20VC: Greenfield Opportunities For Machine Learning, Why Massive Corporates Finally See It's Potential & Why VC's Investment Decision Making Process Needs To Change with James Cham, Partner @ Bloomberg Beta

James Cham is a Partner @ Bloomberg Beta and one the pre-eminent thinkers in the machine learning space. Prior to Bloomberg, James was a Principal @ Trinity Ventures and before Trinity, James was a VP at Bessemer Venture Partners. At Bessemer, James was a Board Member of CrowdFlower and Open Candy. However, James has not always been a VC as he was originally a programmer receiving his degree in Computer Science from Harvard. In Today’s Episode You Will Learn: 1.) How James made his way into the wonderful world of VC from being a programmer? 2.) How are organisations and corporations thinking about machine learning? How are they looking to incorporate it into their current infrastructure? 3.) Kieran Snyder @ Textio said, ‘the writing is on the wall for enterprise to embrace machine learning’. What are James' thoughts and how does the business model vary from licensed and SaaS? 4.) How is building machine learning models different from software development? On the topic of software development, how does James perceive the productivity differences between software developers? 5.) Moving to VC and how does James approach the investment decision making process as a VC? How does James relationship to religion intertwine with his investment career? Items Mentioned In Today’s Show: James’ Fave Book: Man Who Lied To His Laptop James’ Fave Blog or Newsletter: Rob May, Jack Clark, Tyler Cowan James’ Most Recent Investment: Netlify As always you can follow Harry, The Twenty Minute VC and James on Twitter here! Likewise, you can follow Harry on Snapchat here for mojito madness and all things 20VC. Angelloop is the leading post funding management platform for private market investors and their portfolio companies. They help investors manage and track their portfolio companies on the cloud while providing them with access to their investments performance data. Angelloop helps founders of startups track their performance, manage their cap table and keep their investors in the loop. Investors get free access while their portfolio companies pay only $49/Month. Use or share the promo-code 20MinVC to get your portfolio companies online with a two month trial. This episode was brought to you by DesignCrowd, the online marketplace for custom graphic, logo and web design that helps startups, entrepreneurs, web developers and agencies outsource design projects to designers from around the world.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Harry Stebbings?

  This is the 20 Minute VC with your host, Harith Stebbings, and you can add me on the old Snapchat at H Stebbings. I'd love to hear your thoughts on questions for upcoming guests, and I think Snapchat really is the best platform for that. However, to the show's day, and for long-term listeners, you might remember our interview with Roy Bahad at Bloomberg Beta. Well, I'm delighted to be joined today by Roy's partner, James Cham. So James is a partner at Bloomberg Beta, and I have to say, at Bloomberg, James and the team have really become masters of the machine learning space. And if you haven't checked out James's colleague Siobhan's deep dive, that is a must and can be found in the show notes. But back to James, and prior to Bloomberg, James was a principal at Trinity Ventures. And prior to that, James was a VP at Bessemer Venture Partners. However, I don't think that actually really paints an accurate picture of James, as he's originally a programmer,

• 0:44Harry Stebbings?

  shown through his Harvard CS degree and very developer-centric mindset. I'd also like to say a huge thank you to James's partner, Roy, and David Aronoff, partner at Flybridge, for the interest of James today. But before we meet James, if you're a founder or entrepreneur looking for logo design, site design, custom graphics, or even t-shirt and billboard design, DesignCrown is the place for you. DesignCrown is the online marketplace for all things design, even big brands like HTC and Virgin Active use them. So how does it work? Well, launch a project, designers begin submitting quality designs for you to review. You then quickly build up and generate a large gallery of designs that fit your needs. Once you select your favorite designs, you'll be sent all the files you require, and all of this can happen in just three days.

• 1:27Harry Stebbings?

  So much better and more reasonable than an agency, and perfect for small businesses and entrepreneurs looking for fast response times and reasonable pricing. So head over to designcrowd.com/vc and enter the promo code VC100 for an incredible $100 off. Likewise, if you're a founder or investor, Angel Loop is a must. It's the leading post-funding management platform for private market investors and their portfolio companies. Angel Loop helps investors manage and track their portfolio companies on the cloud, while providing them with access to their investment's performance data. Angel Loop helps founders, on the other hand, tracking their performance, managing their cap table, and keeping their investors in the loop. Investors get free access to the platform while their portfolio companies pay only $49 per month.

• 2:12Harry Stebbings?

  Use or share the promo code 20MINVC to get your portfolio companies online with a two-month trial. However, as always, I'm sure you've heard enough from me, so I'm now delighted to hand over to James Cham, partner at Bloomberg Beta.

• 2:31Speaker 01

  You have now arrived at your destination.

• 2:34Harry Stebbings?

  James, it's such a pleasure to have you on the show today. I really have heard so much from so many wonderful people, from David at Flybridge to your partners Roy, Siobhan, Karen. So it's fantastic to have you on the show. Thank you so much for joining me today.

• 2:47Speaker 02

  It's my pleasure. You know, I've listened to a number of the podcasts recently and I have to admit I'm a little intimidated. You've got some great interviews.

• 2:55Harry Stebbings?

  Well, you're too kind and I have no doubt you'll live up to them. But I'd love to get started today by discussing you and how you made your way into undeniably what I call the wonderful world of venture.

• 3:05Speaker 02

  You know, I think that I have a number of friends who knew that they wanted to do venture investing from the point that they were 14. And you know, I have one friend who systematically went about similar to you to talk to every single venture capitalist they knew that they could reach and establish themselves and then find a great job at a great firm. I came about it through a much more roundabout way. I was a developer after graduating from school and to be honest, I had no intention of ever hitting the venture side, in part because my observation was that many of my friends who ended up in investing turned out to be worse people and so had no deep interest in that. But I, through a variety of circumstances and through a couple of failed startups, ended up going to business

• 3:50Speaker 02

  school, which is where I think one escapes occasionally to retreat a little bit. Thanks to a good friend of mine, I was introduced to David Cowan at Bessemer and he caught me at a point of transition and we really got along. So for a number of years, I apprenticed under David and learned to love the world of venture.

• 4:11Harry Stebbings?

  So I'm intrigued, when was the mindset shift for you then in terms of appreciating kind of all that you had to offer to the world of startups from a venture perspective? When did that happen that actually being a VC was actually a very viable option for you?

• 4:24Speaker 02

  You know, I think some people become VC's because they like the opportunity for fantastic returns and some people become VC's because they read too much science fiction. And I Clearly was part of the second camp. Unfortunately, David was also part of that second camp. And so as I saw what he did and I saw the opportunities to work with honestly, the most interesting, stubborn, determined people that I'd ever met before, it was something that was really hard to pass up.

• 4:54Harry Stebbings?

  Absolutely. Well, what can I say? I've hardly, I can't pass it up. So there we go. But but I've never been one to beat about the bush and and talking to you, there's one topic that I just cannot resist diving straight into. And that's the meat of machine learning. So I'd love to start, say, with a topic that that's really intriguing me at the moment. And it's how organizations are thinking about machine learning and incorporating it into their daily processes.

• 5:20Speaker 02

  You know, when we first started the fund, the mandate was and the promise to Bloomberg was that we were going to invest in the future of work. That was going to hit two or three different angles. One of the angles was the future of work was going to be smarter. What that meant at the time was a mix of empowering people, more machine learning. And I have to admit, when we first started the fund, the thought was and the general reaction was that that was further out there. And I've got to credit Siobhan a lot for pushing our thinking around how to use machine learning and the increasing adoption of machine learning. Now, what's interesting, when we first started talking about machine learning three years ago, I think it was still seen as something that only tech companies were interested in. And what's changed over the last three years is that now everyone's interested.

• 6:02Speaker 02

  The CEO of a Fortune 50 company now has the question of what machine learning means is one of the top questions in his or her agenda. The danger, though, is that a lot of the conversation focuses on what I would call either theological questions about the end of the world, which is interesting, but I think oftentimes not relevant, or questions around straightforward labor replacement. And I think in both those cases, those are not the immediate challenge and the immediate opportunity for for most organizations. I think what you're seeing now is the creation of what people initially are calling data science teams. And and no one really knows what it means and no one's exactly sure where to apply it. But they know that there's something there. They know that somehow their organization needs to change.

• 6:47Speaker 02

  That opportunity and that question is is the interesting question out there.

• 6:51Harry Stebbings?

  So I have a couple of questions really that stem from that. And why is it now, in the last probably eighteen months, you probably agree with me in saying, why is it that everyone from kind of CEOs in Detroit to head of Salesforce is interested in machine learning? What caused that rise in public interest?

• 7:07Speaker 02

  Hmm, hmm, that's a great question. You know, one of the things that's been, the slower trend is the fact that data is so much cheaper to store and compute is so much easier to do now. And what that means is that people are able to worry about higher level problems. So that's one, that's one thing. And then the other thing is that in part, thanks to a set of guys who hit out in, hit out in Canada for a little while, diligently working on a set of techniques that continue the work of, you know, folks from forty years ago. We've got cases where we can look at deep learning and say that neural nets actually work and produce really, really impressive results. Those two things, one's slow and one's sort of kind of dramatic because the results were so impressive, meant that people started paying attention again.

• 7:51Speaker 02

  And suddenly you would look and you'd say, wow, the work that Google has been doing for, you know, kind of quietly for the last fifteen years is now yielding incredible amount of fruit and creates all these opportunities. And the other part of it is there's this great investment in, in large data systems. And then the natural question becomes, what do we do with it? You know, we have now these great analytics and we have this ability to figure out exactly how long it takes to, you know, sort of take one widget from one state to another. But what does that mean? And what are the actual applications that are interesting? All that leads to sort of these questions around what are the machine learning opportunities for us now?

• 8:29Harry Stebbings?

  We mentioned that, you, you said about the theological challenges related to kind of the future of work and what will we be doing? And then also the kind of Elon Muskian, will it destroy the world as concerns? You said there were other more immediate challenges. What do you perceive those immediate challenges to be?

• 8:44Speaker 02

  The, the biggest challenge out there right now is that it's not really a good metaphor for thinking about how to apply machine learning. I think that we watch movies that will tell us about falling in love with the robot or will read economists telling us that things are falling apart. But the real opportunity, I think, is Figuring out specific processes and specific places where building good bottles to help us make better decisions, or actually oftentimes to make decisions for us, that's the actual interesting opportunity out there right now. In part because I think machine intelligence is not going to be, at least in the near term, you know, one big intelligence, but as it is for our own brains, many small intelligences and many, many small systems that help us make good decisions or make decisions for us and do things on our behalf.

• 9:27Speaker 02

  And I think that's the identifying that and also figuring out both where the low hanging fruit, but also critically for a lot of corporations, what's actually possible. Figuring that out is a hard challenge in front of us, and I think nobody knows what they're doing yet, right? There are still huge questions around how to think about machine learning, about, you know, what it even means to build these models. That's a challenge in front of many of us.

• 9:49Harry Stebbings?

  I so agree with you with regards to that challenge. And the question then that's posited to me from that is, will we have machine learning in a box that could be integrated into the majority of most corporate systems provided by the likes of IBM? Will it be a kind of box solution or will it be a personalized one-off per company solution, do you think?

• 10:09Speaker 02

  There's always been the dream of making programming so easy that everyone will be a great programmer. And I think similarly, there's a dream that it'll be really easy to build models. And I think what actually happens is when everyone's able to program, you end up with really bad programs. When you have these little black boxes that are not thoughtful about what data they're taking in and how they're using it, you end up with basically bad analysis. My own bet is that you'll see a lot of that happening over the next few years as a number of high-profile projects inside corporations end up not yielding fruit in part because the problem was not defined well enough or there was an assumption that if we just trust this black box without being thoughtful about processes around it, we'll end up with sort of terrible outcomes.

• 10:52Harry Stebbings?

  I'm really intrigued. One portfolio founder of yours, Kieran Schneider at Textio, said the writing is on the wall for enterprise to embrace machine learning. I'd love to hear your thoughts on this and how you think then the business model varies from, say, licensed and SaaS models.

• 11:07Speaker 02

  Oh, I think that's the great question. So the reason why investing in machine learning right now and the reason or the reason why I'm so excited about investing in machine learning right now is because we don't know the answers to these questions and people are groping around to try to figure it out. And I think that there are, let me take a step back and just sort of offer one easy insight that I think all the folks inside the world of machine learning know, but I think is poorly understood outside of machine learning, the machine learning world, which is that building machine learning tools and models is very different than normal software development. You know, sort of normal software development, you kind of know how to QA things and you sort of understand the clear separation between the program and data.

• 11:47Speaker 02

  And what's interesting in machine learning is now you've got the program and you've got data and then you've got a model that you're constantly refining and rebuilding. And knowing whether the model is good enough or what that even means is still something we haven't figured out. And then knowing how we share the model across different organizations or what we do with that is one of those big questions. If you think about the early days of Salesforce or SaaS, one of the things they talked a lot about was this idea of multi-tenancy. That was mind blowing and was one of those examples that they would say that this is one of the reasons why we'll be able to scale unless unlike our other SaaS competitors. And I think there's going to be something, some similar insight and some similar buzzword about the separation of models and the ability to share insights of models between companies

• 12:30Speaker 02

  while at the same time not actually sharing data. And I think exactly what that looks like is entirely unclear. Let me just highlight a couple of the differences between machine learning related SaaS companies and normal SaaS companies. You know, sort of one of them is the fact that the more data you get, the more valuable it is actually for the SaaS company, right? And so all the normal units of measure around how much you should charge are, don't really apply and don't entirely make sense. And so I think all of our best companies are trying to figure this out right now and it's really exciting.

• 13:03Harry Stebbings?

  So, so talk to me about software development then as a framework for thinking about how verticals can transform.

• 13:09Speaker 02

  Okay, so I think we live now in a world where everyone is a knowledge worker. And so everyone from the construction Worker to the chef, the line chef will have a Android phone and will be able to digitize their work. When that happens, when you see that sort of transformation of the workforce, what ends up happening is you need to look to see what are the tools that the best knowledge workers in the world use? And then try to figure out whether there are lessons to be applied from those not those types of knowledge workers. And it turns out that the best knowledge workers in the world are these group of kind of lazy antisocial people who like love to procrastinate and work on meta systems rather than actual applications

• 13:51Speaker 02

  and love to think in theory rather than actually write the code they're supposed to. And those knowledge workers and those people, of course, are software developers. I think if you look in the last forty years, software developers have built the best set of tools for thinking about knowledge work that we've ever seen. So everything from, you know, thinking about debugging in a clear way, or thinking about build systems, or thinking about make files, or thinking about the process for recomposing different ideas. You know, those sets of tools now apply to every single other type of knowledge worker. And when everyone else is a knowledge worker, it applies to every other vertical. And so I will often actually, I've often built this grid which shows every single software development tool.

• 14:31Speaker 02

  And then I'll apply it to some industry and I'll say something like, well, gosh, I wonder if we had bug management for the construction industry. You know, what would it look like if we had version control for legal contracts? And I think that frame of thinking is a helpful frame and has is going to lead to a huge number of startups.

• 14:51Harry Stebbings?

  In in terms of moving to the VC aspect, I I can't resist it, being the VC nerd that I am, I I have to touch on it. So to VC and you personally, I'd love to discuss your approach then to VC firms' investment decision-making process and and how you think about this today.

• 15:08Speaker 02

  You know, I think I think we now know enough about what groups are good and bad at. And we know now that groups are really, I mean, there's been enough theory about this. And we now know that groups are really good at getting new diverse points of view and new bits of information. And groups are actually also really good at doing complex coordinated tasks. Groups are Uniquely bad at making interesting decisions. And so I think that one of the things that we thought about when we started the firm was we need to change the way that we talk about decision making such that we're able to make the interesting decisions and the if I were to look at my old, the old firms that I worked at, the most interesting and the best investment decisions were always very, very controversial. And so how can we create an environment in which

• 15:53Speaker 02

  my partners will come to me with an honest point of view, you know, try to help me make the best decisions that I can make, rather than force me to curry their favor in order to convince them that we should make something. As a result of that, in our firm at least, we've made the decision that anyone can say yes. And of course, when anyone says yes, you own the decision, but it means that when you go to your partners and you go to others, you go with a very different posture.

• 16:17Harry Stebbings?

  How do you change your posture then? Is it a posture of complete confidence, knowing that if you do get the yes, then this is unwaveringly your accountability?

• 16:26Speaker 02

  Well, remember, if I get to yes, then I am accountable. And also critically, when I go to my partner, I'm going to my partner because I respect their, his or her opinion and his or her insight, right? And so I'm going so that I can actually figure out what the best decision is to be made, rather than trying to reposition information or to push something as a great investment, even though I have lots of questions.

• 16:48Harry Stebbings?

  And talking about kind of group thinking there, I'm always very intrigued by herd mentality. And particularly now, when we look at machine learning as a space, there's, there's clear characteristics of herd mentality towards it. So with that respect, does the kind of mass popularization of machine learning, does it concern and irritate you as a machine learning enthusiast well before the herd arrived?

• 17:09Speaker 02

  You know, I'm always glad to have more company, right? You're right that certainly this is an area that lots of people care about and talk about. You know, to be honest, like wanting to be early, one of the reasons why I wanted to be part of a seed fund was so that we can invest, you know, two to four years before everyone thought something was interesting. So I, I for one, am very, very glad to have everyone else join the party. And I think that part of that means that we'll all get smarter about it. The truth though is that there's still Remains a relatively small set of folks who've thought about this type of business model, building these types of applications and we're lucky to know a lot of them.

• 17:45Harry Stebbings?

  I I do wanna touch finally before we dive into a quick fire on on an aspect that your partner Roy had mentioned to me. And it's the relationship that you have between your work as a V C and then your role as a Christian and and with your faith. So I'd love to hear how they interplay and then work together.

• 18:03Speaker 02

  Sure, you know, I think that, you know, so I I do take my faith and my Christianity fairly seriously. And so it was important for me to do work that I thought reflected that. The thing that I love about entrepreneurship is that it is the act of creation. And I think that the active creation, that is to say, making something from nothing or something from very, very little, is as close as we'll really get to doing the work of God. You know, one way to think about investing is that you're doing some straightforward money transfer and you've got an exchange of skills. The other way to think about it is to think about it as this is, this is a tangible example of by putting my faith in someone and saying, yes, I believe that you can create something from nothing.

• 18:47Speaker 02

  And I think that that's as close as we're gonna get to doing that act of creation and doing that work of God. And I find that very congruent and very exciting.

• 18:55Harry Stebbings?

  I I'd love to dive into a quick fire round there with you now. So I say a short statement and then you give me your immediate thoughts. How does that sound?

• 19:02Speaker 02

  Great.

• 19:02Harry Stebbings?

  So let's start with your favorite book and why.

• 19:05Speaker 02

  My favorite book, or at least the book that I give out the most often, is The Man Who Lied To His Laptop, which was by a Stanford professor named Cliff Nass. But he wrote about how we as people treat machines as personalities. He talked about a whole set of tests that he did and a whole set of studies that he did in order to show that, right? And so his most famous example is with Clippy. It's the, it's that horrible little thing in Microsoft Office.

• 19:32Harry Stebbings?

  Yeah, yeah, yeah, no, I do know the one, the paperclip.

• 19:34Speaker 02

  Yes, the paperclip. It was incredibly unpopular. Cliff ended up doing a few studies near the end of the life of it in which he changed some error messages. And he changed the error message from saying, oh, I'm sorry that I can't find the result you're looking for to. Something along the lines of, oh, you know, that stupid developer, I can't believe he couldn't get a good result for you. That act of blaming someone else or scapegoating made Clifly incredibly popular, right? And that's because we as people have a whole set of common patterns for how we interact with the world. Those common patterns are well understood and predictable and they could actually be simulated very, very well by machines. You know, I think Cliff would very much be interested in the world that we live in right now as

• 20:14Speaker 02

  we are now thinking more and more about how do we anthropomorphize machines and how do we create deeper relationships with them.

• 20:20Harry Stebbings?

  This is a meta question for you now. What do you think the optimal relationship is between man and machine? You know, Musk is terrified and then others are terrified about kind of too much integration and too much love.

• 20:31Speaker 02

  So I don't, I think that that's the wrong way to think about it because it's not, you know, machine is just a metaphor we use right now for technology. The truth is that we as people are constantly being transformed by technology and, you know, whether that's agriculture and eating, you know, eating wheat and having that affect our tummy to deciding to wear clothes because it keeps us warm, right? And so I think that that, you know, sort of there's going to be inevitable progression of how different technologies end up influencing us and, you know, in some ways you and I are incredibly augmented by a whole set of things, you know, sort of everything from living inside buildings to like using light. To be honest, I don't know that AI is going to be necessarily that different. Which is say, all those other technologies were incredibly transformative and this will be another example.

• 21:12Speaker 02

  And people who come from a thousand years ago will look at us and sort of marvel and be confused, but we will still feel like we're human.

• 21:19Harry Stebbings?

  Where are the greenfield opportunities in machine learning for you?

• 21:22Speaker 02

  Oh, there's so many, there's so many. I think that if you were to break down every single process inside an organization and you'd say which are the ones that are easiest to digitize? That exercise is sort of the exercise that we go through right now. And I think that everything from redoing logistics over to HR, you know, sort of rethinking the way that contracts are being written, all those are huge, huge opportunities. And then there are a whole set of opportunities around, rather than selling products into specific companies and for one specific function, there are a whole set of companies that can be remade because that one core function will be so valuable. And so we look a lot at that, that part of the world I'm certainly quite excited about.

• 22:03Harry Stebbings?

  What's your favourite blog or newsletter? What gets you really excited when it comes in?

• 22:06Speaker 02

  So there are three. There are two new AI-related newsletters that I just love. One is by Rob May, M A Y, and the other one is by Jack Clark. Those are spectacular. And then I read Tyler Cowen, The Economist, quite a bit, and I think those are probably the things I look forward to the most every week.

• 22:23Harry Stebbings?

  What do you think the biggest misconception is about machine learning?

• 22:26Speaker 02

  That it's magic. One way to think about machine learning is that it is basically really, really smart statistics that that end that end up constantly improving. And I think that as a frame for thinking about machine learning, rather than machine learning as magic, is is is much more useful.

• 22:42Harry Stebbings?

  And then final question, and that's most recent investment for you and why you said yes.

• 22:47Speaker 02

  The most recent public investment is in a company called Netlify. In that case, there are a set of designers and programmers who realised that the way that we make websites and deploy them right now are insecure and too slow. And so they took that insight, ended up creating a variation of of a movement that had started a number of years ago called static web hosting, took that and sort of reimagined it, made it easier to deploy. And now they're sort of growing quite quickly among a core set of like smart and fugly-looking designers and web developers. My colleague Morgan actually first pointed out the reemergence of this trend about a year ago. And then I had heard about Netlify from another friend. You know, sort of as I met the guys,

• 23:31Speaker 02

  you could see in their eyes that, one, they had a core insight into technical architecture and how that would change a business. Two, they were going to work at it like crazy and they were not going to be stopped. And so I'm very glad and lucky to be along for the ride with them.

• 23:44Harry Stebbings?

  Well, James, it was such a pleasure to have you on the show today. As I said, I heard such great things from Roy, Siobhan and David. So thank you so much for joining me today.

• 23:51Speaker 02

  Thanks so much, Harry. It was my pleasure.

• 23:55Harry Stebbings?

  I do want to make a special point state in saying what an incredible chap James was, not only a machine learning powerhouse, but also the most kind and genuine chap to have on the show and really appreciate him taking the time out today. And if you love the show with James today and want to stay in the world of the 20 minute VC, then you can add me on Snapchat at H Stebbings with two Bs, or you can sign up for the newsletter on the 20minutevc.com. That way you never have to miss an episode or an update from us. However, before we leave you today, do not forget to check out Design Crowd, the online marketplace for all things design, from custom graphics to logo to web design. Hey, they even work with big brands like HTC and Virgin Active, who use them for t-shirts and billboard design. So how does it work though? Simply you launch a project, designers begin submitting quality designs for you to review.

• 24:40Harry Stebbings?

  You quickly then generate a large gallery of designs that fit your needs. Once you select your favourite designs, you will be sent all the files you require and all of this can happen in just three days. Much better and more reasonable than an agency and perfect for small businesses and entrepreneurs who need fast response times and pretty reasonable pricing. So head over to designcrowd.com/vc and enter the promo code VC100 and Design Crowd will give you an incredible hundred dollars off. Likewise, if you're a founder or investor, Angel Loop is a must. It's the leading post funding management platform for private market investors and their portfolio companies. They help investors manage and track their portfolio companies on the cloud while providing them with access to their investments' performance data. Angel Loop helps founders on the other hand with tracking their performance,

• 25:26Harry Stebbings?

  managing their cap table and keeping their investors in the loop. Investors get access for free while their portfolio companies pay only forty nine dollars per month. Use or share the promo code 20minvc to get your portfolio companies online with a two month trial. As always, I so appreciate all the support and for you to for tuning in today. Please do email me [email protected] with any questions, queries or concerns. I always love to hear your thoughts and I so appreciate you taking the time today and look very forward to bringing you Friday's episode.