In this episode of the Zero Prime podcast, engineer-turned-founder Rob Woollen gives a refreshingly honest take on the founder journey. Rob shares his path from Unix coder to BEA Systems architect to Salesforce CTO, and finally to pioneering data analytics at Sigma. Get Rob's reflections on turning deep tech into market wins and navigating the AI revolution from an engineer who's built it all.

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Key Chapters Timeline

1. 00:00 - Introduction and Early Career
2. 04:25 - Salesforce Years and Career Evolution
3. 06:51 - Path to Entrepreneurship
4. 10:41 - Sutter Hill Ventures and Sigma's Genesis
5. 14:38 - Building Sigma
6. 20:30 - AI and the Future of Business Intelligence
7. 28:55 - Closing Thoughts

Podcast Transcript

Pete (00:02):
I'm Pete Sutterling and welcome to the Zero Prime podcast, where we explore the early stories of top startups via the experiences of their engineer founders. This week I chat with Rob Wolin, the co-founder and CTO of Sigma Computing. Rob is a longtime engineer who turned founder when he decided to attack the business intelligence space and started Sigma after a stint as an entrepreneur in residence at the storied firm Sutter Hill Ventures. Rob has a wealth of wisdom to bring to the podcast and you'll hear his honest take on what it really takes to start a company as an engineer.

Rob, welcome to the show. I was looking at your background and there were so many cool things that you've done from school and education at Princeton to working at BA systems for many years - what a blast from the past in the first internet bubble. Onto Salesforce for many years in many different roles of engineering management and EIR at Sutter Hill Ventures, and finally the co-founder of Sigma.

Rob (00:33):
Thank you.

Pete (00:58):
I think you and I are going to have a lot to talk about, really fascinating exploring your experience as an engineer founder yourself. So let's dive in. I wanted to ask you initially, tell me about your early days. You were actually at HP even before you were at BEA. What drew you to enterprise software in the first place? And what lessons from those early days still influence you, would you say?

Rob (01:04):
Like many people coming out of school, I really didn't know anything about enterprise software and I don't think I particularly had some sort of passion for it. My early career choices were based on technology. I joined HP because I loved systems programming in college and the chance to work on their Unix kernel, HP UX, was what I did there. But then looking at the years, that whole internet bubble was happening. And I felt like I was twiddling bits in the kernel and everyone else was working at these exciting new dot-com companies.

I'd also moved to San Francisco. And I saw this opportunity to join this WebLogic team. They were in San Francisco, working with Java, which was super cool technology in the 90s. And when I interviewed, they talked about how they had this automated testing suite. At the time, that blew my mind. I'd never seen it before. And it became clear to me that I was going to work with people that were going to teach me a bunch of things, work with exciting technology.

The super interesting thing for me is that every job in my career after that, including Sigma and the founding, the investors I have, is directly connected to that team at BEA and WebLogic. The big takeaway for me was, you can't always pick the company that's going to grow dramatically. That's like picking stocks. But I think you can spot really smart people, people that are gonna teach you things. And that team was just incredible. Since then, I've basically always been directly working with at least someone from that team. And as you'd imagine, when you work with top people, they find other top people and you grow your collective.

Pete (02:58):
That's amazing. And it really can't be overstated, in my opinion. SF tends to be the mecca for enterprise software, and really has been ever since those days, and perhaps even before till now, in spite of various ups and downs of the city and the ebbs and flows during COVID and all kinds of challenges. I think you commented to me before we jumped on that you've literally worked in offices like in the same blocks sort of walking distance of each other for many years. And I want to emphasize that because I preach this to founders, B2B founders all the time. 

We invest in companies in Europe and in New York and all over the world and sort of believe in the equilibrium of software overall. But SF has a special place in its heart, especially for infrastructure companies that start in San Francisco. And I think founders need to understand that and acknowledge that even if they are trying to start companies outside the bay, they really need to spend a good amount of time here and probably build a team here and maybe live here, at least for a period. And there's just so many things that are hard to explain unless you've actually lived it. And your testimony of how this diaspora of folks coming out of BEA has been instrumental to your career and you continue to work with those people even today, I think is testament to that fact.

Rob (04:17):
It's a shockingly small world out there, and especially as you're working in such a concentrated area, you can literally walk out the street and see a bunch of other amazing companies.

Pete (04:25):
And you learn so much from each other, and that camaraderie is really real and palpable. Tell us about your time at Salesforce, because you worked many different roles at Salesforce, I know, and it looks like mostly in engineering management. But was there a progression in your career? How did you get into the engineering management side of things? And was it difficult for you as an engineer to want to be a manager? And were there ups and downs with that process?

Rob (04:50):
From a career side, I joined there as a principal architect role, a senior technical architect. I took on roles there as being a CTO. I was the CTO of the platform group. I think the biggest leap I took was I moved from CTO role to running product management for the platform group. And at the time, it was a big risk, I think, both for myself and for the company. I'd never been a product manager before. I'd never managed people before. And I went from that to not only leading a group, but also a new discipline.

From a personal perspective, it was very challenging. Like many people in a new role, I made a lot of mistakes. On the other hand, I think it was one of the key things that helped me in the later years with Sigma because it took me out of the purely engineering mindset. And I became much more focused on building relationships with the sales team, interacting with customers, even dealing with crises.

Anyone who has a product knows there's going to be times where there's bumps with customers, times there are situations you have to smooth over, you have to figure out how to address those situations. And being in the front line of that was super valuable for me from an experience perspective. I also frankly learned a bunch about myself. I did it for I think almost two years. And then I had the realization that I didn't love being only a product manager. And I went back to being a CTO in a different group at Salesforce. That's part of a career journey. Over time, you learn things that you maybe can do but don't love. And I like getting up every morning and love coming to work. And that's something I've been blessed with throughout my career. And so I try to just focus on what's gonna make me happy and that's guided a lot of my career choices.

Pete (06:27):
Moving through your career fairly rapid fire, I kind of want to understand when the mentality in you started to shift or emerge that you thought you might want to be a founder. Because moving from all of these deeply technical or even product roles into an EIR role at Sutter Hill, which was your next stop, obviously there was some kind of awareness or sea change there. And so I'm wondering what the story is behind that switch specifically.

Rob (06:51):
I always had this desire to work at a startup and I had the funny experience of I actually interviewed with WebLogic when they were an independent company and during the hiring process they got acquired. I loved the team and the product so much that I went through and joined the acquiring company, BEA, which again grew dramatically and so it was a great choice but it was a different environment than the small startup that I planned to join. 

When I went to Salesforce, I had spent time interviewing with mostly startups and looked at a bunch of different opportunities and I fell in love with the cloud. When Salesforce and I talked about managing only one release and having continuous deployment, it was game changing for me. I was coming from a company where 40 to 50% of my team was maintaining older versions. This idea that we could have one version - this is a game changer.

But I think I had this long-standing desire in the back of my mind to start a company. It's funny because people ask me now for advice on starting a company. And I feel hypocritical about it. But my standard advice is: Do not start a company. And I give them all the reasons - it's super hard, your chance for success is very low, you can be the smartest person in the world. I know incredibly smart people who built incredible products and they went nowhere. And the reason I give them this speech is because it's incredibly hard. And if I can convince you in 10 minutes not to start a company, you should just not start a company, because resilience is one of the key things. And I was one of those people - you could have given me this whole speech, it wouldn't have mattered. I still wanted to start a company. It's not logical, but it's something that was just deep inside me that I had to do.

Pete (08:22):
It makes sense to me because my entrepreneur journey has been sort of once I got the bug and I fought through the hardship and made a tiny amount of money and then wanted to put that money into the next product and then bootstrapped the next thing and then failed and then still couldn't stop and sort of the third thing. You realize that this pattern of people throw around this term serial founder. Well, it's really, for me at least, it's sort of become the lifestyle.

And the only reason it's been worth it for me in the end is because it was a lifestyle that I embraced. And I knew if it wasn't going to be the next startup, well, it was going to be the next one or the next one. And I often tell people that Zero Prime is my fifth startup. It just happens to be a venture fund. And I think maybe not everyone has to be in it for one after the other, after the other, after the other, without a corporate job break and without a proper paycheck. And I think that's a valid path as well. 

But I think there is a thing where at some point, to be really good at this, it takes practice and work and experience, and it might take several companies. And I guess my flavor of that advice in the spirit of what you give to many folks is, do you want to start a startup? Go work at a startup first. Because many folks have never even worked at a proper startup, a proper small company. And at least start to build this experience and muscle memory and familiarity with what the fire drills are actually going to look like and what the challenges are actually going to be because it can be an extremely grueling process and it can change your life, can change your family, it can change your relationships, it can change your marriage. There's all these sort of side effects and you have to really sort of be committed and convinced that I think of it as a journey and not just a point in time where, I'm going to start a startup.

Rob (10:00):
It resonates with me. When I started Sigma, I had three small children. My youngest was five months old. There's no logical reason this was a great time in my life to go start a company. But it was like a burning fire within me. I had to do it. I 100% agree with you. I think people that have the opportunity to work at a startup, they can then see so much of it. And there's no right decision. It's a right decision for you. What is the right match for you? And I think too many people try to almost sort of glamorize starting a company, whereas I think it's really just about for you, what is the right decision?

Pete (10:31):
Tell me about the experience at Sutter Hill. Was that the incubation period for Sigma?

Rob (10:41):
It's kind of an interesting path. I was leaving Salesforce. I knew I wanted to start a company. I felt like I needed advice. I needed someone to guide me through a little bit of this. How do you leap from working at a public company with thousands of employees to starting a company the next day? And I feel like entrepreneurship is such an interesting space. If you think about it, like a lot of the people that are just the best at this in the world, they may have only started one company. I would love to have Jeff Bezos and Bill Gates advise me, but what if my company is very different than Amazon or Microsoft? What if the patterns that they learned have nothing to do with my company?

What I found fascinating about the Sutter Hill team is they not only have a bunch of huge companies they've built, they were early in Nvidia, they were big early people that started Pure Storage. Snowflake had just been the previous entrepreneurs in residence before I joined there. And so to me, it felt like this unique opportunity to work with someone in this case, partner Mike Spicer, who I think is arguably one of the best VCs in the world. But their specialty is not only that they've pattern matched and built a bunch of these big companies, it's that they were started from day zero.

And it just felt like such a unique opportunity for me that I took the leap and joined there. And then that's actually where I met my co-founder. He was the other entrepreneur in residence at the time. There was only two of us. And I think it gave me sort of the white space of I disconnected from Salesforce. I hadn't taken on another job yet. You sit at some level in this kind of empty office and think about like, what should I do next and explore ideas? And one of the inspirations we had then there really sort of became the genesis of Sigma. And while we took many different approaches to solving the problem, we've actually been focused on the same sort of initial problem since 2014.

Pete (12:27):
And what was the key insight that made you jump into the analytics space at the time?

Rob (12:33):
When I looked at my own career, it felt like if I look back at even at BEA, the way we deployed software, if I wanted to deploy 10 more machines, that would have been like a month long endeavor of finding someone and space in the data center. We got to rack machines, wire them, a purchasing department has to get involved. And if I did not want those 10 machines after an hour, that would be a big problem. 

With the advent of cloud, elastic resources, the way we can spin up things, spin down them - even also just the idea of open source, the fact that so many things I didn't write the code myself, I built on someone else's work. All of these types of ideas, it felt like it had fundamentally changed how I as a developer had worked in the last 20 something years. 

When I was at Salesforce and I looked at even how the preeminent cloud company ran, the business functions largely still ran on Excel spreadsheets. We had really some guesses or almost like tribal knowledge about what we should do on different things. They made huge decisions based on essentially guesses and speculation without being able to actually leverage any of the data behind it. 

The realization we had was that infrastructure had advanced pretty dramatically to the point where so many of these decisions that are made in business should be able to leverage all of this massive amount of data that people have. But the technology barrier, having to know SQL or Python or something like that to actually crunch the numbers, was keeping it so that the mass majority of people weren't actually benefiting from it. So the fundamental idea was to say, can we build something where the interface allows essentially the masses, all the people that frankly are making the most important decisions in companies, to actually leverage all of this sort of infrastructure transformation that's happened in the last 20 years. And we left the VC firm with little more than that idea. It was quite a journey from there to figuring out how do you actually solve that problem.

Pete (14:25):
And what were the next steps? I think this manifested some UX opinions and I'm sure a lot of product vision. What were some of the key moments along the formation of this idea into product form?

Rob (14:38):
I think back to sort of like, how do you even think about starting a company? I think you have to have almost an irrational confidence. In our minds, it was like, well, I'm sure within six months, we'll have built the great interface that will solve this problem that even though people have been thinking about it for probably 20 years, we're going to solve it very quickly. And as you can imagine, instead, it took almost three and a half years of building interfaces, trying them with people, trying them ourselves, throwing them away, starting over, trying dramatically different approaches, keeping on iterating. 

In a lot of ways, we're unique in that we went through several pivots as far as like completely starting over and rebooting the product. But we never pivoted from the problem we were trying to solve. We kept starting over and trying to solve the same thing over and over again, which on one hand, I think is interesting. As you can imagine, it's a lot easier to talk about in the past.

In the moment, I think people talk about these pivots like there was some sign in the sky that said, obviously you should stop what you're working on and move to something else. And for anyone that's been through them, they're incredibly hard decisions, especially as you have even a small team. Because you have to convince even a small team that we should throw away everything we've worked countless hours on because it's not going to be the winning approach. And I think those are actually some of the hardest times because in the end, those people are really just sort of betting on faith on you and they're betting on their jobs, their compensation, things like that on like, are you leading them down the right direction? And it's probably one of the hardest things as a founder because those are the types of big decisions you have to make.

Pete (16:10):
I assume your co-founder was also technical?

Rob (16:12):
Yeah, his background was actually in building databases. While we were at Sutter Hill, he was actually advising the Snowflake team and working with them on their optimizer. So he was very deep into databases. He built a lot of databases and wanted to try to build something where more people could actually leverage what he was working on. So he got really interested in kind of the same problem that I was thinking about from a different direction. And that actually led the partner, Mike Spicer, we were working with to say, I think you two are talking about the same thing. Maybe you guys want to work together. So that was how we sort of got paired up and started working together.

Pete (16:42):
Because I wanted to ask you about, I see that you're listed as the co-founder and CEO of the company. And so I've seen this pattern before, where there's a couple of very smart, technical people in the Bay Area that get together and start a company. And I'm always wondering, did you draw the short straw? And that's why you had to become the CEO, and you probably would have preferred it the other way around. How did that go for you?

Rob (17:02):
Jason had purely technical roles, hadn't managed people, had really focused on being an architect and engineer his whole career. I had some experience at Salesforce where I had managed and led teams. I had seen a little more of the product side, a little more of the business side. So I think of the two of us, I was a little more suited to be the CEO. And I took that role on, I will say, somewhat reluctantly when I envisioned starting a company. I did not envision myself as the CEO.

But I think like many things in a startup, especially early on, someone's got to do the job. Similar to like I joke with people now about like there was a year I filed our taxes. Someone had to do it. And a lot of sometimes the fun part about early startups are just stepping into things and figuring them out. So I did that for the early years at Sigma. And as we started to get a real product together, I think both from a personal side of it just, it just wasn't me. I still loved code and build product. No one wants to hear the CEO is busy today because he's jamming away on a PR. 

And so for me, it was just back to that sort of happiness thing. I felt like I wanted to still really just be focused on the product and learning how to scale sales teams was just either my expertise at all or frankly even my interest. And so we started a CEO search. And it took us about nine months to find the right CEO. And then it happened to be that we were going through that right as the pandemic was starting.

So the funny story I often tell people is the last meeting, in-person meeting I had before the pandemic, literally the day that they were like, everyone's got to be home by five, the world's shutting down, was I met Mike Palmer, our CEO, for the first time in person because I was like, no way we can hire a CEO without ever physically meeting this person. And so I think I met him at like four o'clock on that day of panic.

Pete (18:53):
You had taken the company quite a ways by that point. That had been six years of company building and several rounds of funding. Were you Series C or so at the time or B?

Rob (19:05):
B, we had raised eight million in series A and a 20 million in series B. We had customers. We had, let's say, some early signs of product market fit, but not at scale. We had relatively trivial amounts of revenue. We had more sort of early fans than we had actually sort of figured out how to really frankly sell the product. And all of sort of the success and scale of Sigma has happened since that transition. That's really where the company, I think, went from almost being sort of an interesting incubation lab to actually sort of figuring out how to get this in the hands of users and how to really sort of scale the product.

Pete (19:42):
I want to ask you two other questions about the AI landscape, because that's on everyone's mind these days. And BI companies, I think, could be no less disrupted than other companies. One, how do you see the AI space affecting BI overall? And then two, why are so many founders building these talk to your data companies?

Is there a possible future where some small company can build a talk to your data layer via LLM and get that integrated into multiple different BI tools? Or are the BI tools themselves just going to win that race and create their own features in that space? And they have the distribution, and there's no room for a third party service there? I guess those are two separate questions, from more broad to more specific, but curious to hear your take on those.

Rob (20:30):
Not surprising to anyone, we've been spending a lot of time on AI. I was spending some time recently with customers and walking them through sort of feature roadmap. And I joked with them that our first slide was AI and that I thought every company they talked to their first slide was AI. So I think everyone's sort of mindset is there. And I think everyone sees a sort of potential for transformation and frankly, almost every industry. 

There's a lot of uncertainty about what exactly is going to happen. And this is true, I think, of a lot of technical transformations. I've told a story internally a number of times about I lived through like the transformation to mobile phones. And I remember when you first got a mobile phone, you thought of it literally as a mobile phone. Like all I did with this device was I could walk around and call people. At the time, that was amazing. Now, if you thought about it right now, the least interesting thing about my phone is that it can accept phone calls. If you told me I lost that feature, I'd be okay. For me, the fact that it's a mobile internet is like insane. I spend way too much time on that device. So I think often it's secondary effects from technical transformations that actually are the long-term unlock. 

Frankly, I don't think we're there yet on AI. I think we're only seeing sort of the first kind of wave of change. And then we haven't really figured out actually all the change that's going to happen from this technology. Specifically in the BI space and the chat with your data, I think this is really an interesting case because, yes, I've seen 30 companies whose pitch is you're going to chat with your data, and that is going to solve all the world's problems. And they largely seem to all show the same thing. It's interface where you type in a question, which somehow you knew the right question to ask, which I think is, frankly, one of the first misses in this. 

I'm skeptical people know the right questions to ask. If I knew exactly the right questions to ask, I would have just built a dashboard that answered those questions. So I think the first problem is I think in general people don't know what to ask. But I think the other problem I see in these interfaces is, you type in a question, it answers it magically, shows you a chart, and if you're lucky, it shows you some SQL or Python that it generated for you. And the big problem is that sort of magic step. So it's super cool for demos that I typed in something and it magically knew what I wanted. But the big challenge is, if I'm really trying to make decisions on this, how do I trust what it did? And how do I know even what it did? And if I'm the business user, I'm the type of person that I'm trying to solve things for at Sigma, showing them a wall of SQL or Python, not super helpful.

And even as a programmer, if you show me someone else's complex SQL query, it's an endeavor for me to figure out what this thing does. So when you're in an environment like we are today, where language models, for very good reasons, are only at best 90% correct. I mean, often it's going to be much lower than that. All of these things, I think, mean that you have to think very carefully about the interface that you actually expose to people. And so oftentimes when I talk about Sigma with people, I try to explain to them that there's a bunch of things we do that are interesting technically, and there's a lot of things we do interesting on the product side. But fundamentally, what we're focused on is building a better interface. 

And when I think about that specifically in the chat with the data space, one of the big things we're focused on is when you talk to our system, instead of it just saying, hey, magically the answer is 47, it actually walks you through, here's all of the steps that I'm taking. And each of the steps in that sort of chain of thought idea, it gives you lots of context about why it did these things or like why you might want to trust this decision or not trust it. 

So I'll give you an example. If you ask it some question and it tells you, I chose this table in the warehouse to use to answer your question, it will actually tell you like, hey, this table is being heavily used by all these people in your data team and used by these most important dashboards that your team is looking at. If I told you that, you'd be like, it's probably a good bit of data for me to look at. It's probably a reasonable thing. Whereas if you imagine if I told you like, the AI has gotten creative, it's picked this table over here, and no one's looked at that in two years. You'd probably be like, I'm little worried about this path that my magical friend here has chosen for me. 

And that's a bit of an extreme example. But in each step here, you can sort of imagine what is the language I would communicate to a user so they can understand, what the AI did, and have a lot of context on, is it actually doing what I expected? Because natural language is very ambiguous. You may have specified something that may have misunderstood or not understood your inclination. Long story short, I feel like there's a lot of these sort of very vanilla interfaces out there that I don't think are the long-term right approach. We've tried to really take sort of a different path to building an interface that we think is a much stronger solution to it, but it's one of the most interesting areas.

Pete (25:04):
And what you say is borne out even in the recent developments of O1 and now R1 deep-seq model, because obviously these quote reasoning models have chain of thought behind the scenes. And OpenAI has been stubborn and refusing to show that work. It's like you're doing your math work, and the teacher wants you to show all your steps. And you've done all the steps, but you refuse to turn in that sheet of the paper, and you just give them the answer instead. And probably because of proprietary concerns, OpenAI's refuse to do that. Well, now folks see that all that chain of reasoning and logic with R1, and it's all exposed to them. And I think that's been an aha moment for many people using these models, just generally speaking, is to see the model doing that work and to understand what assumptions it's making and where it's pulling its data from and how one step is leading to the next is something that a human can quickly synthesize. It's light years, it's a quantum leap ahead or so it feels just in the UX of interacting with these models of these agents. So I think that's been pretty fascinating for everyone is such a simple truth. Like we knew it literally knew it from math class.

Rob (26:03):
You know how you work with humans. If a human just came in and said the answer is 267 million you wouldn't just say great and walk away. You want to understand how they got there and I think the best interfaces when we think about these things are going to be like we actually make it like a very usable interface something that works just like we want to work well.

Pete (26:20):
Rob, it's been great chatting with you. Really appreciate the time. I want to ask you one last question and sort of just an extension of this conversation. What do you think the future of BI looks like? You've spent a lot of your career in the space now and I'm curious, what do you think's around the corner?

Rob (26:34):
I think one of the really interesting transitions, I often look at what are the big changes you see in technology and then how that unlocks things. And we've talked in the last few minutes a bunch about AI. Obviously, that's a big part of it. I'll cite maybe another one that I think is super interesting. When we started the company, data warehouses were very much looked at as almost like a read-only thing. People would essentially bring data into them. They looked at them for purely analytics.

If you look at where those platforms have transformed over the last, say, five years, they've gone from just being purely the sort of analytical stores to now people are actually thinking about them for operational applications. So if you think about the data warehouse, it's where you collect all of your company's information. So increasingly, it's like the only place where you may have all the data about your customer or your product, because it's being brought in from all of the different systems that interact with those people. 

If you want to build an application that works on your customers, it's going to be sort of natural that you'd want to do it in the place where you have all the information about your customers. So now this data warehouse becomes not just like a traditional OLAP analytical store, it actually now becomes a foundation for applications, becomes a foundation for where you run your business. And so we've thought a lot about traditionally Excel is the leading BI tool by far. People don't consider it a BI tool, but we all know this is the number one BI tool by far. 

One of the interesting things that goes on in Excel is that people essentially string together these applications or these workflows. That's the old joke that business runs on spreadsheets. We believe that as data scales, as people care a lot more about governance and security, they can't have data just running around on people's PCs or flying back and forth in email. And so they want to have data, especially at scale, centralized in these systems. It then transforms it to how do I take all these workloads that people traditionally did in spreadsheets? I want to build these quick applications on top of these warehouses. And so we see the sort of BI space broadening from just, hey, can I do traditional dashboarding and reporting to like, can I actually build workflows and applications and not just see data in a dashboard, but actually take action and change things and move? And so that's been sort of a big focus of ours over the last few years and one of the things I'm really excited about in the next few years.

Pete (28:55):
Yeah, it makes perfect sense. And we're living in a data-driven world. I think the data is certainly, we all talked about democratization of data for a long time. But it seems that data does want to live in these applications and be accessed by more people. And we're entering into a whole other level of how to realize that with AI features and the cloud systems being widely and just sort of honestly the business appetite and the utility of having data in multiple places. So it's going to be a very interesting couple of years on the application layer side.

We've been sort of a data council, at Zero Prime, been investing in the infra layer for a long time. But the realization of that infra layer is ultimately inside applications. So it'll be amazing to see what people start to build on infra, that is quite honestly becoming a little bit more table stakes, or at least commonly deployed. So we're excited about that future with you as well. Well, Rob, this was a really fun chat. And thanks for jumping on with me and sharing your wisdom with the community. Really appreciate your time.

Rob (29:48):
Absolutely, thank you for having me.

Pete (29:53):
Thanks for joining us for another episode of Zero Prime Podcast. I hope you enjoyed my chat with Rob Wolin. Don't forget to hit the like button and subscribe in your favorite podcast player to get more episodes from the Zero Prime Podcast. If you like hearing from engineer founders on the cutting edge of enterprise startups and developer tools, please leave us a review on your favorite podcast app and subscribe to the show. We'll see you next time.