue to keep them open. But you

know, I think that we spent a lot of time meeting with entrepreneurs building AI applications and entrepreneurs that wanted to enable the next generation of AI applications.

And so if you look at that fund and you look at the investments that we made, you know, we made a number of seed and

series A bets at that time in 2022 in companies like Sierra, Lora, Fireworks,

Level Path, Lang Chain that just are entrepreneurs building very horizontal applications.

that are attacking gigantic markets with truly innovative tech. And when you open this application and you experience it for the first

time, the SAS comparable looks really bad.

>> Like it just looks like >> So give me an example of one of those contrasts that I might come across in the wild.

>> I think if you just ever interact with Sierra as an enterprise customer, like if you were to buy for the this podcast. If you ever wanted a customer agent, you have two options.

You could go to like a traditional SAS provider, legacy vendor, >> who would that be?

>> Like you could get a ticketing system plus a CRM system plus some kind of autoresponder.

>> So what is this? Salesforce or workday.

>> You could get Salesforce, you could get HubSpot, you could get a Zenesk, you could get >> like like piece together lots of these software. And like by the way like we

software. And like by the way like we invested a lot in these SAS companies and I invested a lot in these SAS companies. So like they're all great

companies. So like they're all great companies have a great deal of respect for all the entrepreneurs. They have

good distribution that they >> but it's the same thing that happened with these cloud vendors versus their on-prem rivals. So Salesforce versus

on-prem rivals. So Salesforce versus Seel and Oracle, Zenesk versus, you know, >> the name >> Remedy was like, I think the ticketing

software before that, Workday versus Peopleoft, like >> the the on-prem incumbents when the cloud software showed up just felt like a terrible piece of software because

like >> they were slow, >> they like they had all the setup, they were expensive, they weren't are not interactive. Like they couldn't be

interactive. Like they couldn't be accessed anywhere. Like why didn't they

accessed anywhere. Like why didn't they just go to the cloud? Like why didn't they just code a cloud app and like fix it?

>> They because it's really hard. Like I

think you've read innovators dilemma and like it applies to technology companies.

>> I actually haven't read it really.

>> I mean I know the premise [laughter] but I haven't actually read the book. Maybe

I should actually read the whole book.

You should page to page.

>> Uh you should read that.

>> You should really read it. You should

read also competitive analysis. And so

it's like these like classic business books and it's if you look at sort of like if you have a great

business that and then you make a technology architecture and you build a large application, you now have built an application that's serving thousands of customers and then you've built a huge

distribution force to take that out and sell.

>> Yep. And so the business machine that you've built runs on this like thing sustaining. So if you need to

sustaining. So if you need to rearchitect your platform, you need to pause everything, pause distribution, break your architecture, move it to the

cloud by the way, and then actually like get it to work, have it scale and all that kind of stuff, and then teach your distribution networks to like distribute this brand new product and then like

basically start from scratch.

>> It's really hard.

>> That's hard. really really really hard.

The thing that I think people really forget is that when Salesforce was like really growing really fast, Sevil created a product called Seible on Demand which was their >> sounds [laughter] like a not good

product.

>> Seable on Demand was the Sevil answer to Salesforce.

>> Okay.

>> And there were people that were covering Sevil at that time that said Seable on Demand would crush Salesforce. Like that

would be the end of Salesforce would be sealable on demand.

>> Were they both publicly traded at the time? Yes.

time? Yes.

>> Okay. So, I'm sure Salesforce stock dropped like 10% the day of launch or something.

>> Whatever. But like seable on demand didn't work because like the seable onrem product >> was just so profitable that like and like seable on demand may have worked as

a product but had deficiencies against the Salesforce product and then Salesforce was because they had built that company from scratch was able to distribute it far more efficiently than

Seibo could like just the company structure was built for selling like licenses for 5,000 bucks or 10,000 bucks. to a company where Seivil was

bucks. to a company where Seivil was built to distribute licenses that were million dollars or greater. And so,

[clears throat] you know, Seal on Demand would come and quote you like $100,000 and the Salesforce rep would be like you can have mine for 10,000 and yeah, Seal on

Demand is like 10% worse. And so, like, you know, and so >> why would you even sign up for that if you're doing any research at all?

>> It's so like that competition becomes so uneven. So, so that the distribution

uneven. So, so that the distribution wins. That assumption assumes that

wins. That assumption assumes that they're not going to look at comparable products. Correct. When it's so easy to

products. Correct. When it's so easy to just That's right. Salesforce.com,

click a button, we're in the product, you can put in your credit card, use it.

>> That's right. And so this is like now fast forward to today with AI applications. We're going through the

applications. We're going through the same thing again. Like for an AI application to become, you know, like for a SAS company to beat

a native AI application, I would argue that they would have to break their fundamental architecture and rebuild the application and redo the business model and retach their distribution on how to

distribute the thing. The advantage of having an AI application company start from scratch is you build this thing AI first from point zero. Like you don't query any code that's not AI friendly.

[clears throat] The first set of salespeople you hire and the first set of marketing hires you make are all intended to distribute this like AI product priced as like however

you want to price it consumption seats whatever but it's priced like an AI product and you go against the SAS vendor in these things it is

to me the early signs are astounding how fast these AI applications grab traction and grow and how quickly they're able to displace traditional SAS vendors.

>> And you know, I think on Twitter or X, we're seeing a lot.

>> You can call it Twitter. I I refuse to call it X. [laughter]

>> I think you see a lot of people saying like, you know, the SAS companies can be really replaced by cloud code. I'm not

sure I buy that. I think that >> of the like, hey, make me Salesforce, make no mistakes, you know.

>> Yeah. Or like, build me a billion dollar software company, you make no mistakes.

>> Yeah. 10 billion. Right. [laughter]

Right. Like I don't think that is what's going to happen. But I do think that if you look at legacy SAS, their business

models are seriously going to be challenged by AI applications that deliver 10x the value for a third of the

price. And if you're an enterprise and

price. And if you're an enterprise and you can buy an AI native version, you will buy it. I I'm I can't see why you

wouldn't like you see the experience and it's that stark. And I'll give you a couple examples. If you look at Sierra

couple examples. If you look at Sierra >> and you deploy it as a customer service agent, it is absolutely a mind-blowing experience. Like you don't need a

experience. Like you don't need a ticketing software, you don't need call routing software, you don't need all this stuff to make tickets that like customer issues that are coming into

your company go away. like it just goes into Sierra and there's a resolution and guess what your customers are happier and you're basically replicating

your single best customer agent to infinity like that is what Sierra is so all of a sudden your customer satisfaction goes through the roof your

business metrics get a lot better like your renewals get better your expansions get better because like customers are actually happy with their experience and so comparing that with legacy which is

like chaining together four or five software solutions. It's just like hard

software solutions. It's just like hard to really compare them and then the question then becomes like why doesn't an existing software person just like do it.

>> Yeah. Because I was going to say if I'm Mark Benny off Salesforce I I went through this.

>> I see why I won and I see like the advantage I had over Seable. Do I look at stay and it's like oh man this could happen to me like I should I should know that this is coming shouldn't I

>> that's right I think that if and of course look I think Mark Benoff is one of the greatest entrepreneurs of all time he's also been an extraordinary friend to startups and how open he's

been with Salesforce APIs and the ecosystem and all that I think I only have great things to say I think that Salesforce should buy a lot of companies

now like I think that one of the things that Salesforce has always done really well historically is buy the right companies at the right time. I was going to say Slack as an example. Is that a bad example?

>> No, it's not a bad example.

>> I feel like that one's been ridiculed of like too high a price. Maybe

>> too high a price. Yeah.

>> I have a friend at Slack. She's like

they [ __ ] ruined it.

>> They ruined it.

>> Yeah. She's like not bullish on She's not bullish on Salesforce or like Slack as a part of Salesforce.

>> Yeah. If you just look at, you know, Salesforce history, I think people forget that it was founded in the late 90s and just in different waves. For

example, when like marketing cloud took off, like they went and bought a great marketing cloud company. Like when they when commerce took off, they went and bought Demandware. Like they were making

bought Demandware. Like they were making key acquisitions at the right times throughout their their growth trajectory. And they were actually very

trajectory. And they were actually very good at M&A. And Salesforce Ventures is an incredible investor. They've like

invested in great companies. They

continue to invest in great AI companies. I think one of the things

companies. I think one of the things that they should do and of course I'm, you know, telling a public company what to do. So I have great level humidity to to assume that

they would listen. But I do think that like if you're >> I'm going to cut that part out. By the

way, I'm not going to let you say that.

I'm going [laughter] to make it look like No, I'm just kidding.

I think [gasps] I think if you're a SAS company right now, you should really think about spending 10 to 25% of your market cap to buy AI

applications. I really think that's a

applications. I really think that's a good idea. And I think if you're an AI

good idea. And I think if you're an AI application company like Salesforce or Service Now or Data Dog or whatever, like name your favorite SAS

company that's public or private. Like I

think they really should go buy AI applications that they then could feed into their distribution networks or like these AI application companies come in and and build them a net new

distribution network. I think it's like

distribution network. I think it's like it's one of those things like if you study the history of software history of application software there are moments in time when the on-prem vendor should

have just bought the cloud thing. The

best example of this is BMC which was you know Service Now before Service Now >> should have bought Service Now way before it got as big as it got.

>> Aren't they like the seventh or eighth biggest software company now or something?

>> Yeah, of course. But like they should like BMC should have bought it and there were moments in time where I think BMC could have bought it

>> and like I think if they showed up like they had the market cap to buy it, they had the wherewithal to buy it but they didn't because like you know it was like well we're trading at x revenue multiple

and I don't want to give sales for or service now you know 20x revenue multiple whatever like but in retrospect it was you know completely changed the The other one is like obviously

you know Salesforce I mean there were times when like it was rumored that Microsoft was going to buy it. There were t rumors that like you

it. There were t rumors that like you know could Oracle ever buy it? like you

know like Salesforce just sort of like completely cleaned out CRM from all the onrem vendors and like all those businesses just ended up going to zero

>> and so like they could have taken Salesforce out much earlier in its journey. So if you just look at every

journey. So if you just look at every big category winner in SAS, there was like an opportunity for the onrem company to make a big acquisition and

make something of it. And like they didn't. And I think that if you're

didn't. And I think that if you're watching the AI application thing happen, you know, you're starting to see M&A sort of pick up, but it's not really at the pace that I would I would

encourage these companies to think about it, which is just like you really should jump into these game this game and like buy some of these things because these companies are about to get gigantic.

Like they're getting to 100 million. I

mean, Manis went zero to 100 in eight months. like

months. like these companies are getting really big really fast. They're getting to 100

really fast. They're getting to 100 million really fast.

Once they're getting to 100 million, they continue to scale beyond that. You

know, there's lots of questions about margin profile and all this kind of stuff. And I'm telling you, these like

stuff. And I'm telling you, these like AI application company P&Ls, like maybe they don't look as pristine and as predictable as like super mature SAS companies, but you know, the early days

of SAS companies, those P&Ls didn't look that pristine either. Like people just should go look at the workday S1 and look at the gross margin of workday

>> in the early days. So interestingly the software had around 80% 75 80% gross margin but they were selling services to implement the software at negative gross

margin. So blended you know they had

margin. So blended you know they had gross margin some years in the 50s or even below 50. That was fine. And like I think there was one year if you go to pull up the S1 either it was like the

first or second year they had like negative gross margin which is fine because >> any S1 >> you're going public it's a software company >> like one of their out like original

>> was like we have negative gross margin >> yeah and then it got better obviously and then you know like the year >> the year right before they went public they had great margin >> but like you could see that evolution in

that S1 >> and so to me it's Like if you've been around software long enough, you've seen some of these patterns before. It's like

do not complain about a negative gross margin software company if you're seeing the patterns that you saw in the last phase, which is like people are implementing these things, treating these things as systems of record,

whatever. There's like some kind of

whatever. There's like some kind of gravity around the workflow or the data or whatever. And these things like end

or whatever. And these things like end up becoming a core part of a business.

They're not getting ripped out. The only

way that business account goes to zero is if the sort of business customer goes bankrupt or goes out of business. Like

otherwise, they're paying for this piece of software. It becomes that essential

of software. It becomes that essential to the business. And if you look at that kind of trend, it's like, yeah, this is happening. And I think you're going to

happening. And I think you're going to start seeing the first set of S1s for these AI applications 2027 2028.

And I think people are just going to be really surprised at like how much these companies look like software companies.

It's like, yeah, they look like software companies. And then instead of like

companies. And then instead of like paying a ton of, you know, gross margin to the cloud vendors, we're just paying a ton of gross margin to inference providers.

Like we're either paying OpenAI, Anthropic, Google, we're paying Coreweee or Fireworks for inference tokens. Like that's where the cost of goods is going and that's

okay. And it's like and then companies

okay. And it's like and then companies get better at optimizing that getting more efficient and and so it's a it's a real wave and I

think the private markets have fully realized this opportunity and I think this is why you're seeing like application companies are it's like

a very attractive category for for venture today but I'm not sure that the public markets have quite embraced this and I'm not sure public companies

have quite embraced this.

>> Why not? Because if I'm public market CEO, I look at my stock and I trade like three times revenue or whatever and I'm like >> these [ __ ] kids are getting like 200 times right

>> revenue. That's right.

>> revenue. That's right.

>> And they're so small. Maybe they're like they're growing fast, whatever. Like

>> why why do you not think they pulled the trigger on some of these acquisitions?

>> Well, I think it's human nature. I mean,

imagine you're a software company that's run the company for a long time and you're trading at three times revenue as a SAS company and you're like, "Okay, I

should go buy the AI version of this thing and you have to now pay 20 times revenue, 50 times revenue.

You're at 90% gross margin. your AI

alternative is at like I don't know let's say 20% gross margin.

>> That's like a pretty sort of like bad deal.

>> Yeah. Like [laughter] like it seems pretty stupid the way you just described those numbers.

>> That's right.

>> Sounds not good.

>> So like imagine being in that room where you're trying to like pitch that deal like to your management team or to your board or to yourself >> and it's probably like 20% of your market cap or something like that where

it's like a almost a bet the farm.

probably close to that threshold.

>> This is again I would just ask people to look at the public markets of 2012 and look at where SAS companies were trading then and compare them to their on-prem

rivals. SAS companies were trading at

rivals. SAS companies were trading at like 20 25 times revenue. Like if you just look at where Service Now and Workday went public like they were

trading at at the time and you could just look at their coverage of these like valuations people would calling them absolutely insane. That's what they were like calling them and Salesforce

was always considered an ultra expensive stock in the beginning. So was Service Now and so was workday. They were all considered like wildly overpriced.

>> Well, a lot of it too was just they don't have any cash flow profitability.

Like if you just straight to pull up the financial statement, you're just like, "Oh, >> like according to the statements, free cash flows, they're trading at 800 times free cash. It's overvalued."

free cash. It's overvalued."

>> Sure. All that is fair. But the

interesting fact on Service Now actually is that they were cash flow positive from like year two or something.

Something outrageous. It was like an ultraefficient business.

>> But most good businesses are >> Yeah. becoming very capital efficient.

>> Yeah. becoming very capital efficient.

Like Salesforce was very capital efficient too. So if you just if you

efficient too. So if you just if you just look at 2012 as a case study look at where SAS companies were trading and look at where the on-prem vendors were trading and we just you know I just talked about how like those on-prem

vendors should have bought the SAS companies they should have just paid 20 or 30 times and that would have been the right business answer.

>> Yeah. I've heard actually from Beni off it's like he would have sold it was just he needed a 40% premium and people would only offer him 30%. He just never sold because he never got the price he wanted.

>> There you go. And so

there were good deals to be had, but at the same time they look like bad deals to the on-prem companies because it was like I'm trading at 20 times free cash

flow or two times revenue and you want me to pay 25 times revenue and 800 times free cash flow to buy this like SAS thing? Like it seems like

thing? Like it seems like >> a fad. SAS could be a fad.

>> 100%.

>> SAS could be a fad. AI could be a fad.

So this is where like you get into this circular thinking of like not doing the deal and you just get stuck. And I think like if you just play this out, these AI

application companies were much cheaper to acquire in 2023 than they were in 2024, they were in 2025, than they're going to be in 2026, than

they're going to be in 2027. And it's

happening. It's like happening right in front of us. Like we're just seeing this happen. And I have to tell you from a

happen. And I have to tell you from a venture investor perspective, it's a really fascinating cycle to live through because as an investor in the first

cloud cycle and you know I would always think like why aren't these people making the move like they should buy these SAS companies like this is obviously the logical thing

to do and here we are again for me like sitting in a second cycle and I'm saying the same thing and it's really interesting that the SAS companies have forgotten

their own state >> that they were in like they have forgotten their own position in 2012 and 13 and 14 and what it would have taken for an incumbent to buy them.

>> Yeah.

>> They [clears throat] are now the incumbent and not sort of embracing what it takes to to buy the upstart.

>> So if I'm an upstart, like if I'm the founder of an AI company and I just heard everything you just said, why would I sell? Because I'm going to beat the SAS companies in three years. Yeah.

>> You'll be bigger than that. Why would I sell to that?

>> That's right. I mean, if if you you know, like you mentioned, some founders just know, you know, there's a number and you know,

there's all these famous stories about like Google named a price to Yahoo and Yahoo said no, Yahoo could have been like the largest company in the world.

Don't Google, Facebook, right? I bet

they talked to Amazon.

>> Oh, sure.

>> You know, there's a story of like Facebook and Yahoo, something like Yahoo ordered a billion and then >> there's some kind of counter or something whatever. Like there are these

something whatever. Like there are these famous stories of like >> and Zuck was like, "Well, what would I do if I sold? I would just start another social network."

social network." >> That's right.

>> So, why would I sell it?

>> That's right. So there are there are these like notes in history where companies they named a number to the incumbent and said like okay you just have to get here.

>> Yeah.

>> And of course in some cases the incumbent did get there and that's how you have like giant SAS acquisition or the incumbent didn't and then you know those companies went on to be

independent and got gigantic. And so

I think that some founders may just have a number in mind that if the incumbent hits like you know 40% premium to you know their current stock price or whatever like

that might be attractive but I think like what I'm surprised by is how few people are trying like you would I would have would have expected

many of these doors to be you know M&A people all over those those companies being like What would it take?

>> Do you think part of it is that there's so much latestage capital that if I'm a founder, it's it's not as hard as it maybe could be or should be to fund

raise. And

raise. And like, yeah, I could take a deal, I could sell to Salesforce, but also like there's like 18 people that are giving me $100 million and to keep going. Like

it it actually makes that easier. It

does to stick on the path.

>> Absolutely. The private markets have gotten way bigger today than they were in 2012 and 2013, 2014. So that makes it much better.

I think the other part of it is going public has gotten harder for companies and I think there's >> Why is it harder?

>> It's the same thing.

>> Yeah.

>> Right. Like I mean what's so hard about it now? So the difference between today

it now? So the difference between today and even like 2007 and 8 is like the number of things you have to do to be a public company. There's just more to do

public company. There's just more to do now. Is it really difficult? No.

now. Is it really difficult? No.

>> Just hire a couple more people.

>> Hire a couple more people, pay a couple more consultants and they'll do it for you. So,

you. So, >> and so what I think is going to happen [clears throat] and I think you already see it from the bankers

>> is there is a growing demand from public software investors, software PMs >> because they're looking at their universe and they're like this thing shrinking.

>> That's right.

>> Like what is up with this? So they're

you can already tell like from our conversations with investment bankers, they're already telling us like the software investors are telling us to bring them the AI application companies.

And so for the first time in a long time, you're starting to see investment bankers talk to companies under $und00 million of run rate saying, "Do you guys want to start doing non-deal road shows

where you start meeting like PMs of public software investors?" Like people, you know, this is quite a shift. I mean,

a couple years ago, people would say like, "Oh, you need 500 million of ARR before you can like talk to any public investors cuz like if you don't get there, like nobody wants you to go public."

public." >> Yeah.

>> Very different. Or is like we just had a conversation with a banker who was like who wants to organize a non-deal road show for one of our

companies and the company's not yet at 100 million because it's like fundamentally really interesting technology and there is a great deal of demand from

public software investors to meet these companies and like for the first time in probably a decade I'm hearing bankers say things like,

"Yeah, 100 million AR, we could probably take that public." Haven't heard that in a while.

>> Really? And it's and it's probably just because I mean it's really the companies are growing fast and that's all investors care about. They just want you to grow as fast as possible. That's

right.

>> In a in a >> semi-healthy slash will be healthy at the end state.

>> That's right. I think if you just look into the public markets, how many companies in software are growing greater than 30%.

>> Isn't it's like zero.

>> That's right. [laughter]

Yes.

>> So you you can't be growing less than like 3x to raise a like a series A in venture land.

>> There you go.

>> Like if you're like below 3x your growth like I would say it's probably better just get the growth rate up. Like figure

out how to grow faster versus spending time.

>> Actually I think it's like the 3x thing is probably overdone. Like I think if you're growing like two and a halfx still pretty >> okay. Well fair. Yeah. Like

>> okay. Well fair. Yeah. Like

>> but there's like a lot of private companies like double from 50 to 100.

Like that would be really attractive.

>> And there are a whole bunch of private companies that want 50 to 150.

>> Yeah.

>> And it's just these public market PMs are like give me that. Like I want that.

>> That's right. That's exactly what they want. And they know because you know

want. And they know because you know public market PMs also are stepping into private markets and meeting these private markets companies on their own

>> and saying like wow there's like a ton of growth in these companies and if you're sitting here as a software PM you're looking at your universe of software companies that are available to you as a public market investor.

>> Yeah.

If I was sitting that seat, I would be demanding the bankers bring me >> give me this >> like applications because again like if you're a software PM that invested

through SAS and I was talking to a hedge fund manager who was at one point through SAS he was telling me he was long on a hundred software names.

>> Okay, probably all growing like 50% a year. He was like we, you know, he went

year. He was like we, you know, he went long software starting in 2010 and he just decided like this is clearly the future and like every time a software company came public like he figured out

a way to enter that company and like they were extremely successful etc. And his comment to me is when are you bringing your AI application companies

to the public market? Like we need those in the public market because we're completely starved for growth and all the growth is being just taken

by these AI native companies. Like

they're all taking all the growth. Like

if you just look at net new AR added, where is it going? Like it's really just going to all these like AI companies. I

think maybe you saw this stat. You might

know what I'm talking about. Since Chat

GPT launched, I believe this is about a quarter ago that I saw this stat that OpenAI and Anthropic added as much revenue as every single publicly traded software company. Did you see the stat?

software company. Did you see the stat?

>> I buy that.

>> And it's I mean this was three months ago, so it's probably even bigger now.

>> Right. And so, you know, I think you know there was press about OpenAI revenue that had gone from like 6 to 20 billion this year. I mean,

That's a lot. Yeah.

>> That new 14 is a lot.

>> Yeah. Well, and part of the argument though for some of these AI companies is like, oh, the the valuations are so high.

>> Yeah.

>> Like how do you square that up then if you're like >> you're you're thinking about like what am I investing into? Maybe you're doing a series A, you're doing a seed round or you're doing >> it's a public company, but like >> that's right.

>> How do you justify the higher valuations on some of these companies?

>> I think it just depends on your fund size and your strategy. So, we have a very specific fund strategy. We're a

$500 million fund with four equal partners.

You know, you you know Eric, you know Ev, you know Peter, you know me.

>> I actually don't know Peter. I've never

met Peter before.

>> Well, >> I want him on a podcast. It'd be cool to meet him and have him on some time.

>> So, it's four of us and we're investing in seed and series A companies. And the

primary goal of each of our investments is that we want to be the primary board member for the company. Like that's the that's the goal of every investment. And

if a company's not looking for that then like we kind of don't have a role to play. And so you know the valuation

play. And so you know the valuation frankly is like not the governor in our investment decisions. Like if you just

investment decisions. Like if you just like if you were to be you know a fly on the wall in one of our partner meetings like the discussion really isn't about

you know the valuation or the deal structure. Like I just we don't spend

structure. Like I just we don't spend very much time on that at all. That

conversation is really about the company >> and does the partner that's advocating for the company, you know, want to work with that entrepreneur and do the rest of us want to work with that entrepreneur too and

help them support and build something really meaningful? And oftentimes what

really meaningful? And oftentimes what you'll find in our conversations is that when one of our partners is excited about a company, you'll quickly find that the other three partners like

encourage you to like lean in. I think

this is like where our incentive structure really helps because we all share economics equally famously. And so

if my partner is excited to work with an entrepreneur to help them build something big, I want them to go do that. It's like, "Yes, please go invest

that. It's like, "Yes, please go invest and >> go make me money."

>> Yes.

>> 100%. And so our incentives are fully aligned on that. And so when somebody gets excited, it's very clear like the the firm like helps rally and sort of

like helps them gain elevation, helps them finish that that investment. And so to us like that's the governor and it's and

then the other side of that is also like by you know having four partners each of us probably has capacity to do two investments a year and so as a fund we're doing call it eight nine maybe 10

investments a year >> someone gets really excited in one year.

>> That's right. And so and so that means that you have on any given day sort of like your time as the governor of like where do you want to spend time with? who do you want

to spend time with? And so that's ultimately it and that's our strategy.

And so that means that we have decided that that means that there's a typical investment size and a typical ownership that we'd like to go

for. And of course, we're very flexible

for. And of course, we're very flexible on that. It's like there's no rules.

on that. It's like there's no rules.

Like we don't have written rules that say like you have we're only going to do it if we get this much or that much.

>> The classic venture model is like 20% series A. Yeah,

series A. Yeah, >> $15 million check or something like that. Wow. Or maybe,

that. Wow. Or maybe,

>> you know, when I started in the business, it was a little bit less.

>> I don't know. It's just all over now.

We're doing I saw a four million4 billion seed round the other day. I

don't know like what what is this anymore?

>> But I think for us >> to be clear, we still have those rounds where you can write small checks and get meaningful ownership. You're just like

meaningful ownership. You're just like this is part of the incubation effort that we have. So we have ERS. were like

helping incubate companies and I think those opportunities still exist when you're building relationships that early and there's certainly like companies that are much further along that you know have a little bit of traction or whatever and they're commanding a

different market price and that's okay and like for us as long as we have a relationship with the entrepreneur and can serve on the board

like that's cool like it's we're flexible on that. So, what's the most untraditional kind of venture round? Like when I'm

like a venture purist, I would like scoff it. What's like the one you think

scoff it. What's like the one you think they would be like the least characteristic?

>> You know what's really interesting is that if you look through the history of Benchmark, there are times when Benchmark did these like non-traditional investments. So, if you look at the

investments. So, if you look at the internet era, I don't know if you know this, but like Nordstrom spun out Nordstrom.com and Benchmark invested in that corporate spinout as an example.

>> I know you guys invest like Jamba Juice.

That's probably the craziest one.

[laughter] >> So, so if you want to scoff at sort of like traditional venture, like there's like examples like this throughout our history. M

history. M >> and perhaps the most famous and I remember because I was I was just entering the ecosystem then was when Benchmark did the growth round at

Twitter. That was like a very unusual

Twitter. That was like a very unusual move for Benchmark at the time.

>> Was it like a series C or something?

>> Yeah, that's right. Okay. And um it was like may have been a series B or a series C and that was like considered a late stage round at that time. And it

was very unusual to see a a very early stage firm like Benchmark do that round.

And so I think the thing that that people like this narrative of like somehow there was only like one set of ideal deals that Benchmark has ever done

for since the founding for you know 25 years and like all of a sudden like the model has to change. Just like no, the model has always been you have a small

set of partners working with companies that they really want to work with. A

set of partners that really want to buy a certain of like fundamentally game-changing ideas that end up becoming

really large standalone companies. And

that idea then has resulted in lots of flexibility on the other side of like what does that structure look like? And

the one thing that that we haven't done is created a fund of family of funds and we haven't gotten big as a partnership. We've continued to be small.

>> You kind of there was an era like early benchmark. You like went to Europe. I I

benchmark. You like went to Europe. I I

remember >> like an Israel fund maybe too.

>> That's right. There was benchmark Europe, benchmark Israel, benchmark US. And you know, I was in the

benchmark US. And you know, I was in the venture business then, like I wasn't at benchmark. Um, but yeah, Benchmark had

benchmark. Um, but yeah, Benchmark had and then decided to get small again and we've been small since.

>> And you think that was the right move?

Get smaller.

>> Absolutely. I think now other people have built incredible franchises by getting big.

>> Yeah. There's people that do more in management fees every year than your fund size.

>> That's right.

>> So, multiples of our fund size in management fees. Yeah,

management fees. Yeah, >> I think it's a great business and I think like I think you'll eventually see venture firms that are public too. I

think that's okay. I think that's great.

>> And I think ultimately you have to come back to the partners themselves and what kind of organization do they want to be part of? And for us, like we want to be a part of an

organization that where you could do deals like Manis and Sierra and Lora and Fireworks and Lingchain all in a span of 12 months. Like all of those deals are

12 months. Like all of those deals are completely consistent with how we want to practice the venture business when specifically in all of those you have a benchmark partner partnering with a

founder, joining the board and working with the entrepreneur to create a really big business.

Do you think that is maybe like an outdated model of like that we must own 20% in your series A like that that approach of like I have this rigid

portfolio construction based on the rules of that have become memes over the over the years whatever like is that not a good approach to venture anymore?

>> I think everybody should approach venture however they want to approach it. I have like everybody's on their own

it. I have like everybody's on their own journey. Everybody has their own

journey. Everybody has their own strategy. This is like the most but I

strategy. This is like the most but I mean I've heard like Eric told me he's just like we're trying to find people building the best companies and just >> be there and own a part of it and that's how you make money for sure.

>> And I think like >> look there is a model that works really well which is the YC model. They have a very specific structure, a specific amount of money, there's an incubator program, and there's a specific

ownership on the other side of that. And

I think that's a fantastic model. And I

think YC is like great value for founders. Like I like whenever founders

founders. Like I like whenever founders ask me the question of like should we do YC? I say absolutely yes.

YC? I say absolutely yes.

>> A good chunk of your like personal portfolio companies have done YC. That's

correct. Yeah. Like I am a huge fan of YC. I think it's like a great program.

YC. I think it's like a great program.

I'm a big fan of the partners there. And

so I think like YC has a very specific structure.

>> There are other incubators and other early stage funds that have very specific things that are like we only want to do deals that are this specific check size, this kind of ownership. And

I think that's a winning strategy as long as you don't have FOMO and you like very specifically focus on, you know, I only want to do these kinds

of investments. Like okay, great. But

of investments. Like okay, great. But

the way we're structured is we're generalists. We're a group of

generalists. We're a group of generalists and we want to invest in really exciting companies. And look, the last investment

companies. And look, the last investment I did was a crypto company.

>> This just FOMO.

>> That's right.

>> I was like, what? What the hell is this?

[laughter] I was like, what? You're like

enterprise. I always thought it was like enterprise software. That's right.

enterprise software. That's right.

>> Man, it's consumer AI. Yes. And crypto.

>> Yeah. Consumer crypto. So if you look at the two investments that I made in 2025 were both consumer apps.

>> So >> are you rebranding or going through like like a phoenix moment of like enterprise SAS has been burned to the ground by the public markets?

>> But I think like fundamentally if you go back to again what is that motivated by?

I just was I just thought the entrepreneurs in both cases were extraordinary. And as a generalist you

extraordinary. And as a generalist you kind of understand what they're working on. you have a deep appreciation for the

on. you have a deep appreciation for the product they're building and how they're approaching the problems. And honestly, like I just wanted to work with both of

them. Like I just really think like at

them. Like I just really think like at FOMO, it's Paul and Se. They're amazing.

Like they're just extraordinary entrepreneurs that want to bring a totally brand new experience of crypto

to consumers. Like I'm you're well worse

to consumers. Like I'm you're well worse than this worse than crypto.

>> I mean I don't know. I'm like it's all a scam. Like it's [laughter] just people

scam. Like it's [laughter] just people scamming you. So what's the difference?

scamming you. So what's the difference?

Are they scamming you like a more polite way?

>> Absolutely not. Crypto is to me the way I think about crypto is that it's it has a huge on-ramp and like there's a big barrier to entry. Like I think that's

part of like why there is fraud and scams is because like it's very hard to onboard onto crypto and hard very hard to manage crypto.

>> Yeah. I remember buying my first NFT like everyone's like oh this is the future. It's so easy. It took me like 30

future. It's so easy. It took me like 30 minutes >> get metam mask up and like I bought this NFT and I was like >> there's no way there's no way this is like your plane ticket is an NFT. Like

come on. This is not >> the experience is so frictionful that I to and then it's like very easy to like analogize back to my own personal experience which is like crypto to me

has been hard for me to experience as a software investor >> because it was such a frictionful experience to get crypto or to get an

NFT or to experience anything on chain and I met Paul and say and they told me to download the FOMO app and try

something and I did and it was like consumer grade and I was like whoa and there's a built-in social graph and all this kind of stuff with like UGC all these kinds of elements that are like

very classically software just apply to a new industry. If I

search FOMO in the app store will it come up?

>> Yeah, absolutely. And you know I think that it's these are the kinds of things that you know I look for. It kind of begs another question of what what do

you or what do you feel like benchmark looks for in founders that you're trying to back? Like we've talked a lot about a

to back? Like we've talked a lot about a bunch of different stuff, but if I were to like sound bite this, what is it that you guys are looking for? The through

line for the entrepreneurs that I've worked with is that they have some deep insight on the problem they're really passionate about. And it's it could be

passionate about. And it's it could be just like serially going backwards from like the investments I've done. It's

like could be consumer crypto, could be consumer AI, could be legal AI, could be document processing, could be sales tax, like it could be stable coins, it could

be payment rails, it could be, you know, integration software, all of these things. The common line in all of them

things. The common line in all of them is, you know, I'm typically investing seed series A. Usually there's no product, usually there's no revenue, usually there's no metrics.

Like for example, Manis I did pre-launch. like this was a beta product

pre-launch. like this was a beta product and there's some deep insight that the founder has some deep perspective that the founder

has and intuitively as soon as I hear that you're like yes that's absolutely obvious and that's how the world should function >> and you know when I hear that it just I want

to work with those founders and for me that's like the the number one thing above all else and the great thing about being an early stage investor is you get that to go with these founders on these

journeys and then it's okay if it doesn't work. Like the thing that is a

doesn't work. Like the thing that is a mistake as an early stage investor is missing out on the companies that work, not investing in companies that don't

work. If a company doesn't work, it's

work. If a company doesn't work, it's okay. Like it's a 1x error. If a company

okay. Like it's a 1x error. If a company works, it's, you know, it can generate a lot of returns% >> IRA.

>> Yes, that's right. And so you just want to be in companies that that work. And so you don't you don't worry about the downside. And so

when you have this like view as I do that you just want to work with founders that have a deep passion for a sector or a problem and have some unique insight

as to why that opportunity is now available and why they're uniquely positioned to address this problem. I

want to back up. May maybe this is an interesting probably like last thing we can talk about. You told me that one of those interesting insights was with codegen some like things that you saw some stats.

>> It's also a company where they publicly there's like negative gross margins.

There's people like oh these companies are >> they you read like some of the consensus maybe like a year or two ago it's like six months and these things are going bankrupt, >> right?

>> So how did you what was the thing you got excited about there and then how the margins how did you get comfortable with that? Look, we're investors in cursor. I

that? Look, we're investors in cursor. I

think code generation and look, one of the man's primary use cases was also code. I think that we're very early in

code. I think that we're very early in how much code can be generated for the world. And two, the thing that surprised

world. And two, the thing that surprised me is how much demand there is for code generation across consumers, B2B, proumer, like there's just massive

demand for code generation products.

I do think that the margin question at the moment is a little too early to make a final verdict on like we don't know and the thing that that is happening

that you [snorts] may have seen for example is like we're Eric is on the board of a company called Cerebras which is a very specific AI chip that speeds up inference

once that chip sort of starts to propagate and you start to see AI technology run on that chip as an example It's like an AI native chip, right?

Where it's like it's like built for running AI native workflows on top of >> so inference goes way faster on a cerebrous chip as an example. So if you

speed up inference dramatically. So the

thing we don't have yet is we don't have AI specific chips beyond Nvidia. We

don't have AI specific clouds. We're

starting to get that.

>> We're starting to get AI chips. We're

starting to get AI clouds like fireworks. We're starting to get AI

fireworks. We're starting to get AI infrastructure built. Once all of that

infrastructure built. Once all of that gets built, then we're going to start to see a stabilization of like the infrastructure parts and then only then are we going to actually understand what

the gross margin characteristics of these things are going to be. But right

now, I think it's like >> too early to judge the P&Ls of these things. All you can actually just get a

things. All you can actually just get a sense of is the consumer, proumer, and B2B demand. And right now we haven't hit

B2B demand. And right now we haven't hit the ceiling of that demand. Like the

more we produce coding models, the more we generate code, the more we make code generation faster or more efficient or more accurate, if there just seems to be

more and more pull of it. And I think like if you just look at the amount of revenue generated by code generation, it's gone zero to like a couple billion

really fast. And you can count that at

really fast. And you can count that at the inference layer. You can count that at the application layer, whatever you want. Like it's probably the fastest

want. Like it's probably the fastest growing software market in the world right now. So you can judge this demand

right now. So you can judge this demand side of it. And I don't I think it's like way too early to understand what the long-term margin characteristics of this sector is going to be.

>> I was Is there anything else you want to talk about at all?

>> No.

>> Perfect. I had a bunch of other stuff. I

know. We got to we got to get going.

Sorry. I probably need to eat this banana before we cut the we cut the filing. Actually, do you know how to

filing. Actually, do you know how to break a banana in half?

>> You ever seen this?

>> Wow.

>> Have you ever seen that?

>> No. That's amazing.

>> Yeah. It's my one of my skills in life is opening.

>> Well, this is a lot of fun. Thanks for

Thanks for having the other banana.

>> I'm [laughter] good.

>> That's a good place to cut it right there. You refusing my banana offering.

there. You refusing my banana offering.

[laughter] [gasps] >> And thank you for listening. A quick

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