The #1 Mistake Killing B2B Startups | Arun Penmetsa, Storm Ventures

Can the biggest AI labs simply walk into any market and replace the software companies already sitting there?

Storm Ventures has spent 26 years building an answer for B2B founders. The firm has made close to 200 investments, backed 11 unicorns, and produced some of the cleanest enterprise exits in Silicon Valley, from AirGap Networks selling to Zscaler to earlier winners like Marketo and MobileIron. Its portfolio today runs from Tekion in automotive retail to Atomicwork in IT service management, Krisp in voice AI, and Synthpop in healthcare.

Arun Penmetsa is a Partner at the firm. He built enterprise software at Oracle and Google before moving into venture, and he now leads Storm's work in AI, security, and digital health. In this conversation, he is unusually specific about how the decisions actually get made. He meets ten to twelve founders every week; the entire partnership makes only six to eight investments a year, and the single filter he trusts most is urgency. If a buyer can comfortably wait six to twelve months, the pain is not real, and the company is already in trouble.

Arun is direct about the question every AI founder now gets asked, which is whether OpenAI or Anthropic will simply take their market. He answers that the foundation model companies will own a handful of core verticals and leave the rest, so the durable business is the one that owns the full stack between the model and the interface and delivers a real outcome inside law, healthcare, construction, or a market as unglamorous as convenience retail. 

He also lays out where he is placing his next bets, from physical AI and humanoid robots that can retool an assembly line on the fly to vertical companies like Tote that rebuild the software running gas stations and corner stores, where a single day of downtime can cost an owner tens of thousands of dollars. 
If you are excited about building enduring B2B and AI companies, this episode is for you.

00:00 - Trailer
01:21 - The 26-year-old B2B fund behind AirGapp, Marketo and Tekion
02:47 - The patterns Storm has seen repeat across 200 investments
05:37 - What actually drove seven clean exits
06:37 - The airgap story: the agentless bet that Zscaler bought
13:10 - The one signal Arun trusts more than pedigree
14:37 - Where the moats survive once the models get this good
18:01 - Why Storm backed Atomicwork against ServiceNow
20:00 - Two weeks to decide: how the fastest deals happen
25:27 - The cold email that gets a venture partner to reply
28:52 - The funnel: 500 founders a year, 6 to 8 checks
35:31 - Why Stanford on the resume buys you nothing here
38:13 - The pattern behind every 4 billion dollar outcome
40:44 - The question every AI founder gets: can OpenAI replace you
46:16 - The mistakes that killed companies Arun believed in
50:14 - The million-dollar wall every B2B founder hits
53:30 - Why buyers have already decided before they call you
58:12 - AI agents that stay on a one-hour insurance call
1:08:33 - Physical AI: assembly lines that rebuild themselves
1:12:39 - Tote: the fuel pump they built to win gas stations
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This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.

Transcript

SPEAKER_01 0:00

I can explain the week to be I think that only because one of the things when before we invest in companies, we think a lot about is what is the famous problem and how urgent that famous. Is the problem something that needs to be solved today, or can I wait six to four months? So we spend a lot of time thinking about urgent things.

SPEAKER_02 0:21

Where do you think the most important application?

SPEAKER_01 0:29

I think it's very hard to do. The models will get better at all of these, but their goal, at least today, would be to provide the best model that they can. Everything you build on top ultimately comes down to somebody else. And I think there's a big gap between the application and the model. So go build for that gap.

SPEAKER_02 0:45

What's been you're learning from companies that you were very bullish about invested in, but they didn't work out?

SPEAKER_01 0:52

I think.

SPEAKER_02 0:57

Hi, this is Siddhartha Al Waliya. Welcome to the Neon Show. I'm your host and managing partner at Neon Fund, a fund that has invested in some of the best enterprise AI companies that have come from India building for the globe, like SpotRuff, CloudSec, and Atomic Work. Today I have with me Arun Pennasta. Arun, welcome to the Neon Show.

SPEAKER_01 1:15

Thank you, I'm Nisid. It's great to be here.

SPEAKER_02 1:16

Yeah, Arun. So Storm is a legendary name in venture capital in Silicon Valley, right? It's a 25-year-old institution, right? You are on to the fund 7 now, if I'm right. It's a 200 million plus fund. Yes. And you have back some of the great names uh across the US India corridor, as well as only US names, for example. Uh a name that everybody knows in India is Techyon. Right. Right. In India. Uh and uh the company today is more than 300 million in AR last valued at more than uh 4 billion. And Storm has special expertise in B2B.

SPEAKER_01 1:50

I think you have all always then only B2B. That's right. Yeah. Yeah. Storm, as you said, uh you started out about now 26 years ago. Uh I think 2000 was the first fund that took outside capital. There was a fund before that that the founders put money in themselves. But yes, we have uh focused on B2B throughout throughout history. Uh, and we can go into reasons why. Um, but uh yeah, it's been a great journey. We've been fortunate to invest in companies like Tekeon and and many others with amazing founders. Um, and hopefully there's you know a good journey for the next 20 years.

SPEAKER_02 2:24

And and you have also been on the first check in Vakato. Wakato is also you know 100 million plus ERR. Last public numbers are like 5.7 billion uh in market cap. Right? So uh the first is uh what has helped Storm build the right to win such companies?

SPEAKER_01 2:42

Yeah. So uh yeah, Worcato wheel at the CDZ. I I don't remember if you were the first check, but uh but yeah, um but I I think there's a few things that we tend to spend our time on. One, I would say um given our B2B focus, one of the things we think very deeply on is uh what are the patterns that we've seen repeat over time, right? I think we've done something like 200 investments over that time frame. So we tend to think a lot, especially on the go-to-market side. What does it take for a company that's has some revenue to transition from founderless sales to being building out a go-to-market team? How do you think about the right go-to-market for your company? How do you think about the right pricing for your product that that go-to-market can eventually support? Um, how do you think about hiring the right people to fit that go-to-market? Um, so so one, I think we have seen patterns over the years. And we had this conversation before the podcast started, but but if you think about B2B, like every sector is like is different, right? You know, you have cybersecurity, you can have sales tech, you can have healthcare or construction. But one thing we've found over the years is when you sell to a type of buyer, whether it's SMB or enterprise, the process uh tends to have patterns. How do you think about figuring out the right pain point? How do you think about bringing the rest of the organization along in addition to your champion? How do you think about moving deals through procurement and legal? How do you think about objection handling? How do you think about compliance? How do you think about the right way to price deals? So I think one of the skill sets that we bring is as you build out your company and think about go to market, is how do you structure that for success? I think that's one. I think second, we tend to be, or at least we think we tend to be, uh, relatively good partners for the long term, right? I mean, Storm has been involved with companies that have done really well and we've been with them throughout the entire journey. So, you know, our goal is to help the company and their founders succeed. So I think founders appreciate that, you know, we we are in many ways easier to work with and tend to work throughout the journey. I think third is also just economic cycles, right? Um, you know, I joined Storm. I wasn't there since the beginning, but like, you know, if you think about the founding team, I mean, they have seen, you know, depending on how you count, two or three economic cycles, right? So that's one benefit I get a lot when we're sitting in these meetings is you see valuations going up, you see valuations coming down, you see buyers being very aggressive adopting software, and you see periods where they're pulling back. There's no budgets for anything. So I think just having that perspective and that long-term view could also be helpful to founders as they think about what's next. So those are some reasons.

SPEAKER_02 5:33

Yeah. And there have been like uh seven great exits in the recent years. Airgap is one of them, Dasera is one, TrueStar, Forme. So Forme became Zerant, right? Right. And they acquired one of our portfolio companies, NT. Oh, great, yeah.

SPEAKER_00 5:48

Fantastic.

SPEAKER_02 5:49

Right. Small world. Then there's Rally Team, MyLI, Limbix, right? And they all have been great exits. Can you talk about some of these exits, what have led to them uh in in the last, let's say, few years?

SPEAKER_01 6:02

Yeah. So I think there's a few patterns. I think one, I you know, as far as I remember, in every one of them, the founders have deep, deep domain expertise, right? They really understood um the pain point. And this is something we can talk about more about. But one of the things when before we invest in companies, we think a lot about is you know, what is the uh what is the pain you're solving and how urgent that pain is. Um, and I think the urgency part is hard to figure out um all the time. Can you give examples? Yeah, so like, you know, if you think about Air Gap, right? Yeah. It depended on the customer, but but when they were trying to build out sort of this, they start out in network segmentation and eventually they grew a lot, a lot more. When the pandemic hit and you had all these factories where you still had to come in and do work, um, you couldn't work remotely, right? I think the pain really was you know, attacks are happening, the pace of attacks is security attacks is happening, the pace of them is increasing, they're getting more sophisticated. How do I build my network to protect against that? And when we think about urgency, we think of it more as is the problem something that needs to be solved today, or can I wait six to 12 months? Right? Because if you can wait, yes, if it's a great solution, I'll still buy it, but it's not truly urgent. It's probably in my not top, not in my top two or three list. So we spent a lot of time thinking about urgency. So I think in in in all these companies and to varying degrees, right? Uh, the founders had a good idea about where the urgency lay. Now, some of these exits were better than others, so the the exact level varies, but I think that's that's one. I think deep domain expertise um gets you that. I think the second thing is if you have a good product sense, that also helps trying to understand. So I think I think that was one. I think second, you know, if I think about all those uh founders, they were very, very good at product, right? They really understood what the customers were looking for, how to build for that. And you know, this is sort of a cliche today, but it's very important is they're always customer first. Whatever the uh they really want to solve the problem for the customer. So I think that is the second one.

SPEAKER_02 8:16

Yeah. And uh was it the industry also that influenced these exits? Like they were sitting in the right industry at the right time. Maybe you can give it a question.

SPEAKER_01 8:24

I mean, some of it, yeah. Um, you know, I think again, if you think about uh just because we meant maybe you mentioned uh uh Air Gap, and I'll mention one other example after that. I think the product that they built became very strategic because at that time there weren't many companies that were agentless. So, you know, Airgap didn't have to deploy agents on every device.

SPEAKER_02 8:47

And can you maybe let's take the case study of Air Gap? Yeah, what did they do? How did you meet with the founder? Yeah, how much did the raise and the channel?

SPEAKER_01 8:55

So I met Ritesh, gosh, I'm trying to remember now. I think I probably met them through the seed investor, uh, maybe about six to nine months before we actually invested.

SPEAKER_02 9:03

This is which year?

SPEAKER_01 9:05

Uh I want to say 2021, maybe. Uh, I think that's right. Yeah. Uh, because they exited in 24. Yeah, so it must have been 2021. Um, and uh um so um so I met him uh early, and that at that at that point the company was very early. Like I don't think they had any revenue. Rutesh had an incredible vision for where he thought networking should go and how do you build the right infrastructure to secure it? And this is sort of a very simplified version, but a lot of security when you think about networks is that one of the easiest ways is to put uh agents on every device. Yeah, you're a small piece of software that is tracking what's happening on the device, whether it's the server, whether it's a camera, anything, right? But obviously, putting agents on something is work. And in many cases, even customers don't want to manage thousands of agents running in their environment. And this becomes even more hard when you go into something like a factory, which are probably running machines that are very specialized, and they are probably running some OS that is 15 years old because you don't want to touch these and upgrade these, because even a small amount of downtime can be extremely costly. So, one of the innovations that AirGap did was they came up with this agent-less architecture where they would sit sort of sit in line so they would see all the traffic, and based on their product, they would figure out what looks like a threat, what doesn't look like a threat, how do I manage these devices and all that. So it was especially for that market, the the non-IT sort of the OT market. Um, that was really, really powerful, right? Because these factories and industrial sites, or in some cases, even campuses, didn't want to suddenly deploy tens of thousands of agents in the network. So that was what they're doing. The team was fantastic. Um, you know, they had deep networking background, they had been at Juniper before and a number of other companies. So I think technically they were able to really build the right solution. The second is as I mentioned earlier, they really knew the domain, right? Um they knew, I mean, it's probably one of the best networking software teams or just networking teams in the world. So they were doing this. So I met with Ish. At that time, it was very early. I was intrigued by the idea, but it was just too early for us. We didn't invest. And then I reconnected with him, I think maybe five, six months later, towards the end of 21. And then he had a little bit of fraction and he was trying to raise. So, you know, we liked we liked the team. Um, we liked the idea. We thought there was a path here to a really big business. So we decided to lead the around. Um, the company grew really well after that. Um you know, one of the things that was that was great with this, with this team, and I think you know, a lot of the people you mentioned is just their transparency. They would be very, very transparent with how the business is doing, the challenges they were they're dealing with. That also made the engagement also, I think, more enjoyable because we could have open conversations. And so the company grew well. Um and I think just their position in that market made them very strategic because I think the technology was um uh innovative. The technology made it simple to really sell into large customers. Um, so they were a handful of potential acquirers for that. That eventually led to Zscaler buying them, like I said, in I think it was April of 24. Um, so it's just been over two years.

SPEAKER_02 12:34

Yeah, great exit for the fund.

SPEAKER_01 12:36

Yeah, yeah. I and and I mean, listen, in hindsight, now I I well I did feel it at that time too that the company could have been a lot bigger. But um, but you know, I think given the value that they brought um and what they were doing, I think it was a great exit for the team, for for the investors. I I think I think Zskiller has has benefited from it as well.

SPEAKER_02 12:57

Yeah. So so you mentioned a few things here, right, across these seven companies. You mentioned that the pain has to be there. You validate try to validate the urgency from the customer. It cannot be a six to twelve month bind cycle. Right. Uh, right? Then you signal deep domain expertise that you are looking for in founders. Yeah. Uh right. And very, very good at product. Yeah. Like they have to be customer first, right? Uh is that all the signals that you are looking at to make a yes decision?

SPEAKER_01 13:24

Um, so I think that's a number of it. I mean, the other thing we think a lot about is go to market. You know, can this team execute a go-to-market that, in our view, is very relevant for this industry or the right way to scale? Now, it is very hard to know in the early days what works. A lot of it is experimentation. But when we think about sort of the value we can bring, and having seen this pattern across now hundreds of companies, we tend to spend a lot of time with them on figuring out, all right, listen, you, the founding team, can clearly sell. You know, you have the domain expertise, you have the passion for the space, you have the founder title, um, you know the product better than anyone. But obviously, you won't scale beyond a certain point. So, how do we take sort of the playbook that is in your head and build a team that can go execute it? So, we think a lot about that and the the potential to go get to that next stage. Um now, then you know, I think there's general turn things. We we in most cases we like to have a thesis on the market. There are exceptions to that, but we need to have we we would like to build a view on where we think the market goes. Um, and then where this team has an advantage that is hard to replicate.

SPEAKER_02 14:37

But let's say today the markets are changing so dynamically with yeah, anthropic launching methods, yeah. Uh, and the capability of models is so advanced. Yeah. Where do you think the the modes remain uh in vertical or horizontal applications?

SPEAKER_01 14:52

Yeah. I mean it is getting harder, right? Um, so I think I think listen it it's it's debatable what is a true mode or not. I think execution matters always has always mattered a lot, it matters more. But you know, going back to specifically the AI question, I think if you think about the stack needed to succeed in, especially when it's into larger companies, right? You have the model layer and you have the application layer on top and the model layer at the bottom. If you're just a pure sort of application UI level company, I think it's very hard for you. But between those two, there's probably a lot of different layers, right? There is sort of a context and memory layer, which really tracks how the business actually works, the data that you need to collect. There's an orchestration layer that in in many cases can has to coordinate multiple agents, right? Um, there is a governance and security layer, which is very big, right? Like, how do you make sure this agent says and does the right things at all times? Now the models will get better at all of these, but their role, at least today, would be to provide the best model that they can, right? And everything you build on top ultimately comes down to somebody else. But you know, there's always risk that the models will pick a handful of verticals, like you know, anthropic with coding. I think they they want to.

SPEAKER_02 16:13

And they nailed it.

SPEAKER_01 16:14

Yeah, they nailed it, right? So so that is becomes very risky. But do I think they'll build a product for legal that can really work inside you know complicated law firms for healthcare, for construction, for automotive? I don't think so. I mean, they'll be doing everything in the world then, right, at some point. So that is where we think about where these companies can build a boat. One of the benefits, and you know, this is a longer conversation, but like the whole SaaS to AI transition is one of the benefits that some of these legacy companies have. And I'm using the word legacy broadly. There's like companies more than four years old, right? So any company older than four years, you audit them as legacy. Yeah. Is that, you know, I think one they they have spent years running inside their customer environments, right? So they really understand how their customers' businesses work. And I do think in some cases they can build the best domain specifications, right? They won't build the best model, they leverage the model. And in some cases, the model getting better is great for them. You know, I think they have to build the right architecture, right? To make sure governance is there, security is there, they can orchestrate agents and all that. So I think that's how you build the stack. Um and then I think there's also areas like, you know, if you think about healthcare, where the deep domain expertise makes such a big difference because you can't afford to be wrong. But that's how I think about these companies that are now AI first is what are you doing in your vertical or in your specific use cases where, yes, there's a general uh generic prototype you can probably build using the models, but how do you become so good at executing the workflows that they have that they don't want to go with the general solution and maintain it?

SPEAKER_02 17:57

And we are common investors in atomic work, which is a different friend and us visionary entrepreneurs. Yeah. Uh right? What what convinced you about atomic work? Yeah, it was uh it was not a conventional bet with the kind of ownerships and everything. Yeah. So why did why did you move out of the that's right.

SPEAKER_01 18:16

So I think there's a few reasons. You know, as you said, Vijay is an incredible founder, right? Um, and um I didn't know the rest of the founding team then, but I but I'd met Vijay many years earlier. Um, and and I met him a couple of times after that. But I think one is a bet on the founder. Uh, I think two, you know, we had some experience with the space, and we can talk more about you mentioned Formy earlier. So, you know, that was roughly in the same space. Um, so we knew there was a market here. Now, everybody talks about sort of the Service Now disruption story, but I do think there's a pretty big market, even leaving out Service Now in sort of the upper mid-market area, right? Or lower enterprise or however you want to call it. A lot of that is underserved. And it was underserved historically because building a service management platform for that market was tough because, in general, service management has to provide all these functions functionality. It's an expensive, heavyweight piece of software. So it was just hard to build for that. I think for me did a good job building a version of it, especially for the European market, uh, early on, which is why they got acquired. Um but I saw that as the vision that was very interesting. Is if you execute well here, and especially if you can leverage AI to build out parts of this functionality quickly, I think I think the service management market has just gotten much bigger. So those were the two reasons why we invested.

SPEAKER_02 19:40

And uh historically, let's say uh today deals move pretty fast. Like in two weeks, you need to make a decision, right? Whether you are in or not.

SPEAKER_00 19:48

Yes.

SPEAKER_02 19:49

So historically, how much percentage of the deals that you do or invest company you invest in? Uh, you have known with a past relationship, let's say you mentioned some of those names. Yeah. And where uh, you know, yes, it just comes to you and you have to make a decision.

SPEAKER_01 20:02

You know, uh, I mean, there are periods when it when it varies. I would say historically, the vast majority of our deals are sort of first-time founders we've never met before. I think for me personally, more recently, it's become people I know a little bit, but the sample set is so small that I don't want to say like it's it's the trend. Um but but especially when deals move very quickly and the market is turning um sort of very rapidly, like it's easier to bet on proven entrepreneurs, right?

SPEAKER_02 20:34

Do you go for second-time founders then?

SPEAKER_01 20:36

Yeah, yeah, absolutely. You know, we've done, you know, we can talk about companies, but you know, in the last two of my last three deals were second-time founders. Um and it is not like we're specifically targeting second-time founders, although it's great when you get experienced successful founders, it makes our life a lot easier as well. Um but they are usually expensive. They are expensive. You know, there's obviously fewer of them. If you look for successful founders, you know, there's fewer of them, obviously. But you know, we it's not it's not a requirement for us because I think I think the other aspects we talked about, right? Like just the the ability to recognize an urgent pain point, the commitment to build a big business, the speed at which they can move, the domain expertise, the passion, I think that that can be anywhere. So um yeah, I don't have an exact answer on the percentage, but it's shifted over the years. But I think I would uh my instinct is most people we've invested in are first-time founders that we didn't have a relationship with before. And also, as you can imagine, if you spent a few years in venture, you naturally tend to have a network. So you tend to meet people who you've met before, right? When you start out, you don't know anyone. So that by by definition, they'll become people you don't know, right? So yeah.

SPEAKER_02 21:56

Got it. And how many percentages are the company? You have 200 plus companies. Companies.

SPEAKER_01 22:00

Well, 200, sorry, is is roughly we've invested throughout history. Not obviously all of them are still there. Some exited, some some didn't do as well. But I think our active portfolio, uh, I don't have the exact number. It's probably between 60 and 70.

SPEAKER_02 22:16

Understood.

SPEAKER_01 22:16

Yeah.

SPEAKER_02 22:17

So so let's say the 200 companies that you invested in.

SPEAKER_01 22:21

Yeah.

SPEAKER_02 22:21

How many of them were first-time founders versus second-time founders? Would you have some some ballpark ratios?

SPEAKER_01 22:27

I don't have a number. I don't I don't want to guess, but I would say the the major uh vast majority, I think, would have been first-time founders. Yeah.

SPEAKER_02 22:36

God. But for first-time founders, B2B uh sales, especially enterprise sites are tough. Even if they have been in corporate, uh, you know, yeah, once you take away the logo, the sales become much harder.

SPEAKER_01 22:48

Yes. So listen, like building any startup is hard, right? I think even if you have experience in the in the space, you still have because you yes, you can get your first maybe handful of customers through your relationships. But after that, you're selling based on the product, the pain point you're addressing, and so forth. So I think I think that doesn't concern us as much. I think what we think a lot about then is what I mentioned earlier, right? Like, like, you know, Tehi, one of my partners, has built this really awesome framework called um sort of the customer uh hero journey, um, where he he he talks a lot about when you are selling to someone, put yourself in the shoes of the customer more than thinking of it in terms of your pipeline. You know, it doesn't really matter whether for the customer, it doesn't matter whether they're an MQL or an SQL or like, you know, in the demo stage or whatever, right? They are thinking about, man, do I have this problem that I need to solve? How desperate I am to find a solution. So we talked about urgent pain, right? For us, we think that's one of the best indicators of success, especially early on, is if you're truly solving something that's urgent. And I think it's very hard to figure out urgency. You know, when you go sit in the market and say, oh, I'm solving this problem, would you buy it? A lot of people, if it's a reasonable problem, will say yes. But you really have to sort of figure out, yes, I will buy it today. I don't care if it's a rough, unpolished product, I need it desperately now, versus, oh, great, build it out and come back to me in a month and I'll think about it, right? Like that's very different reactions. We think about helping our founders think through what do you say in the sales process to get them to the next meeting? You know, we call them wow factors. We think about how do you deliver value quickly as a product, right? You know, if if you somebody signs up and you go live six months later, maybe that's okay for your product. But the longer the sales cycle goes, the more pressure there is on, sorry, the more the longer the product takes to onboard and launch, the more pressure there is on the sales cycle because it's a very significant decision to try out something. Whereas if something goes live in like two hours, as an example, right? That's much easier for sales because they can say, just try out the product, right? So we think through all of these with the founders. Um and I think that teases out where the challenge is. And listen, we're not expecting people to be perfect, right?

SPEAKER_02 25:13

Sure.

SPEAKER_01 25:13

A lot of stuff has to be figured out. But um in some cases, the benefit of second-time founders is you intuitively have figured some of this stuff out. It is harder for first-time founders in general, but great founders can execute no matter what.

SPEAKER_02 25:30

So, for entrepreneurs listening to it, what would be when they reach out to Storm, let's say via cold versus warm channels, what would their expectation for turnaround times and investment forces look like?

SPEAKER_01 25:41

Yeah. Um in general, I tell people, and there's obviously variance here, is you know, from first mean term sheet, I tell them three to four weeks, right? Yes, some deals have moved faster. I don't think deals have really taken much slower. I mean, there are definitely companies we've met once, we haven't invested, we reconnected four months later and then we invested, like air gap, but I wouldn't count that as one six-month cycle because we paused in the middle. Um, so what I would tell, so three to four weeks is the answer to your question.

SPEAKER_02 26:17

Understood. And just say uh out of the companies that you uh invest in, like how many uh cold reach outs would you would you so cold reach outs is tough.

SPEAKER_01 26:28

Uh honestly, just because we get so many, um, and I know it it's probably not fair, but the reality is, gosh, I don't know. I maybe get 10 to 15 cold emails a day.

SPEAKER_03 26:41

Wow.

SPEAKER_01 26:41

Uh um and this doesn't even include LinkedIn. So I'm if I haven't responded, you know, I'm sorry. It's just that I just unfortunately uh sometimes just don't have time to read everything. Um and then, but maybe this this could be helpful advice. So a lot of cold reach out emails tend to be very generic. Hey, I'm building something for this. Can we meet? Right? Those are just hard to follow up on because you only have X number of days and hours in a week. And if I respond to all of them, I won't have time for anything. I think crafting a very good cold email which says, you know, I am so and so. This is my background. This is the problem that I'm solving. And I think this needs to be solved for this reason. Like this, this is a massive, urgent pain. And then this is our growth rate. We have customers, we have grown quickly. Um I think that is much easier to process and decide whether to follow up on or not. Um, I'm still probably not going to respond to every cold email, but at least I at that point can make a quick decision and you know, at least respond with sorry, this is not a fit or not. So maybe that's that's advice is you know, we've definitely done deals in the past where, you know, in some cases we've done cold deals, but like it's rarer. And in those cases, uh and we've done deals, you know, where we've gone, I think the fastest I've seen first made to Term Sheet, and this was not a cold deal. I think is like 12 days or something or 10 days. And um, and by the way, I'm talking about deals where we led the round, which is which is most relevant, I think, for founders, right? If you're following on, there's more time. But anyway, I think crafting a good cold email um or tying it to one of our existing investments is is easier for the VC to process as well at this point.

SPEAKER_02 28:39

And how many uh entrepreneurs you would meet, let's say in a year?

SPEAKER_01 28:43

In a year.

SPEAKER_02 28:44

Physical meetings that you would do.

SPEAKER_01 28:47

Oh I don't know in a year, but I mean if I think on a weekly basis, uh first meetings, probably about 10 to 12.

SPEAKER_02 28:56

10 to 12 first meetings every week. So you're meeting like 50 entrepreneurs every month.

SPEAKER_01 29:00

Yeah, something like that. Uh now obviously there's like holidays and like you know, during Christmas, I'm not meeting that many people, but like that's probably the average.

SPEAKER_02 29:10

So probably like uh on on a lowercase, 300 companies in a year?

SPEAKER_01 29:14

Maybe, maybe. Uh these are very rough numbers, by the way. I may be off. Now, as I've gotten sort of later in my career, it's I have you know more portfolio companies, you know, that takes up time and all that. When I was early in my career, I probably used to meet like six, seven, eight hundred a year. Because I was that's what that's what that's all all I had to do. So it gets a little bit tougher as you get later because you know, you're meeting your existing portfolio, you're potentially meeting LPs, you're meeting just the broader network, you know, founders who've exited you've met in your portfolio, you need to catch up with them. But yes, to answer your question, that's probably the ballpark.

SPEAKER_02 29:50

Yeah. So I assume across the team of five partners, thousand meetings with new, like what first-time meetings would happen in a year across.

SPEAKER_01 30:00

Oh, across all of us, yeah. That's probably true.

SPEAKER_02 30:02

That's fine. And you would invest in what, 10?

SPEAKER_01 30:05

Yeah, we typically do um, I want to say six to eight a year.

SPEAKER_02 30:10

Okay, like one and a half to one and a half company per partner almost.

SPEAKER_01 30:14

Yeah, roughly. I mean, some years, you know, I've done one deal, in some years I've done three deals, right? So that's that's probably the average.

SPEAKER_02 30:22

Um that's that's pr pretty low. So to build conviction only in one company a year across so many that you meet.

SPEAKER_01 30:30

Yeah, and you know, I I think there's it depends on the level of filtering you do, right? Um, you know, we definitely meet uh companies where we think we're just not a good pick, right? Either for the stage they're at, either in terms of what value we can bring and so forth. So I think I think in general, I think this is probably true for many investors, but like the first to second meeting drop-off is quite large.

SPEAKER_02 30:56

What would be in your case? Let's say if you're meeting 10 to 12 companies in a week, how many of them would you take it to second?

SPEAKER_01 31:01

Maybe two or three.

SPEAKER_02 31:02

Okay, so that's a good filter.

SPEAKER_01 31:03

Yeah. And and also it's not just because of our time, right? We are, you know, one of the things I try at least to be very cognizant of is like the founder's time, right? They're probably meeting 20, 30, 40 VCs. Unless I feel like there is, I think there's a reasonable chance this gets to you know serious diligence, then it's better to just pass early, right? Like their time is valuable, our time is valuable. And the reasons could be many things, right? It could be the the traction is not where we need to be. We we have invested in this space, we just don't believe in that market for whatever reason, right? We have a competitive company in the portfolio. Um, I just don't know. I mean, this happens sometimes, right? There are certain markets, like I'll give you an example, like real estate. I have never done a real estate deal. Yeah, it's not something personally I spend much time thinking about. So if it's a real estate AI deal, you know, I meet the founder to try and understand what they're doing. And more than likely, I will tell them, listen, I just in some cases the other partners who may know it. So sometimes I can forward to them, but like, listen, I don't want to waste your time, I just don't know it. Um so yeah.

SPEAKER_02 32:14

Got it. And how many, let's say you mentioned you are meeting 10 to 12 companies a week, to you we would take the second meeting. How many of these companies, let's say, hypothetically we discussed 300 in there, you would take it to the rest of the partnership?

SPEAKER_01 32:29

Yeah, so one of the things that's Tom is we tend to take deals quickly to partnership. Um, so we we are very collaborative. Uh, and one of the things I tell people when we invest is um you're not just getting access to me, you're getting access to the pool team. So there have been times when founders reach out to the rest of the team and say, hey, I need help with. I mean, they uh they don't need to go through me to get to the rest of the team, right? So, but the other aspect of that is we tend to meet when anything I think is interesting, we talk about as a partnership, right? Because, you know, other people have looked at similar deals, right? Other people have invested in similar deals, other people may have folks in the network I don't know about or I have loose connections to that may be able to help. So I would say a high percentage of those, maybe 70, 80 percent, get at least talked about in the partner meeting. It may not be a long conversation, but it is this is why, because if I have a second meeting, I like something about it, right? This is why it's interesting. This would be the thesis, this is what we need to see to get to an investment. What do you guys think? Like pushback, you know, maybe somebody has invested in in one of these uh companies 10 years ago, and they think it's a terrible market.

SPEAKER_02 33:45

Anyway, that's who and so and how many, instead of this guest discussed in the part, but how many would you really make the entrepreneur discuss with the partnership?

SPEAKER_01 33:53

Full partnership? Oh gosh, I think in the year maybe like 10? More than 10, I would say. Maybe like 30 to 40.

SPEAKER_02 34:06

Okay, so out of 300 companies that you see, you would make 30 to 40 companies.

SPEAKER_01 34:09

Yeah, maybe maybe uh oh sorry, just mine. I was thinking what about the thousand. So maybe it's like 30 to 40 in the thousand.

SPEAKER_02 34:17

Okay, across the thousand.

SPEAKER_01 34:18

Yeah, yeah, yeah, yeah. But the one thing I would say is in a lot of cases we work in teams of two. So even if I think a company is interesting, typically one other person on the team also takes a look at it. Um so you would meet a second person from Storm, I think, reasonably soon. Uh, but the full partnership probably is something like that. So 30 to 40 out of a thousand.

SPEAKER_02 34:40

Cody, and and only six to eight get an approval at the Yeah, ballpark, right?

SPEAKER_01 34:45

Uh I again uh we tend to involve the partnership more aggressively. I don't know what's the industry average, but um because I think there's uh feedback that, well, at least I can benefit from, you know, especially especially T and Ryan, who have so much experience. So and generally we are all pretty good with that. Like if somebody wants us in a meeting, we're happy to do it. Um, I think especially today with how quickly things are going, it's great for us to also like learn and see what other things are.

unknown 35:16

Sure.

SPEAKER_01 35:17

So when the founders don't come from pedigree, what what their chances are for I mean it depends on what you define pedigree, but we are not looking for a certain template at all.

SPEAKER_02 35:28

Sure.

SPEAKER_01 35:29

Right. Um, you know, um, I mean, of the companies you mentioned, I mean, none of them I think came from actually, I hope this is true. I'm not not, you know, I I don't remember all their sort of backgrounds, but like um, it's not like they all came from Stanford or anything like that.

SPEAKER_02 35:47

Or or or second-time founders.

SPEAKER_01 35:49

Yeah. I mean, you know, if you think about, you know, Ritesh was a first-time founder. Um Deeptiat Mayala was a first-time founder, Core at Formi was a first-time founder. Ani at Dasera was not a first time, it was second or third, I think third time founder. Um, yeah, so so in a lot of these cases, these are not from pedigree or like have this incredibly well, well um documented success. Like J at Tekion. Clearly, incredible run at Tesla. So you can call that pedigree if you want, right? So, but a lot of these people are very smart, very you know, successful in their own ways, but it wasn't like they came from like what what a Jay did or like sold a company.

SPEAKER_02 36:33

Maybe Tesla in 2016 was not so popular.

SPEAKER_01 36:35

Yeah, yeah, it wasn't so popular, but you know, you could you could you could see why he would be the right person to build this for the automotive space, right? And um yeah, anyway, that's the way I understood.

SPEAKER_02 36:49

And uh you have companies like Atomic Opera, the founder started from India and then moved to the US. Right. You have companies like Spina, the company which is purely India based, but selling to the US market. Yes. And over a period of time, Sanjay has started spending more and more time here. Yeah. So how many of these companies you would you would do?

SPEAKER_01 37:07

Yeah, so I think in I mean, a significant chunk of our portfolio, I don't know the exact percentage again, is uh founders of Indian Origin, right? Many of them were born and raised in India. We have some that were born and raised here. Um and um I think in most cases, the at least one founder is here. Almost everyone has teams in India. Um so um we are totally fine if most of the team is there. I think in general, you have to be in the market you're selling in. So it is rare, I think, that a team can be fully based, let's say in India and then sell into the US. You can maybe for some kind of products, but in general, if you as you get to like mid-size and enterprise, like they want to meet you, you need to hire people here and so forth. So I think in most cases there is some presence here. I think that's not just true for India. We invest globally, even other countries, typically a founder moves here.

SPEAKER_02 38:07

God and have you uh seen a specific patterns that you try to learn from from the portfolio that became huge outcomes, let's say Tekeon, Wakato, and others, like there became like four or five billion outcome. Yeah, you couldn't have predicted in the beginning that it will become such big thing.

SPEAKER_01 38:27

I think I think having um I mean this is this will keep this will seem very generalized, but I think maybe there's a couple of things. I think one having a product vision on where the having a product and market vision. Like I believe this market will go like this, yeah. And for that, this product needs to exist. Yes, you have to prove everything out, yeah, right? It's not easy to say that PyCs. But I think a really strong um product vision is important to that because you need to be building towards something. Yes, it'll move multiple ways in the middle. So I think that's true with Jay. I think that's true with um Mercado as well. So I think that. And I think second is just sort of this little feel very like touchy-feely kind of thing, but like just that relentless focus on execution and you know, this may not be the right way of saying this, but I'll do whatever it takes to make this company succeed. Right? I think the companies that have done well not that is not, I think, sufficient for success. But I think, you know, if I have to pattern match across some of the successful companies, I mean, they would they would, for lack of a better way of saying it, not let the company fail, right? That's that sounds a bit cheesy because so many things that every entrepreneur wants that. Exactly. Every founder wants that. There's so many things outside their control. But I'm trying to pattern match on the successful ones. And I think part of it is just the founder's personality, makes a big difference.

SPEAKER_02 40:00

So you're saying relentlessness matters a lot.

SPEAKER_01 40:03

It does, yeah. Because every company goes through challenges, right? And and again, I I don't want to make it seem like that is the reason only for success because a lot of things are outside your company.

SPEAKER_02 40:12

But that's a necessary but not sufficient luck.

SPEAKER_01 40:13

Not sufficient thing, right? I mean, a lot of luck is involved. Clearly, a lot of luck is involved. You know, markets change, you know, regulation changes, you know. I mean, even with techyon, such a great company, let's say the regulators change something, right? It could have hurt the company significantly. Um and and I'll go back to what I said earlier, right? Like I think to build a big business, you really have to understand, you know, what the journey the customer is taking, right? Like some of the things I mentioned. Like, are they really solving a problem that they care about? And as you become big, you launch multiple products and you have to kind of redo that journey. Um, so those are some things I would say.

SPEAKER_02 40:52

Yeah. Today the entrepreneurs are start starting, there are a few challenges, you know, when when they go to rage. Obviously, the the top 0.1% or 0.1%, the VCs go after them. Yeah. Right. But I would say the the remaining part, let's say if there are 100 companies in the market, 10 definitely could be large outcomes. Uh one of them, obviously, every every VC is going after. But the rest nine. There are a few challenges for them. One is the question that they get is can anthropic come in your domain? And the second is there is no clear exit path. The exit path could be uh, let's say for your stakeholders, uh, the company at 100 mil era or 200 mil era right could go public uh in the US market, but that doesn't exist today. And the bar for public it's has gone up like immensely higher.

SPEAKER_01 41:45

Yeah, it's much higher. Um, yeah, I mean this is a good question. I think it depends a bit by sector as well. Um, you know, if you're starting out now, yes, you obviously have to think where is sort of the leading edge of the models and how quickly they're moving towards whatever sector you may pick, right? I do think over time, today a lot of the value has been accrued in the infrastructure and model layers. I do think over time we'll start seeing value getting accrued in the application layer. So I do think there's opportunities for big companies to be built. I also think that the models will not build everything, like we talked about, right? Certain things they will because that's core to them. So I think still there is opportunity for you as a founder to figure out the right vertical, the right product to build. Make sure you deliver what the customer needs in terms of, like we said, governance, security. Essentially, like if you go to them and say, listen, you want this outcome, right? Higher sales, better healthcare for your patients, you know, higher revenue, whatever it is. I will deliver that. What is it worth to you? Now, underneath, whether I'm using fully AI, whether I'm doing services, is not as relevant to the customer as long as you deliver it at a price point that they care about. Now, obviously, the pricing pressure comes from they'll say, Oh, I'll build it with cloud, right? But customers, for the most part, are not set up to build a lot of products that are not core to the business. Yes, the engineering team can say, Oh, this is easy, I'll build it, but are they going to maintain it for like 10 years? Right? Are they going to make sure when compliance and audit comes through that they're going to sit and figure out all through the logs? So the thing that I think I would encourage funders to think about is everybody has some level of core competency, right? Um a manufacturing company's core competency is obviously building products. They will not go and build everything. And I think there's a big gap, like I mentioned earlier, between the application and the model. So go build for that gap. Deliver the right outcome to the customer at the end of the day, and work with them on thinking through a price point. It makes sense. Now, depending on what value you're providing and what pain you're solving for, it may be that the price point is not enough to sustain a business. Or that price point may only support a certain go-to-market, like a product or go-to-market. So those are the things that as a founder you have to figure out, right? Your passion for the problem, understanding what the outcome the customer wants, building the right pricing and go-to-market for that. And I think there'll be many of those. Now, going back to exits, this also defines what kind of exit you can get. In some sectors, especially like cybersecurity, there's you know six, seven, eight really good buyers. And in general, what happens is one of them starts buying the network he buys. And I'm I'm overgeneralizing, like, this is not fully true. But and then if you don't get bought, the question is what happens to you, right? Like, can you go become a big company? Maybe it is, but it goes back to like I would I would encourage founders not to like think about the exit specifically as a as a reason not to do some one or the other. Because the things that you're building for, the reasons we discussed, right? The the pain, the outcome, and all that work for you in terms of starting the company, in terms of growing the business to getting an exit. Because if you're delivering true value, there's always someone going to buy you, right? Whether it's an older company trying to pivot. And I think at some scale, if the value you deliver is very good, I mean, you can go public. There will be this is kind of a weird time. I mean, comparing to Anthropic and SpaceX, it's hard to imagine anybody else going public today. But like if you're building now, you're thinking about public 10 years down, right? The market is going to be so different. We will see new companies coming out all the time. The AI is obviously very different, but like if you think about previous waves like the cloud wave and so forth, right? You know, the the big companies of today didn't start out until five, six, seven years after AWS came out, right? Now, yes, AI, you can argue, is accelerating the process. So, in many ways, we are just in innings two, maybe, of uh, you know, if you think of a baseball analogy, not a cricket analogy, but like a baseball analogy um of this journey. So if you build now, yeah, you think who knows what the market looks like in 2035. So so yeah, but yes, that's the way I would think about it.

SPEAKER_02 46:25

And uh what's been your learnings from companies that you were very bullish about invested in, but they didn't work out.

SPEAKER_01 46:34

Um I think let me talk about where I think we made mistakes, right? Because I think in in all the companies I can think of, and I won't name names, but like I I do think the founders did a really good job executing. I think some cases we we didn't identify the urgency of the pain as well, right? I think it was a important pain point, but it wasn't urgent enough, right? I think in some cases, you know, we could have done a better job at helping the company manage cash. Because there have been cases where I think these companies could have had big outcomes, but we burned too fast, and then you just never get that shot. Right? In some cases, you know, the markets turned a little bit in terms of um um the regulation made the pain point not as urgent anymore, right? So I I think of out of all of them, the thing that we could control um, if you want to say that, is I think really pushing the team just keep thinking about is this something that that that the customers desperately want? Because I think if you take your eye off the ball, when the market turns and budgets get tight, you'll get killed, right? And then it is very hard to do this. See, in hindsight, all of this seems like obvious stuff, but but being in some cases, we could have been but more prudent with cash. But you know, sometimes when things are going great and you want to accelerate, um, um, maybe these companies should have fundraised earlier. But those are things that in hindsight, um, we could go on. I I think the third is um um just tied to hiring, right? Um, I have seen this in some cases, and this this this depends again a lot on founder personality, is when you're growing fast, the instinct is to lower the bar for hiring because you want to get people, you're like, I need people, I need people, I need people. That can obviously work out well if the company does great, and which is why I'm saying some of this is hindsight, right? But but that increases the risk profile of the company significantly.

SPEAKER_02 48:55

Modern learning has been we have had 65 companies in neon across the last seven years.

SPEAKER_00 49:00

Yeah.

SPEAKER_02 49:01

Uh the the reason we have six to seven uh companies that did work out or down. The common reason among all of them has been lack of product market fit.

SPEAKER_01 49:11

Product market fit, yeah. But again, like then the question, first of all, you invest very early. You invest much earlier than we do. Um, but then the question is why wasn't there product market fit, right? Like what what was this, what was the cause where the ultimate sim symptom or outcome was no product market fit.

SPEAKER_02 49:26

Yeah, I I think uh it can be back to uh the same what you mentioned. Yeah. Uh the the pain might not have been deep enough, or there was a misunderstanding on on part of the team on understanding the pain. Like there was some other pain, but because the customer never like like opens up 100%, even the customer doesn't know that that this is the exact pain, which is a burning problem, and the team just starts building for something else. That's right. And both both are never on the same side.

SPEAKER_01 49:53

Yeah, and then you're not you don't have enough money to and you run out of money, right? Yeah, exactly. That that happens a lot. Um yeah.

SPEAKER_02 50:01

Yeah, like because I've seen like companies and usually even let's say reach a million error, but get stuck, there's no new round happening, and there's a downward.

SPEAKER_01 50:12

Yeah, I think the millionaire is important because you know I mentioned this very early in the conversation, right? Is I I think that transition from when founders are selling, because the founders can often get uh traction that's not repeatable, right? Because you know, the founders obviously they have the passion, they know the space, they know the product, they can say we'll build this for you. And and when you when you when you're selling at that stage, typically you also tend to get early adopter customers. There are customers who are like, I like working with startups, I believe a product like this should exist. But that is not the vast majority. The rest of the market is much more clinical in how they decide to evaluate or buy a piece of software.

SPEAKER_03 50:52

Yeah.

SPEAKER_01 50:53

So so I think that transition from whatever it is, 500k million, you know, five, 10, 15 customers to your next 50, where you're going from Ponderlet sales to a go-to-market team, is a big sort of you know, gap, right? Which is that's that's part of the area we spend a lot of time, our time on, is we try to identify can this gap be bridged in some way, or what needs to happen for the gap to get bridged. Um so yeah, it's it's a it's a very tricky thing. But what you said, we, you know, that's the conventional wisdom, right? Is you get to a millionaire, you raise a CDs A, whatever the amount is, you hire a sales leader, you hire to spend money on marketing, and growth will come. But you know, it doesn't because you have to do a lot of work to get there.

SPEAKER_02 51:35

Yeah, yeah. Because you have seen companies even at like a million era, uh, even if not founder led sales, yeah, because there was not enough product market fit at that stage, yeah, the sales become much harder and exactly.

SPEAKER_01 51:50

Yeah, yeah. I mean, it's not an urgent pain. You don't say the right things in the sales process, you're not delivering value quickly enough, like I mentioned. So all of this puts more pressure on sales to convince someone.

SPEAKER_03 52:01

Yeah.

SPEAKER_01 52:02

And the best way to convince them someone is if you're if you deploy the product and it just works, right? Or they are so desperate for it, they're like, okay, I get it, but I want to try the product now because I need something. So, in many ways, uh, you know, I think as you said, product market fit, especially at your the stage you invest in, is a key sort of breaking point. But if as we think back and, you know, do sort of analysis in hindsight, we try to think through what were like the signals we could have caught earlier on, or what questions we should have asked, or how much more time. Now, this gets tricky these days because the rounds are moving fast, as you said. So you don't have a lot of time to do this, but those are some common reasons.

SPEAKER_02 52:45

So for us, the thing that we have uh uh you know learned is uh every uh you know every new dollar uh that comes in, if there is product market for it, it becomes much easier.

SPEAKER_00 52:57

Yes, absolutely. Yeah, yeah, yeah. The return on that is much higher.

SPEAKER_02 53:01

Yeah, every new dollar becomes and and some of the signals maybe to evaluate that as the cash keeps on going down.

SPEAKER_00 53:08

Yeah, your payback period goes down. Yeah, you're right, absolutely.

SPEAKER_02 53:11

Yeah, and uh eventually uh there has to at even at your SNB or at enterprise, the word of mouth has to kick in. If word of mouth is not kicking in, it's only a sales led, then probably there's something wrong. And yeah, yeah. And eventually it'll break out.

SPEAKER_01 53:27

It'll break out, yeah. Yeah, because you you can't scale that indefinitely. Yeah. Yeah.

SPEAKER_02 53:31

And so sales is is is becoming more harder and harder in AI waves.

SPEAKER_01 53:36

Yeah. Never mind. But there's also a lot of noise in the market.

SPEAKER_02 53:38

A lot of noise in the market. Yeah. How is software buying change? Because you speak to a lot of the CIOs and buyers.

SPEAKER_01 53:45

Yeah. I think I think just the amount of research that people do in advance, and in some ways you can argue that people have already made a decision, right?

SPEAKER_02 53:56

I'm not able to understand.

SPEAKER_01 53:57

So if I'm a buyer, I'm making this up, but like if I'm a buyer and I'm looking for let's say a uh AI security product, right? I think now AI security is very crowded, so maybe this is harder, but they have the tools today to do the research, look at 50 companies, look at certain criteria, narrow that down to two or three, and say, I'm gonna pick between these two or three. It's the right product profile, it's the right set of features, it's the right price point, it's its product is simple enough that I can test it very quickly in like two, three weeks, um, and I can make a decision. So then if you have 50 companies and 50 salespeople reach out to you, you may pilot more. Like I've you know, talked to CIOs where they're like, and I'm piloting 20 products, but I don't think I'll buy any of them, right? Because they have enough, done enough research in advance that they know exactly what they need and they're looking for that signal. So that's I think the other challenge, both for the you know, the buyers and the startups, is there's a lot of noise in the market. And this is changing now, but like the the pace at which real transactions, for lack of a better way of saying it, are is is very low. So so I think, and this has been happening for a while too, but I think the buyers in B2B software today are much more prepared and aware of what they want and aware of what the options are. So, which is why why the sales process becomes even more critical, right? Like you can't get them for like four meetings before they make a decision. You need to deliver value or highlight the pain in the first meeting.

SPEAKER_02 55:38

Yeah. So there is no time for discovery then.

SPEAKER_01 55:40

There's not much time for discovery because you know they can go online, you know, create an agent, do a search across. Yes, there's always a chance that a vendor that they couldn't surface comes in. But in general, you're probably going to hit 70, 80, 90% of the solutions. And then you can set your benchmarks, what you're looking for, you know, you can prepare for that. Um, and then, you know, they are almost at like, okay, I have four vendors, I'm going to pick from one of these. Let me try those. It's I'm not going to sit through 50 vendors educating me on what the problem is and what their product does.

SPEAKER_02 56:14

And what about the traditional wisdom of building a champion, then building a team, uh, like knowing a 10-member executive team inside the enterprise. Yeah. How much of that is valid today?

SPEAKER_01 56:26

I think I think some of that is still valid, especially if you're selling to large enterprises, right? I think you still need a champion who is going to um champion your product. Uh, because there is risk, right? Especially if you bring in AI tools, they need to perform at a certain level of accuracy. They can't, and the more complex the use case, the more mission critical the use case, as in healthcare, the bar gets higher and higher. So somebody at some level is sticking out their neck by bringing you on. I'm not saying pilots, right? I'm saying actual production deployments. Um, I think a champion still needs that. You still need to convince you know, other parts, maybe IT, maybe procurement, maybe finance, especially finance now, right? Where these AI products that come in, they're coming in at a pace that wasn't true before. They may consume, depending on your pricing model, especially with usage-based model, right? Like suddenly org starts using it or doesn't use it. So it's not like we're shifting away from the standard uh subscription model more to a usage-based model. And ultimately, a lot of people are talking about putting FTEs and um and are doing this, putting FTEs and delivering outcomes. So, but but you still have to convince the broader org. Now, the pace of that I think has accelerated. The other benefit with some of these AI products is that you can deploy and show some value very quickly, right? The days of I need six months to integrate and onboard makes it. I mean, I think those days are harder to like at least those kind of products are harder to sell because six months onboarding is a very long time, right? So um it's evolved a lot, but I think stuff you mentioned is still very relevant. It's on an accelerated cycle.

SPEAKER_02 58:10

Yeah. And what what do you think about new categories like voice AI? Have you invested in any of the things?

SPEAKER_01 58:16

Yeah, we we we we have a couple of companies, like for example, we're investors in a company called Crisp, K R I S B.

SPEAKER_02 58:23

Um That's quite a scaled company, right?

SPEAKER_01 58:25

Yeah, yeah, yeah. They're at decent scale now. Uh they've done a lot. Um you know, started on noise cancellation. Today they do, you know, accent localization and a number of other products. Basically, if you use voice in your platform, right, either internally or as a product, like the Crisp API can provide you a lot of value.

SPEAKER_02 58:44

And they also build agents?

SPEAKER_01 58:46

They they in they are starting to, but today they're more um uh they they provide a full full solution, but mostly you build on top of them for certain use cases. Um so you know, we've we've done that, you know, Synthpop, which is a healthcare AI company that I invested in last um uh last fall, um they they work in something called the durable medical equipment space. So the uh and they're expanding beyond that, but that is the space they started in, where um there's a very complex process from when if if your physician orders a medical device, and I'm using device broadly, but like uh you know a wheelchair or a sweep apnea machine or glucose monitor, what happens from the physician ordering to whose services that order, like a vendor? Uh, how do you verify insurance? How do you get it to a patient? How do you handle delivery? How do you handle feedback, replacements? It's a very, and because the reimbursement on this is very complex, there's like this massive flowchart of what a human needs to do, right? So they are using uh agents for that. And a big part of that is calling insurance and patients, and you know, some of these calls can last 40, 40, one hour. So it's not like an agent that asks a question, hangs up. So that's a good example. So voice AI, I think, is a great medium going forward for a lot of this, especially where you have to interact with humans.

SPEAKER_02 1:00:04

And uh what's the the lowest ARR or like one is lowest and the other is average ARR that you usually come in?

SPEAKER_01 1:00:13

So we have done companies at incubation in founders we know. It is where I'm trying to think of an example where we've done it in people we don't know. I think it's unlikely. Um so beyond that, I would say the average, gosh, the average is a tough number. But I think we've done everything from companies that are at a few hundred thousand to a few million uh to start off with. Uh again, I do I don't want to say like you have to hit a million ARR. The number also matters on depends on the team, you know, how strong our thesis is in that market, how competitive it is in that market. Like, I'm just taking a simple example, right? Like if you are building a new sales automation tool using AI, there's a lot of companies doing sales automation. So there it's unlikely if you're at 100K, we would come in, or let's 100k is tool less, but let's say at 500k, because there's probably 100 companies doing that, right? So we need to show something, we need to see something that you're building that approaches the market in a different way or solves it for a different buyer or a persona before we would invest. So there the bar is a bit higher in terms of traction. But if you're building, you know, this is a company called um TOT, which I mentioned to you before, I call building something for convenience retail. That is a market that every deal is like a seven-figure deal, and you have to build a lot before you um get to revenue. And there's really not much competition. So there we may be willing to take an earlier bet. At zero revenue. In that case, we did zero revenue. Again, Sham is a second-time founder, so it it breaks the mold a little bit in terms of maybe the question you're asking, but but then we can understand why the traction is lower and we can take a bet. But I think for your listeners, gosh, like half a million to like two million is like let's say the range. But like everything there's exceptions to that.

SPEAKER_02 1:02:18

Yeah. And do the most of the companies that you see are in that range?

SPEAKER_01 1:02:24

I uh I mean it it fluctuates. We definitely see earlier companies. Um, and by the way, like in in some cases, the investments we've done, we met them when they were very early, but we kept in touch and we did the next round, right? So that works out. So I don't want to make it seem like we don't meet companies early. In fact, I mean many companies that are earlier than us. And there are many times I've sent them on to other funds that do pre-seed and seed, and in some cases they have invested too. Um but and if you like the team and the idea, you know, at least I try to stay in touch, and then we can potentially lead the next round. So um, but yeah.

SPEAKER_02 1:03:02

Got and I assume uh out of the 200 companies uh right that you were done, uh today you said just 60 to 70 live, right? So it would have Ama, like the remaining would have been like around 40-50 shutdowns and 40-50 exits.

SPEAKER_01 1:03:19

Probably if I think about every exit, and I'm not saying the exit was great in every scenario, we've had some very good exits, but yes, I would say a bunch failed, maybe like as you said, 50-60. You know, I think we've seen a lot of in in our history, like the capital returned or like a 2x. We've seen a lot of those. Again, I don't have the exact numbers, um, but I'm sure there've been a bunch of those and obviously some bigger returns.

SPEAKER_03 1:03:44

Yeah.

SPEAKER_02 1:03:45

So for how many, you know, I know it's it's not a right measure, but how many unicorns would Storm have in the history of Storm?

SPEAKER_01 1:03:51

So I think we've had 11.

SPEAKER_02 1:03:53

If I remember. Which one would be? Some we discussed, like Tekion, Warcad.

SPEAKER_01 1:03:56

Yeah, I mean Talk is Tekion, Workado, um, Splashtop, uh, Marketo, Mobile Iron, uh from the earlier days. Um trying to think who else. Yeah, those are like six or seven. There's another four or five. Yeah.

SPEAKER_02 1:04:14

And which are the most recent ones?

SPEAKER_01 1:04:16

Well, Tekion is probably the most recent one because they they raised two years ago, three years ago. I think Tekion is like the most recent one. I'm trying to yeah, I think so.

SPEAKER_02 1:04:29

Got and when when companies accelerate, let's say from one to ten, you obviously mentioned the transition from founders-led sales to like a professional sales team handling the sales. What are the biggest accelerants that you have seen from companies which go from 10 to 100?

SPEAKER_01 1:04:46

Yeah, so I think there's a couple of areas. One is, as we mentioned, as we discussed, the the go-to market becomes very repeatable, right? You are able to, at some level, predictably say, if I invest 20 million in this business, I'm gonna get X out. I think second, in most cases, we have seen companies launch a second product, right? And that product has its own journey.

SPEAKER_03 1:05:06

Yeah.

SPEAKER_01 1:05:06

But your initial product is growing, you launch a second set of second or maybe even third set of products beyond that. Third is, and you know, some of this is cause and effect, it's hard to argue, but like you bring on a more seasoned team, right? The team that got you from one to 10. And in some cases, we've had leaders scale across it, but you get a team that is more experienced with sort of building out and scaling. Now, AI is changing a lot of these, but um but but those are those are some things. I think the stronger, you know, we call this unlocking growth that founder led sales to a um uh a go to market team. Like, so before you scale your growth, you have to unlock growth and we've built a lot of frameworks around how do you test for it, how do you go about finding it, how do you measure it. Um but I think the stronger that is. The easier it becomes. And then once you get to later stage, you know, you are you have a lot of, you start because a lot of it becomes number driven at that point, you start having signals on whether it's working, not working, what are some early signs of it, things like that.

SPEAKER_02 1:06:13

And do you see in the current AI wave product market fit also changing dynamically for companies?

SPEAKER_01 1:06:20

Yeah, I mean it it is changing in the sense that the pace at which you can get a buyer and potentially even churn is much higher, right? Because the AI products, one of the benefits of that is it is easier to build, but also I think it's easier to put it in and replace it, right? In some cases it's harder, like especially in like certain industries or like certain deep products, like with compliance and all. Maybe that's not a good example. But but you know, 10, 15 years ago would be much harder to like get a product in, but then ripping it out would be much harder, right? So now, especially if the customers have done a good job, you know, managing their data, um you know, building models on it and so forth, and depending on how the the rights there are assigned, you can get people in and out very, very quickly. So that increases risk a lot. So, yes, we can get product market very quickly, but the next product comes along and they can deploy quickly as well, which is why when we talked about earlier, I think companies really need to think about how you are building that full stack between the uh model layer and the UI. Like you need to own that entire thing. Because if you own the entire thing, at least in some verticals, it is a lot of work to repeat out. And second, if you build that well, as the models get better, your product just gets better. So if somebody comes in next and they're using, they're also using the state-of-the-art model, it is hard for them to offer a much better product than yours unless you've just designed yours badly, right? Or you don't understand the pain. So so I think you compete there going forward.

SPEAKER_02 1:08:05

So today, if you have to think about four to five themes that you are most excited about or thesis that you are building internally, what would they be?

SPEAKER_01 1:08:14

Yeah, so one is I really like verticals like healthcare and construction, and you know, Toad does convenience retail. We invest in a company called Flywheel that is focused on cloud marketplaces. So I don't want to say these are like an off-center, maybe the wrong word, but they're not like straight down the middle, right? So that is one area we're spending a lot of time in. Um, I think second, we're spending a good amount of time in physical AI. I think um, you know, how AI and it can accelerate sort of hardware development and how it can do tasks that you know previously would have been very difficult to train for and build for machines and robots.

SPEAKER_02 1:08:51

What do you mean how AI can accelerate hardware development?

SPEAKER_01 1:08:54

Well, like we're we've been talking about like how robots can do work in homes or like you know, um there are companies that that automate uh obviously self-driving is a great example, but like in industrial use cases like for security or in mining and so forth, right? Where machines can now be much smarter in how they behave. Um, I think we'll see more and more assembly lines, right? Like how can machines be better at picking. Um here's a simple example. Uh in many cases, if you have an assembly line, what would happen is if the assembly line was making X, you want to change that to making Y. There was a big refit that needed to happen, right? Or you know, you have to retrain people and so forth. You know, a big push in physical AI, and obviously there's risks of job losses and all here, is that you're building machines that can do multiple things at once. Let's say a human or robot, right? Um, where they can just adapt to whatever is coming, right? So that's a that's an example of physical AI. Um, so I think there'll be a whole industry that's built out where given the data sets that are available, a lot of it it is visual data and the right world models that are built around it. You can train these machines, and I'm using machines very broadly or robots much, much faster than what was before possible before.

SPEAKER_02 1:10:21

They're saying training data for for this physics machines. Yeah.

SPEAKER_01 1:10:24

Yeah. And and you know, with self-driving cars, it took whatever, 10 years, 15 years to get the data. Uh and you know, obviously, uh a car driving is a very high risk.

SPEAKER_02 1:10:36

Yeah, you know, you can't cannot afford one accident. Exactly, right?

SPEAKER_01 1:10:40

So, but you know, like picking fruits is like a lower bar for that. Um yeah, so so I think I think I think that is an area we're spending time.

SPEAKER_02 1:10:52

Are you also thinking of uh let's say more hardware developed getting developed? So AI for uh AutoCAD kind of things, that of also venture.

SPEAKER_01 1:11:02

Yeah, I mean I think listen, any software that uh follows roughly a set of rules and is algorithmic, I think AI has a great use use case for that, right? So if you're doing design or CAD where you know there's a set of best principles, I think AI does that well. Anywhere that requires a lot of human judgment or you know design thinking or creativity, uh I think AI will get there reasonably well. Or or maybe the it can do a good job if you have no creativity or skills, but then that's where it becomes much harder, right? So if you are in an assembly line, I'm just using that as a very general example, and you are it's it's a very repetitive task. That's where the AI can get good at because it's repetitive. You can train it tens of millions of times very quickly.

SPEAKER_02 1:11:56

And what are the other things?

SPEAKER_01 1:11:58

Um, I mean, I think we're continuing to look at um, you know, areas like cybersecurity, um, right, where there is um a lot of budget. Um, you know, and I think in a lot of uh one of the interesting trends across all of these is just the business models are evolving. We're going from, as I mentioned, from pure software to um a more um uh usage-based or outcomes-based. What you offer underneath in terms of is it pure software or is it is it software and service? Yeah, or companies are shifting there as well. So I don't know if you call those themes, but those are the areas we're looking at.

SPEAKER_02 1:12:38

Understood. And any particular, let's say in vertical, you give two great examples like TOT. I've never heard of such a company building for general uh stores at gas stations. Yeah. It's a huge market, yeah, completely underserved, uh, right. And the solutions must be really old.

SPEAKER_01 1:12:56

Yes. Yeah, I mean, tot is a good example. I can describe it more, but yeah, the use case um is this market called convenience retail, which um um which is, as you said, you know, think about gas stations or or 7-Elevens, like the sort of the smaller footprint stores. And the the challenge, as you rightly pointed out, is I think twofold. One is they use a lot of legacy infrastructure. But two, it is a very complex um setting because you have people in stores, you have checkout, you have self-checkout, you have to connect gas to it. Um uh, you know, a lot of these are in places uh, you know, with potentially some cases you don't have great connectivity. So so building out a modern platform to manage all of that. And then uh on once you're deployed in these stores, you can build a lot of apps on top, right? Because you can build um an inventory app, you can build a finance app to track how these stores are doing, forecasting. You can do so many things. But the underlying infrastructure to get these stores to adopt you is very hard. Um, so in many ways, as I mentioned earlier, I think of it like what Tekon is doing for automotive, these guys are doing for convenience retail.

SPEAKER_02 1:14:10

Yeah, but this is a perfect kind of vertical that you would like to attack.

SPEAKER_01 1:14:14

Yes, yes. So this is this is great. I mean, it's obviously not very simple because um it takes time to understand the vertical. And in some cases, you do need you you do need a very experienced father, which is why I mentioned domain expertise, right? Like this would be hard to do for someone who was doing something completely different and said, okay, I'm gonna build in the space, right? Not that it's impossible, but the bar becomes higher because the sales cycles are painful. You really need to understand how these people buy, what are the signals, and the lift is heavy, right? Because you can't build a small module and say deploy because you have to run the entire store. So the the zero to one is a big step. It's not like you can get 5k, 10k, or R and scale from that.

SPEAKER_02 1:15:00

Yeah, you have to, and all these smaller convenience stores are not owned by one Bebu Sulan.

SPEAKER_01 1:15:06

I mean, there are there are chains, but you're right, there's many chains. And you have to go convince someone to say, by the way, this store that does half a million a year or a million dollars a year, or maybe even you know five, ten million a year, it's gonna run on my software. Yeah. Right? And and if it's if it's you know, if it's doing a decent amount of business, actually half a million a year is probably too small a number, like you know, it's probably millions a year. And you have an outage for like half a day or a day, I mean, that's potentially tens of thousands or hundreds of thousands, that may tens of thousands, hundreds of thousands of revenue. So it is very high impact. That is hard to test um, you know, by yourself. So, you know, you you mentioned earlier, I think I can't remember if maybe it was before we we started, how you know Tekeon had to build their own dealership too.

SPEAKER_02 1:15:57

Jay mentioned to us that the the first deployment was the own dealership that they bought.

SPEAKER_01 1:16:02

Yeah, exactly, right? That's how you test. I remember in the tote office uh in the Bay Area, they have fuel pumps that they connected to and tested. So, like actual fuel to go get a fuel pump.

SPEAKER_02 1:16:13

Okay, so did they build a fuel pump in their office?

SPEAKER_01 1:16:15

Yeah, I mean there's no fuel passing through it. But there's a billing. But the yeah, the billing, you know, what happens when you press this button, what uh signal do you get on the wire? All of that they had to like build.

SPEAKER_02 1:16:26

Very complex.

SPEAKER_01 1:16:26

It's very, very complex. Yeah, yeah.

SPEAKER_02 1:16:30

Oh, amazing. You know, thank you so much, Aaron. No, thank you.

SPEAKER_01 1:16:32

Those great questions, and uh yeah, congrats on your success too. You know, uh you guys are doing really well.

SPEAKER_02 1:16:37

Uh thank you so much, and would love to to work together more.

SPEAKER_01 1:16:40

Of course, yeah, me too.