Chang Xu — Basis Set Ventures

Wednesday, May 25, 2022

Chang Xu is a partner at $140M fund Basis Set Ventures.  We discuss everything from why open source is a good business model, how next gen infrastructure might look, what she learned at Upfront, and even practical advice for forming a startup board.

View More Transcript

Hello and welcome to the LA Venture Podcast. This is Minnie Ingersoll host of the podcast and partner at TenOneTen. TenOneTen is a seed stage fund here in LA. All opinions expressed on this show by me and my guests are solely our own.

Chang Xu is a partner at $140 million Basis Set Ventures where she is investing in AI and technology companies that transformed the way people work. Prior to joining Basis Set, Chang was a principal at Upfront and was a founder and operator before that, Chang, nice to see you.

Really great to be here, Minnie.

Thanks. Well, why don’t we start with Basis Set and you can tell me a little bit more about AI automation and what you’re looking at.

Awesome. So Basis Set Ventures, we’re $140 million seed funds investing AI and automation. We’re very thesis driven and we have four different thesis that we focused on. Infrastructure, collaboration, automation, and autonomy. I spent a lot of time on the first thesis, which is infrastructure developer tools.

And how we think about infrastructure is the very backbone of how tech companies are constructed. So like AWS, Azure, Github, Twilio, these are all examples of infrastructure companies. And the other thesis that we invest in collaboration, automation, autonomy. my partners are just the perfect fit.

They both spend a lot of time in the early years at Dropbox combined 10 years, at Dropbox in the early years and her partner Long, she led Corp dev M and A at Dropbox during their most inquisitive time where they acquired from dozen companies and has literally seen every single collaboration tool company in the Valley.

And my partner, Sheila was the second marketing hire on Dropbox. 

Well,  let’s talk about your area of focus. Um, tell me more about, you know, the further sort of sub-segments that you’re doing.

Yeah, love to. So we have different thesis within each of them. And how I think about infrastructure,it is broken down to four themes. I call it racist ceiling, lower the floor, open source and data privacy.

Fascinating. I had no idea. I mean, you and I hang out a fair amount. I did not know that you were raising the ceiling or lowering the floor. Tell me more.

Very much. What about construction here? Uh, no. So for raising the ceiling, what I mean by that is there always going to be better and secure and faster, fundamental building blocks for infrastructure, uh, to the tune of database systems and as streaming data and infrastructure and infrastructure as code and all sorts of ways of, of how developers can build things and put them together in a more powerful way. So we will invest against that as the second is lower. The floor lower. The floor is about, no code, low code is, uh, it’s about enabling developers to be able to build where non-developers to be able to build without having to hire for the requisite skillset. We’ll just spend the time, but so that they can still leverage modern infrastructure just without the hassle.

And then the third one is open source. We’ve been investing in open source since we started the fund in 2017, when open-source is not obvious. And now open source is kind of a hot trend and it’s, not, surprising, uh, why? Because open source is almost table stakes for how infrastructure is discovered, bought, and sold today.

At the same time, open source is just really powerful. Uh,  The community for open source is very powerful way for products to be discovered and sold into really large contracts. So there are a number of different reasons why open source is going to be a driving factor in a lot of the fast growth information. 

We can I do,

can I ask more about that? So open source is going to be to lead to large contracts is not like the world’s most obvious thing to me. Source sounds sort of free. So can you just tell me more about open source and how the business model works?

Yeah, of course. I’ll tell, I’ll tell you about it through the eyes of one of our portfolio companies. So w in late 2017, we led the seed round for a company called rasa. It was a small company based in Berlin at the time, just two guys and open source projects. At the time, um, they’ve already gotten really awesome early adoption and it’s so the company through rasa is a open source, conversational AI platform.

At the time they already got really awesome early adoption. Some of them are, leads into enterprise customers that are looking to use conversational AI, but they have a ton of internal data on how they communicate with customers. They didn’t want just a generic chat bot. They wanted a chat bot that’s tailored to their company, their product, and it’s high quality.

So they go to rasa and fast forward to today. last year Andreessen led a series B in rasa. and now they have over 10 million downloads. they have over 600 people contributing product to the company when they only have 150 people employed at the company. So it is a powerful modes from a product development, part of velocity perspective, also from a sales perspective.

Um, it’s great Legion for the companies like banks telcos and the like that’s discover them and work with them.

How do they translate? How does open-source usually, how does go to market usually work with open source in terms of like translating a lead into a paid contract?

 several ways. So it often starts was developers at a company that has a mandate to build some sort of products in this case, conversational AI to look for what can I use, what are the options? And they will try to play with the different companies options. And in Ross’s case, they will play with them through their free product, through open-source product, free product.

They would, they would play with them in, in. their own sandbox environment and, and try it out. And if they like what they see, then they might get engaged in the community and ask questions. And at that point, that developer and probably their immediate, um, coworkers are pretty interested in this. So at the point that rasa and other open source companies come into a company for a sales conversation, the customer has already.

Self-selected and is already much further along in the funnel for something that will be pretty mission critical for the company and a large contract and therefore a large buying decision.

, how do you measure when you’re looking at an investment into a company like rasa, how do you decide that it’s going to be successful?

What are you looking for there?

I wrote a blog post late December, publish it late December last year. I kind of debunked. a myth, which is most people look at the number of stars and the stars growth, but that’s really just a surface level metric.

If you talk to companies that are building an open source today, I think a lot of them would find that they resonate with that. What really matters. So we dug into all the different metrics that you can find on GitHub and other channels. What really matters is issues and issues are when people will.

Tell you about something that’s not working on your product or file a bug or ask the question. Um, and why does it matter is because when someone’s reading, engaging with your product, at the level of asking issues and filing bugs, they are much more interested in and much further down in their journey with your product than someone just yeah.

Clicking to start something. So we, we very much look to the two, three layers down and don’t just look at the service level metric to understand what projects really have adoption and traction and engagement, because that’s what matters in most of the early stage.

Interesting sounds like looking on GitHub, not beyond the stars.

Yes. Beyond the stars.

and I think I interrupted you because I was interested in open source. There was raise the ceiling lower the floor open source. Did we mention the fourth one?

Last one is data privacy and with increasing amount of data, just everywhere. Also in this world where a lot of apps are built by putting together APIs and module components. Your data could just be sent across many providers at the same time. You have Data regulations, that are increasing popular.

GDPR CCPA did a resident, resident regulations and just a number of these regulations. So how do you innovate fast and how do you really take advantage of all of her data, but still stay compliant? So we see data privacy as a really large, tailwind, and emerging space So we’re absolutely interested in back more founders building this space. that’s that we invest in.

Um, well, let’s go back to raising the ceiling and lowering the floor. Cause both of those are interesting. Um, I mean, no code, you talked about, open-source being trendy, but no code is super, hot. I would say. what are you seeing in no code? what excites you about this space?

Yeah, I would actually lump together with no code and low code. because if you lump that together is actually a huge segment of the population, One way to think about it is, think about how many people knows how to do SQL versus how many people knows Excel. It’s, it’s a, it’s a huge comparison, people that know Excel.

And, especially think about the people that can build fairly complex Excel models, they essentially know how, how to think. like, uh, like programmer like a developer, but they might not know or want to. Putting in the effort to write national programs. But if given the tools, they can modify things, they can copy things.

They can Google, they can, they can build something from scratch. So that is the huge nascent market we’re going after it, enabling everyone like that, to be able to build apps, to not have to resort, to hiring software developers, not re, not have to resort to outsourcing that work for them too. To build whatever’s in their mind, whether it’s internal tools or whether it’s, uh, some small program they’re launching for their company or something external.

Yeah, I’m excited about everyone becoming a creator, like just to be able to create the thing that’s in their head, as you say,

Yes, absolutely.

it’s fun. Um, and then I’m gonna imagine that raising the ceiling is actually some of the more technical stuff you look at. so what sort of things you’re looking at, for the advanced developer tools, I guess,

yeah. we think a lot about data infrastructure, ML ops. just the next generation of infrastructure in general, for example, spaces that we’ve we’ve explored are in the new data infrastructure world. A lot of data is moving to streaming from batch to streaming but the Ram ramifications go up and down the value chain.

So how do you merge fashion and streaming? what can be moved to streaming? What will stay in batch? So we invest in companies along that continuum. We’ll have this Delta investment that, that we can’t disclose right now. Um, and also we just invest in, in picks and shovels of infrastructure.

There are certain critical pieces of infrastructure. That’s just. Missing, for example, everyone uses Stripe to process payments,  But Often a company that integrates stripes still has to build a lot of things internally in order to be able to process payments. and we see that as an increasingly large use case in the wave of companies moving online and taking payments in, not just the very simplistic transactional means, but like recurring or usage based or any other otherwise.

And we will fund, uh, infrastructure pieces that have just been missing like 


Hm. And you’ll fund infrastructure, but you’ll also fund application layer,  can you also just define ML ops for me? Can you just explain what that field is?

Is the idea that. It is so challenging to take a machine learning model to production that I think there’s some statistics that says 99% of machine learning models never make it to production. And one way of solving it is if you think about the entire dev ops tool chain of what it takes to take software to production, it’s a very now very.

Return landscape with a lot of multi-billion dollar companies along the entire DevOps tool chain, you have best in class tools at every single step of the tool chain, but AML, it is super nascent. So I imagined that the future of model ops will likely look at, look like something along the lines of dev ops, which is best in class tools at every single step, starting from maybe its feature stores experimentation.

model serving and the different steps in that chain. So that’s, that’s like one way to think about it. another way to think about it is Some companies They don’t really meet or want such a heavyweight, tool chain, On deploying their data science to production for them ML, maybe less ML, but more just data scientists or even shorter that it could just be data analysts or business analysts. And you could have tools for that too, for them today. Maybe it looks like, Google CoLab, or something that’s very visual and GRI based and for them, the tool chain looks different.

So we think that there’s going to be. A proliferation of tools that enables people very much, like you said earlier, that enables people to, to create regardless of their technical abilities and their starting point. 

Hmm, I guess sometimes, um, when I think about AI or I think when some people think of AI, they think of sort of the algorithm coming out of a university. And I think what you’re saying is actually the algorithm could be replicated. It’s really getting that into production and, and sort of the application there.  , well, that is a great, overview of the sort of stuff you’re looking at. maybe give me a little bit, let’s go back to lawn and Sheila and just overall, the broader spectrum of stuff that you guys are looking at.

I love talking about my partners. They’re so easy to brag about because they’re the most amazing partners one can ask for.

Aw, that’s sweet. I’m always saying the same about David and Gill, but, uh, tell me more about their investment thesis. I was looking at your portfolio and, uh, it’s fairly broad in the category of enterprise software.

Yes, it’s very, it’s very buck, but they’re there comments, threats. Like we think that we’re in a once in a generation transformation as company go from on-prem to the cloud, as companies go from monolithic architectures to microservices and APIs and the first generation of winners are just frankly, moving companies to the cloud, enabling that move to be possible.

Well, we think that the next generation of breakthrough companies will be intelligent, connected, and led by their product for technology. Um, they will have, uh, the same characteristics as a Dropbox Figma or Rosso’s of the world, or they won’t win.


So that’s the common thread on, uh, on everything that we invest in and the thesis of our funds.

Hmm. So when you say they’re not just in the cloud, they’re actually intelligent. break that down a little bit more for me.

Yeah. So if you think about, uh, Dropbox, where it’s, it’s a file system in the cloud, you still have to know what folder you need to go to a file you’re looking at, but what if you nose into the four, you need to go look for it. What folders you need to look at and what files you need to go to. And that’s, that’s what intelligence means.

Okay. Hard to imagine something better than my Google drive folder system. But, uh, but what you said makes sense. Uh, it’s not just translating into the cloud, what you were doing on prem, but it’s really adding in a layer on top of that. 

Of AI on top of that, or first data on top of that, which later you can train into AI. 

Um, do you make a distinction when someone says AI and you’re like, ah, it’s not really AI, that’s like advanced ML or something. Do you guys look at that? And you know, are you, do you hold, uh, are you judgemental when companies come to you claiming AI when it’s really something less than AI?

I think we were pretty good at understanding the technology and assessing technology much, like much like you, I tell on 10, it’s one of our core advantages, but we  don’t hold it against. The founder is very much around what is the market you’re going after and what does it take to win?

Like we do invest in cutting edge AI, companies like our investment in paths, robotics. They’re a autonomous welding robot and they have used computer vision. Trained the best computer vision algorithms to be able to detect exactly what seems to need to weld and their algorithms are so good that they can, uh, they’re not distracted by say if there’s glare or a shiny metallic surfaces, which could put off just your standard algorithm and they can also be able to actually detect the scene, even if it’s different from say the CAD drawing that they were referring to.

So we invest in companies using cutting-edge. AI and ML, but we also just invest in companies where they don’t have any ML today, but they’re building a data and workflow advantage that over time. That is a great foundation for AI.

I love that you, you brought up your AI example was, was autonomous welding. That’s great. Um, okay. So I went online, I’ve never met lawn. I went and listened to a talk and it was about like AI and automation in the time of COVID and what she actually talks about was all this founder psychology work.

That you guys had done. Um, so I’m fascinated. Maybe you could give me the overview. Um, I know you did some work on that. Maybe you could give me the overview of what you found with founders psychology.

Yes, of course. So as a cetacean investor, we hear a lot of debates among our peers on what are the traits that makes for a successful founder. And because as C stands for investors, we’re often in the role of evaluating founders on whether they can build a big company or not. And we find that a lot of CCN professors, they have the notion of, okay, it’s, it’s something on their resume.

It’s the logos you’ve worked at before. It’s where you went to school. It’s your age? It’s your experience is whether your MIT dropout. We’re not because a Jew in a rush dropped out at MIT and found a Dropbox. So of course that’s back more, but we think a lot of that is misguided. So we conducted this study where my partner has her PhD in psychology, and she applied that PhD and we collected a lot of data on successful founders and less successful founders.

I looked at the differences, we ignored everything that can be found on a resume. I looked at attitudinal traits, that we think could matter, and these are things like. Execution ability, day-to-day effectiveness, scrappiness, agile thinking, um, and, and items like that. And what we found was, was really surprising.

we clustered the founders into two different archetypes. There’s three archetypes of successful founders and three architects of less than successful founders We give names to each of these archetypes, successful founders are humble operators, agile, visionary. And seasoned executives and the less successful founders, they are passionate outsiders, overconfident, storytellers, and stubborn individualists.

And you can find all of this research on our website where you can go to founder 

I feel like I see, overconfidence, storyteller.

can definitely call those out now.

Yeah. It’s going to be a question I had, which is where do you find yourself referring back to these? Like when someone pitches, are you like, you know what, I think that might’ve been an overconfidence storyteller or where do you find yourself using these in practice?

so in our research, we looked at what differs a overconfident storyteller from an agile visionary, for example, And the two archetypes are, are pretty similar in that they both can tell a good story, um, and has really good vision about where they want to see the industry and their companies have become.

But one thing that we found different and I am going to look on our pages so that I don’t say the wrong thing.

. So tell me, yeah, go back to telling me the difference between the over-confident storytellers and the agile visionaries.

Yeah. So the attribution overconfidence storytellers are, there are similar across storytelling, confidence and founder market fit. But they’re very different on two traits, which is agile thinking and scrappiness. And it’s because overconfident storytellers, they are super charismatic. They tell such a great story of what the, what the vision should become, where they’re going with the company, but they’re not as empathetic.

and they don’t listen as hard as to customer feedback on where the market is to be able to really rapidly. Navigate, and they’re not as scrappy and executing. It’s a super subtle differences across all of these types of butts. Our research has pushed us to think a lot harder and it asks second, thirdly, our questions, uh, whenever we work with founders,

interesting. Um, yeah, I I’m trying, I like this. I like this whole theme. I want to keep, um, anything else you, you know, have you been surprised when you invest in people, do you think people can change who they are? Um, uh, anything else interesting to draw out of this work? Um,

I think what’s, what’s fascinating to us is. This actually just took off real virally among founders and some founders reach out to us and they self identify. As one of the archetypes, we’ve had a number of founders, that’s that say, Oh, love, love this work that you’ve done. I am absolutely a humble operator.

You should, you should back me. Um, and then we also had a founder friends that, uh, that they reach out to me and said, Oh, well, thank you so much. This is exactly where my last startup failed because I was a passionate outsider. Um, and this has finally helped me to, to put a framework around, around that failure.

What’s a passionate outsider again, like w what does that mean?

Passionate outsider are, are folks that are outside of an industry. And they’re really passionate about changing the industry and they have an idea of how to change, but, uh, that founder market fit it’s it’s just not there.

So a passionate outsider is a, is not a good architect. Right.

No, it’s not.

Okay. Well, you know, you might want to change an industry that sometimes we will say like the ignorance, 

Sometimes it works and sometimes he doesn’t.

Uh huh. The other thing I know you guys have been doing a lot of, is building out, what do you call your network of sort of mentors and sort of this platform you’re building.

Yeah. We call it the hyper-growth network. we’ve been Paladin internally with our portfolio for a while and. We’re excited to finally open it up to the public. So it’s along our theme of the next generation of companies. The next generation of winners will be intelligent, connected, and have product line growth.

So we have tapped our network, off go to market leaders and product leaders across all the best Silicon Valley Bayer companies, and just privatized that, that network. So if you think about the value that we offer as investors is we offer bespoke intros and connecting points of our network. This way, we took ourselves out of the equation and our founders, and also a select number of founders outside portfolio can just get in touch with anyone in our network.

Pre-vetted and they could be your advisor, your mentor, your customer, or which potential potential partner.

did you think about how to structure that? Because as I understand that yours is just sort of an open Rolodex, if you will, some, some people do more like, let me pair you with some mentors.

Yes. We very much want it to be. Open and for this program to scale, we have hundreds of mentors signed up across, uh, hundreds of logos and across every single function, but we didn’t want to put too much structure around it.

And just want to see where this program takes off.

That’s really interesting. So, it’s just elicit people who say they’re willing and interested in taking a phone call. And why do you think the mentors choose to participate?

For them it’s uh, it’s no obligation, no commitment. It’s pure. Everything is doubled often. And. For them, it’s a great opportunity to discover up and coming startups that we’ve pre-vetted and that they can, invest in, if they’re interested in angel investing, a lot of operators nowadays are, um, they could advise them and they could partner with them.

also, we’re going to build out programming for, for just the mentor side, because they’re interested in connecting with each other. And I think that will be a wonderful network, to tap into as well.

That makes a lot of sense. I think that’s the reason a lot of people want to do these things and sometimes they want to put it on their LinkedIn or maybe they want their face on the website.

Yes, we have a beautiful website with, uh, some faces.

Um, and so when you’re doing this programming, I know, um, I’ve seen you on clubhouse. I’m curious. I’m not, I find in clubhouse kind of stressful myself too much.

Well, palace, it is a total riot.

Yeah, well, what have you what are you doing? What are you seeing there?

So in clubhouse, we run the AI club, uh, which is a place for experts to come and talk about AI. Uh, and we brought, we define AI very broadly because we really see AI as the next wave that will, that will perfectly across all companies. Um, just like cloud and SAS was the last wave. And we featured folks like hyphen Lee who wrote the book AI super powers and is a thought leader around AI.

He was formally led innovation at Google and Microsoft. Uh, we’ve also featured folks like the CMO of confluent col Leo Netty confluent is a $4.5 billion company built on the open source project Kafka and is it’s absolutely in. Uh, incredible company, um, in that space also hella men who was the head of marketing at angel has previously led marketing at plaid Dropbox, and just has great, great advice on how to do marketing to developers because that’s slightly different.

And we have so many of these lined up as you can, you can see if you recognize some of the names. There are also a folks on the mentor side of our hyper-growth network, where it is all very synergistic for us. We also have a podcast called hypergrowth where we interview, similar leaders and talk about how do they grow their companies in the earliest days.

Um, and these are the first growth leaders at. At like notion or sauna or Stripe and, um, and all these companies,

Great. Um, I will get on clubhouse. I will come listen once I, once I figure out where in my life clubhouse fits. I’m not sure yet. Um, great. Anything else on Basis Set? Otherwise I might ask you about, you know, your previous, I might go backwards in your history.

um, Nope. Have you talked about anything else?

well, I mean, let’s, um, let’s just talk about, a little bit about upfront and then, you know, your founder experience. So maybe you could compare, like, what do you think you took from upfront, when you came to Basis set.

Yeah, um, upfront is a, uh, sorry, let me start over again. Upfront has built an incredible institution over the past two decades. And has definitely put LA on the map for, for, uh, it’s venture ecosystem. And upfront is also from, that’s just fantastic at, at Brent and telling a narrative and telling a story that’s compelling.

So I’ve took a lot of that and, and took it with me. I placed a set and imbued it in some of our work, like the founders psychology work, like open-source metrics. Um, just. Telling stories in a way that resonates with founders, but backed by data and backed by a, a strong vision for, for the, for the fund that we want to build on for the future.

We want to see.

Yeah, I would say that, um, S there were some very compelling presentations that upfront did, with great data. And I believe that you, in fact, were the person who pulled that data and those presentations together.

I spent so much time on PitchBook and the many other sources scraping data.

Do you remember? Like, do you remember like the 2019 one? Um, I mean, I think you did a lot of the work, but I think Mark presented them.

Mark is fantastic at storytelling. He can always, uh, we worked together on pulling the story out of the data because everybody has access to the data. The data is not proprietary, but what’s really proprietary is. Is what is the story there? What actually matters? What should people be paying attention to?

What has actually happened happening in this industry that we all love called early stage venture?

. Um, okay. Totally fair. Uh, what about, um, Uh, why don’t we talk a little bit, but let’s go, let’s go before upfront. And, um, you were a co-founder and also an early first product manager, both at two different education startups. Right? 

Yes that I, I co-founded, out of Harvard with a friend in ed tech, it’s called NEMS at the time, we also saw the. start for cell Khan and Khan Academy. So Anya math, it’s kind of like, well, bring in Khan Academy, but to China, which has just a very different and huge, education technology market, where they create adaptive learning software for kids to learn middle school math and starting from there into expanding to other subjects.

So. I was two years out of college and, building the startup. And I learned so much about how to empathize with founders at the beginning of their journeys. and that’s, that’s also where I found the bug of, I would need to be in a startup world because I’m so motivated every morning to wake up and go after the vision that I’m really passionate about, but also work on it with a group of people that are passionate about the same vision.

And it just drives me like no other I’m an over project is.

Can I just stay on, uh, on onion, math. So are there any, you know, you said the China market for ed tech is very different. Are there lessons that we should be learning because China’s like the Canary in the coal mine. Like what they’re doing is what we’re going to see here soon.

Yeah, I think there’s a lot of lessons we can take away from, from Chinese tech. Um, funny story. We’ve actually taken our founders at BSC to, uh, to China on a trip. And I think the trip was actually covered and in the New York times, um, where, where we tour all the Chinese tech companies and the, and the growing companies.

And one thing, for example, it’s, it’s 906, which is the working hours of 9:00 AM, 9:00 PM, six days a week. And just to the relentless, um, and the competitiveness in that market, everything moves 10 times faster than it is here. and I think we very much, like learning from, from China.

Hmm. but you don’t want to end up, I mean, do you want to be aggressive? You want to move quick, but nine, nine, six is not necessarily something where I’m like, Oh, that’s what I want to get my startup to.

Yeah, w we don’t, we don’t advocate for that at all.


It was just interesting to see a different perspective and a different sense of urgency. any sort of different perceptive perspective opens doors and. Oh offers other optionality for what’s possible.

um, okay. And so, and then, and then Minerva project.

So the mineral project is rethinking what a tech enabled elite education could look like. And one that can scale. And I was the first product manager at the moon river project. 

and where do you see that? W where do you see higher ed and elite education going?

Yeah, that’s, that’s really, really a tricky question. Um, higher ed has had a number of headwinds. Over the recent years and also with COVID has certainly really not helped it’s financial pictures. Your, your dad is, uh, was a professor at Caltech, so I’m sure you understand this all too well. That’s I think higher ed, if it doesn’t innovate in 10 years, there’s no way higher ed will look the same as it does today.

Um, and do you think that kind of combining these two themes, do you think that, uh, like in China, I think there’s this huge sort of private tutoring market. Do you think, how do you, do you think there’s lessons from other markets that we’ll see in, in education here in the U S.

Market. It’s just a lot more vibrant in China than in the U S for like the slew of reasons. But for example, China, an entire country runs on the national college entrance exam. Whereas we don’t have anything off the thing magnitude or importance here in the U S but I do see that the, the modern future of work just becoming really different, but it, most of it, I think most of the Tam will not be selling into the traditional educational institutions.

So let me move into my random, random question section here. Um, first random question was, um, you know, I saw you present at innovate, Pasadena, Chuck and I both live in Pasadena. Um, and I just want to recreate some of that cause it was so good.

And it was such tangible advice for founders. Um, you presented on boards that startups should have so quick Chung, how many board members should I have and when should I form my board?

Yeah, I think you should form a board when a institution investor in visa around and asked you to, or rather you should consider a form of your board. When an institutional investor leaves around and then asks you to, and in the beginning, uh, boards are typically say three members, which is often found her a common seat, which is often your co-founder.

If you have one and the investor. So in the beginning, boards are founder controlled boards. Um, and then over time with each subsequent rounds, the new lead investor would likely want to ask for a board seat. So the, the next rounds, you often go from a suite of five because boards often have an odd number of board members.

Otherwise the tie-breaking becomes a little tricky. So if we go from three to four, three to five, you. Often get two comments. One is the founder. One is the co-founder and two investor sees. One is often a senior investor. And then the second is a series a investor.

And the last seat is a independency that everyone has to agree on. And then that is a balanced board where neither party really dominates. And then over time, you’ll graduate to an investor led board. That’s that’s the most common path for companies.

So when you invest, when you Basis Set, invest. do you take a board seat in a seed round?

We sometimes do we, don’t always, and it’s very dependent on the situation and also on what the founders are looking for. Also how big of a check we’re writing and what the dynamics around us.

Yeah. The example you just gave of having three board seats, we often don’t take a board seat in the seed round. So we would say even you can postpone that sometimes till the a.  okay. Second question. That was a good one that came up at innovate Pasadena. my source for all knowledge. Um, how do I think about what a board votes on versus, you know, sort of what requires a shareholder vote?

So these are the things that would get outlined in your, your legal docs when you negotiate them. Um, but. Typically a board vote. It’s more expedient than going to all your shareholders and ask them to vote because that could include all of the employees that have formally worked for you, but now have electric company.

Um, so things that boards typically vote on are. Any M and a, um, acquisitions or change a business for the company or, uh, taking on a large loan or venture debt or, or a large, uh, contract contractor, um, hiring, uh, anyone senior, uh, at the company and approving new financings.

Anything else on the boards. It was a great discussion. Everyone should go back and listen. It’s online talking. It’s really good. uh, John, where did you grow up? What did your, what w what did your parents do when you were in high school?

I grew up an hour West of Boston in this town called Shrewsbury, Massachusetts, where, uh, I did a lot of shoveling in my youth. Uh, and built a lot of character, but nowadays I love living in LA love, living in Pasadena, uh, where there’s no snow and no shoveling. And so I would no never trade it. Um, my parents, they’re both chemists.

So my dad is a chemist at a pharmaceutical company and, uh, he. He’s on the R and D sides to make drugs for cancer. My mom is a chemist by training, but then, uh, when she came to the U S she, she changed her career and became a lab tech, a lab technician for a cardio, uh, cardiovascular lab at UMass. Those are just too long.

My, my mom works. My mom. My mom is at a medical school, uh, working in a cardiovascular lab.

Fascinating. I’ve known you for a while. I had no idea. well, this is great, John. I just basically said, seems like such a great fit for you  📍 and you always have such great insights. So thanks so much for coming on the pod today.

Thank you so much, meaning it was really, really great to be here.