165. AV, Mobility, and the Disruption of Big Auto (Evangelos Simoudis)

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Evangelos Simoudis of Synapse Partners joins Nick to discuss AV, Mobility, and the Disruption of Big Auto. In this episode, we cover:

  • Backstory / Path to becoming an investor
  • What is the investment focus/philosophy of your firm?
  • What are the key drivers of automotive disruption?
  • Can you talk about the role of big data and machine intelligence in AV?
  • Can you give us an overview of the framework you use for investing in mobility and talk about the new investment areas in the category
  • What business models have and will emerge as we adopt autonomous vehicles for on-demand mobility services?
  • What needs to happen for these business models to be implemented and what sort of timeline are we looking at?
  • Talk about some of the notable companies funded in the space and potential exits in the short-, medium-, and long-term?
  • What do you think will happen to the large, incumbent automakers?
  • Where do you find interesting big data and AI startups working on mobility-related problems outside Silicon Valley, Israel, and China?
  • How do you identify areas to invest, create value for your portcos, and help drive exits?
  • Advice for founders working on AV and mobility tech?

 

Guest Links:

 
 

Quick Takeaways:

  1. Synapse’s focus includes big data applications using AI in industries such as telco, finance, and transportation.
  2. Synapse invests at Post Seed and Series A.
  3. Synapse leverages relationships with key players and thought leaders. This surfaces the problems that can be solved with big data solutions.
  4. Analytics play a pivotal role in tracking potential startups.  Synapse maintains a detailed database and scoring system to monitor specific companies and categories to invest in.
  5. Key drivers in automotive disruption are consumer mobility with increased urbanization, robotic assistance for an aging population, and the sharing economy of the new generation.
  6. Big Data and AI are enabling autonomy.  They map the path for the vehicle and also personalize the experience.
  7. As investing in automated vehicles is capital intensive, Synapse focuses on enabling autonomy—finding technologies that will help corporations achieve their automation goals.
  8. There are a variety of business models that may be adopted with upcoming automotive services, including transactions, subscriptions, advertising, data, and fleet management.
  9. Evangelos believes that monetization will be repeatable by 2022 or 2023. The most important aspect of the business model is repeatability.
  10. While great progress is being made on the tech side of automation, many concerns remain unaddressed: privacy, infrastructure, liability, and cybersecurity. Without considering these factors, it is difficult to assess monetization.
  11. The inability to predict the movement of vehicles and individuals in cities may adversely impact exits.
  12. To reach the startups in other countries, Synapse finds local partners who share a similar investing approach. They have found that the startups with the most success come from areas with a large automotive presence such as Germany, France, and the UK.
  13. Many founders lack an understanding of how the automotive industry works. The phrase “Disrupting the automotive industry” should not be used.  Instead focus on your startup’s key competitive advantage.

 

Transcribed with AI:

0:03
welcome to the podcast about investing in startups, where existing investors can learn how to get the best deal possible. And those that have never before invested in startups can learn the keys to success from the venture experts. Your host is Nick Moran and this is the full ratchet

0:22
Welcome back to TFR Today we feature autonomous vehicle expert Evangelos Simoudis. The VC who wrote the book on mobility joins us to discuss key drivers Big Data, business models exits, large auto geographic centers of influence and advice for founders. Here’s the interview with synapse partners MD Evangelos samonas.

0:48
W e and PhD Evangelos salutis joins us today on the program. Evangelos is co founder and managing director of synapse partners, a venture firm that invests in early stage startups developing big data applications. He also advises global corporations on startup driven innovation and information technology. Previously Evangelia served as Managing Director at Trident capital and Apex partners in 2012. In 2014, he was named Top investor in online advertising. And many of us know him as author of the popular book, The Big Data opportunity in our driverless future Evangelos Welcome to the program. Very good to be here. Thank you. You’re in Greece at the moment, but you guys are based in Menlo Park. Is that right? I am visiting my parents here in Greece. I left Greece about 40 years ago, but every year I come to spend some time with them. Good for you. How’s the weather out there? Hey, it’s actually very interesting.

1:48
Actually, so but no complaints. Good deal. Good deal. Well, it’s a pleasure to have you. I appreciate you making the time. So maybe we’ll just start off. Can you tell us a little bit about sort of your path to venture? Absolutely. I left this as in Greece about 40 years ago to study at Caltech. Eventually, I got a PhD in computer science in the area of machine learning, and very large databases. That was in the late 80s, when, during the previous AI spring, after working in the 80s few years as a software engineer, Digital Equipment Corporation, I came to Silicon Valley in 1990. Between 90 and 2000, I did two startups, both in the area of data analytics and machine learning. And after selling the first and before starting the second, I was general manager at IBM running their business intelligence solutions division, global responsibility. And after selling my second startup, I was invited to become a partner at Apex partners, which a large private equity firm at that time, they were doing also some venture, I got hooked into venture and eventually moved to Trident capital. But I came to be a venture investor said after spending years as an engineer, and obviously, as an entrepreneur and corporate executive, he tells a bit more about the startups that you founded.

3:24
Yeah, the first one was called recon. And it was in the area of financial analytics. We were that was said 99. Around late 90, it was we were working on trading models and early versions of derivatives. It was a very interesting time because both the technology was was new. But also the venture capital was not as developed at that time as it was later on in that decade with the first internet way. So raising money and building the company was an interesting experience with a lot of important lessons, which helped me in the in my second company, which was actually a much bigger success than the first was that second company. The second company was called customer analytics. And it was a company that was we were taking a lot of CRM data from a variety of systems, primarily, we were working with financial services, institutions, retail banks and other financial services institutions. And we were developing a better understanding of the consumers. So by the time we sold the company, we had over 100 corporate clients. And as I said, we were acquired by a public company called exchange applications, which doesn’t exist anymore, but at that time was one of the high flyers in the CRM space. Got it? So can you tell us a little bit about the investment focus and the philosophy at synapse? So it synapse we’re doing

5:00
have a couple of things which I tried to combine from my previous 15 years as a venture investor, we’ve decided to focus exclusively on companies that develop big data applications. So for us, these are applications that combine big data with AI based methods for exploiting that data. They can be horizontal applications, or they can be vertical. And in terms of vertical, we focus on three industries on the automotive, and by extension, transportation industries, but also telco and financial services. The second aspect of our approach is that we form this very close relations with corporations in these three industries that I mentioned. And in identifying these corporations, we try to not only focus on on companies that are important players in their industry, but also companies that are thought leaders, and they look over the horizon. And we spent a lot of time with these companies a lot more than I’ve ever spent is a VC in my prior years. And through these interactions, we identify how they’re strategic problems that can take big data solutions. And where we put our own expertise and experience is in determining which of these problems really will have general appeal, and could be worthy of investments to startups. Karen, can you give us a short example or a little case study? Yeah, so if you look at what we are doing, actually give you a couple of examples. So one of the companies that we invested very shortly after we started the firm, with a company called X 15 software. So we came at x 15, by talking to as turned out three of our telco corporate partners. And we identified that they had a problem with analyzing very large data sets with security alerts, but doing so in an economic manner, because they were seeing their data sizes increased tremendously. And the business models that some of their existing vendors were using would not allow them to examine as much data as they felt it was necessary to so we looked at a number of startups that were working in this space that time, and we settled on, we selected x 15. And as part of the work that we do, we maintain significant databases with data about the soul, the companies that that we track. In fact, our database today contains about 5500 startup that we track, great detail. And then we score constantly score in terms of their ability to perform and other quantities that we believe are important. So we ended up investing in x 15. Shortly after we invested, we introduced them to these corporate partners that he told us about the problem, they worked with a couple of these companies, these corporations. And ultimately, a couple of years later, as I said earlier this year, in fact, they were acquired by FireEye was very good exit for us, we were the only venture investor in the company. And so we created a lot of value. Our corporate partners also found a lot of value from the company itself. So that’s one example that we brought like full circle, if you will, around our model. The second work is what we’re doing around autonomous mobility. So one of our ongoing thesis is around in how big data and AI enable autonomy we’ve made for investments so far in that space. And again, identifying your companies like metta Moto, in the area of simulation, autonomous vehicles simulation are a novel in the area of data fusion and analytics for the sensor data, and a few others. But again, this both the areas to invest in as well as ultimately the the startups that we have elected to invest in have come through this interactions and the analytics that we do from the databases that we maintain. Got it? Well, I do want to talk more about mobility and driverless vehicles. You and I have been discussing this for a few weeks now. And I’d love to go deep on on that topic today. And I think a good place to start kind of how you laid out in the book is to talk about some of the key drivers of automotive disruption. Can you highlight some of those major drivers and then maybe we’ll talk about how that applies for startups and and opportunities for tech disruption. Yeah, so first of all, we have to look at the drivers

10:00
that deal with consumer mobility, which is what the book is, has been focusing on, because of all the attention that is getting in these drivers are different from the mobility of transportation of goods. So let’s leave the transportation of goods aside for for the time being. But in terms of the consumer mobility, we have urbanization being a big driver. So we have a great movement of populations into cities, we have increasing densities in urban areas, which facilitate this shared mobility we have the another factor is the in some geographic areas is the ageing of the population, which will require more robotics driven help, including autonomous mobility. Actually, the sharing economy is another factor that, again, is impacting different segments of the population, particularly the younger segments. And there are a few more, but these tend to be chi major trends and major factors that impact the move to autonomy and the interesting autonomy. Got it? Can we talk about sort of the role of big data and machine intelligence, autonomous vehicles and mobility? Yeah, so big data, I think, combined with with AI play are key ingredients. And again, to date, the bulk of the interest by all of both asset both investor side, the intrapreneur side, and even the large corporations that have gotten into the space and increasingly more and more on getting into the space, deal with enabling autonomy. So making a vehicle go by itself from point A to point B. And in doing that, there’s a lot of data and AI that is being used from the mapping of the areas where autonomous vehicles operate, to the actual operation of the vehicle, the planning, the understanding of the environment, in the control of the various components of the vehicle. But I think there is even a much bigger role of big data both for the personalization of the cabin itself, right. So we have been thinking about what what will what will individuals do once they are in this vehicle, and they have to travel medium to long term distances, I’m not talking about what happens when you drive for a mile or two, let’s say within Manhattan, but what happens if you are in Los Angeles and you have to take a 20 mile commute, right? So personalization of the cabin is a very big data and AI can play a very big role. And we’re already working with a couple of our corporate partners on helping them understand that and determine what kind of startup investments to make in versus what kind of internal developments to pursue. There’s also a big role in that big data and AI can play in developing these personalized transportation experiences. So today, we’ve been thinking of autonomous mobility is someone in a vehicle getting into the vehicle from point A and being dropped off at point beat. But imagine a situation where from the point that you are ready to exit your your home, let’s say in the morning, until the time that you return home, and all of the journey that you’re making on a daily basis, and the various modalities of transportation that you have to utilize in the process are all planned for you, right in order to maximize your convenience, to minimize the amount of idle time to take into account the weather conditions, the traffic conditions, your comfort in all of that. And that’s where I think big data will play with AI will play a critical role we’re already seeing in places like San Francisco, the use of trains combined with ride hailing combined with bikes and more recently, scooters, all of that can start coming together in a seamless experience that is tailored to each individual. And in order to be able to do that we will need to be going through a lot of data and analyzing it properly and combining it properly in order to give the right personalized experience. Great. And you’ve mentioned a few of the different types of transport. You’ve mentioned a few of the different technologies at play here. It seems like everyone I talked to about mobility and autonomous vehicles kind of has a different frame or a different lens by which they kind of look at this opportunity. Can you give us an overview of your framework, the one that you use for investing in mobility and maybe also talk about some of the new investment and

15:00
Maria’s in the category. Yeah, so we have not invested in vehicles, and there has been a lot of investment, we feel that he has gone into both vehicles into particular modes of transportation, as well as complete autonomous vehicle stacks. In other words, the AI capability, the soup to nuts AI capability that enables a vehicle to become autonomous, right? We have we have shied away from it, frankly, because these tend to be capital intensive spaces that attract big players. In fact, some of these big players are corporate investors and corporate partners of ours. So through these interactions we have decided not to focus on that instead is our initial thesis around autonomy has been enabling the autonomy so that in a sense, providing the picks and shovels that help other corporations achieve their autonomous vehicle goals. So this is why we invested in divergent 3d, which is providing a manufacturing system for next generation vehicles using big data and AI and how additive manufacturing methods we have invested in meta moto as I said before, which is providing a cloud based simulation system for simulating autonomous vehicles and their their performance. We have invested in understand.ai, a German company that is using machine learning to automatically annotate all the images video LIDAR radar that are being used in order to train the AI that these autonomous vehicles utilize. And as I said before, we’ve invested in renewable which provides essentially middleware for fusing data from the hundreds of sensors that these vehicles have and analyzing it, and then feeding that data to the AI so that it can make the right decision. So again, this is the picks and shovels approach. More recently, we’ve started moving to our second thesis, which is around monetizing, enabling the monetization of that autonomy. And the first investment we’ve made in that space is a company called Safe graph, which analyzes very large quantities of data to understand the daily journeys of consumers. Now, the based on the example that I mentioned a few minutes ago, so that the various companies that are going to offer mobility services using autonomous vehicles, they will be able to use the decisions and the analytics that safe graph is using is developing in order to set provide these customized transportation solutions. So going forward, we’re looking at more investments in this monetizing autonomy thesis. So with safe graph, how will they monetize if they have this coordinated, you know, transport solution, and and where’s the monetization piece. So the monetization piece is essentially doing what Nielsen is doing for media. So it’s giving you insight about what consumers do. So the companies that are providing this type of transportation services are buying insights, if I can call it that from safe craft in order to be able to provide better service to their own consumers very much like media companies are buying insights from Nielsen in order to be able to provide better programming on TV and other media to their audience. I see. So while we’re talking about business models, can you talk about some of these different business models that have emerged and the ones that will be adopted with autonomous vehicles and on demand mobility services? Yeah. So, I will say that today, we do not have a very clear picture and we meaning the the corporations, companies and corporations that are things that are working on autonomous transportation, they do not have a clear idea about what will work and what will not work, we have however, some initial hypothesis, the first model that is very likely to be adopted initially will be transaction based this will be an extension of what we are doing today or what ride hailing companies are doing today. So you want to go from point A to point B and there is a an algorithm that is minutes based and distance based that prices that transactions then I think very quickly we will also move to subscription based model so maybe for for medium base distances, maybe

20:00
commuting based transportation, you could think these companies are adopting various forms of subscription models. And then I also think that over time, I do not know how quickly but I we’ve seen some startups already thinking about that we will move to advertising based models. And I see those again being in in shorter distances may be relating to entertainment, you know, so in other words, I’m in a downtown area, and there is transportation that is being offered based from the venue’s not based on Bay from the venues that offer entertainment around a specific area, maybe around the convention or around the downtown area, I can also see advertising driven models being extended to last mile delivery, right, where maybe a retailer or grocer can say if you only buy from us, we can deliver things for free. So again, that type of combination. So this will be models around the robo taxi, the ride hailing part of the mobility services part of autonomous transportation, then I think we’ll see a family of business models around the big data monetization. So whether it is the entertainment that is being offered, while we are transported or other types of digital information, whether it is the various convenience services that are being utilized while being transported, whether it is whether or something like that those can be monetized. Also, whether you are alone in the cabin, or you’re sharing the cabin, and what kind of conveniences you want in the cabin while you’re being transported. And then I think we will have a third family of models will be around fleet management. So the companies that offer add, manage and maintain these fleets, Robo taxi fleets are going to have their own models, which can be based on the size of the fleet, they can be based on the value of the goods being transported, or the maybe distance based. So again, I think we’ll see variations there. But the point is that, in my opinion, we’ll see three broad families of models, one for the robo taxis, one for the data itself, and one for this fleet management and maintenance. Seems like there’s a lot that needs to happen in to evolve in order for some of these business models to really take hold. What sort of timeline Do you think we’re looking at for each of those? Nick? Hi, I’ve had a number of conversations about this. And in fact, even in anticipation of our conversation today, I have interacted with a couple of individuals who I respect, I tend to be more conservative on my timeline. And I think that we will start to see enduring in repeatable monetization, maybe 2020 to 23. I know that some corporations, whether it is way more whether it is GM, Ford, they claim that they will start charging for their rights earlier. And I do not doubt that they will do that. But again, my thinking about a business model is when I have repeatable operation and something I’m doing as opposed to the experimentation that may need to go through during a pilot phase. I also believe that while we are making great progress on the technology, front of autonomy, and we will be able to address many of the remaining problems over the next two, three years. There are several other issues that remain to be addressed. Whether it is liability, privacy, cybersecurity, transportation infrastructure, in those issues, in my opinion, given what I know, and given the discussions that I’ve been having with players around the world, I think will take longer to resolve than just the pure technology problems. So when I talk about this business models starting to take off around 2020 to 23. My statement is not only based on the technology part, but also on all of these other components that will need to be addressed. Got it? Well, I’m sure that that informs sort of your investment approach Evangelos I’d like to hear your thoughts on technologies onboard that are within sort of the autonomous vehicle environment and then and those things that are outside of the vehicle but necessary for AV to be a standard. How do you think about each of those in do you invest outside of the vehicle as well or not?

24:57
Okay, so we will definitely

25:00
invest outside the vehicle. Now the question would be, again, what are the investments that can be done by a startup and ultimately, your successful company or a large corporation and what needs to be done by governments, whether it is city governments or country governments. And this is something that we grapple with so, so today within the vehicle, with regards to autonomy, per se, the problem that remains is vexing. One is to be able to look ahead or to predict the movement of vehicles and individuals, pedestrians and other entities that can impact the planning decisions of the vehicle itself. And today, while we’re getting better, that we still do not have a long enough horizon to allow us to deal with the complex environments of big cities. This is why when I think of how autonomy will progress, how more will be deployed, I think we’ll go from shuttles, you know, whether it is Campbell shuttles to cities shuttles, and then move to maybe trucks that are driving in highways, maybe dedicated lanes or some constraints, and then ultimately to cities of arbitrary complexity. So in other words, we will be moving from more constrained to less constrained environments. So from our show, as investors, we pay a lot of attention to these developments, because they will definitely impact how long we will expect to hold this the investments that we make, I mean, in other words, I don’t believe that we will have in many cases quick exits. And so this is within you know, said within the within the vehicle, we’re also looking at the various sensors and data that can be collected now within the cabin, show that we can provide these personalized experiences. And finally, we are looking at what other data will need to be collected in order to make decisions around fleet management, because again, one of our beliefs in this space is that whether it is on the on the consumer transportation or whether it is on the on logistics, the autonomous mobility will be adopted by fleets, and less so by individuals. So not that they will not be personally owned vehicles with autonomous features, but we think that the broader application of these technologies will be done by fleets. So fleet management, we see is a very critical various aspects of fleet management, she is very critical component of this whole enterprise. And along with fleet management don’t only mean what’s hap how the fleet moves and is able to execute on minute by minute basis, which means that’s what brings the revenue to the company that operates the fleet, but also when the fleet needs to be maintained, how the fleet in order to maximize the uptime, how the fleet needs to behave in order to maximize the passenger revenue, miles as opposed to just miles, how to minimize the cost of maintenance. So there are a number of very important decisions, thankfully, are all data and AI driven, that are now going to become key in this area as you’re starting to deploy fleets of these vehicles as opposed to just worrying about making a vehicle capable of going from point A to point B autonomously.

28:50
Right. You mentioned the exits Can you talk about some of the notable companies funded in this space and potential exits over the short medium and long term?

29:01
I think in the in the short term, we will see two types of exits we will see big exits very much like crews when acquired by GM or NuTonomy are being acquired by aptiv of companies that have core technology which the acquirers feel they need to access in order to create their solution or to complete their stack. So, you have various models of how these companies go about creating their offering. In the same time I think we will also have some tacking acquisitions of smaller scale exits of companies that you know is it happens in every other field they got initially funded, but they have not been able to scale properly. So if you look at the acquisition of oral by now auto you know it’s again this type of exit medium term, we will see

30:00
exits of companies that are either getting quick scale and have the ability to go public or companies being acquired because they have a successful solution. And they’re acquire may have tried to do something similar, but they fail. So now the acquirer is moving to make an acquisition in order to have a successful company in order to complete their solution. And, but I think those will take a few years until, in other words, these startups are able to demonstrate their mettle. And then ultimately, longer term, we will have exits of companies that are successful and scale and they can they have the ability to go public under any market condition. And maybe even after they go public. They even get acquired, so their venture investors, they can do the so called double dipping. But that’s how I see the in the space of autonomous mobility, the three horizon of exits,

31:02
what do you think happens to the large incumbent automakers?

31:07
You know, I wrote a piece on my blog a few weeks ago, and I stated that I believe we’re going to have five categories of incumbents, the potential two incumbents, I think we will have the ones who are who have decided to develop autonomous vehicles. And then the question is whether they will stop it just developing the vehicles are also offering mobility services, or stop only the offering of the vehicles. Again, if we assume a world of shared mobility, which again, depends on what kind of Horizon you want to take, and in the book, I take a 40 year or so Verizon, I do not believe that the incumbent ecosystem, as we know it today, especially the non Chinese automakers, I don’t think that we will be able to have the number of incumbents that we have today, the market, in other words, will not be able to sustain it. But a lot of things have happened over a 40 year period. So it’s not clear.

32:15
Yet, do you find that the best opportunities in this this area are in Silicon Valley and Israel and China? Or do you find sort of interesting Big Data and AI startups working on mobility related problems outside of those areas?

32:30
Well, I would say that, first of all, we are seeing a great selection of startups working in these areas of AI, let’s stay the top level first and big data around the world. So many European kind of like from Scandinavia to Greece, if you will. We see in every country in between, we see companies developing interesting technologies. The question in all of these cases is whether you want to how far away from your home base you want to go, in order to invest in such companies, the approach that we have taken because again, we’ve made investments abroad is if we find local partners that we trust and have the same approach to governance that we do, then we will definitely partner and make such investments. Now as it pertains to mobility, I think that we are seeing a lot of ideas from around the world, again, from outside the countries that you mentioned. But we do believe that these companies can benefit greatly if there is certain parts of the automotive industry around them. So I think that German companies, for example, working on various aspects of autonomous mobility benefit greatly. Same thing with French companies. UK mean, we’re looking right now at the investment in the UK. And we’ve been quite impressed not only with the company that we are considering, but also with other startup that we’ve seen in the process. So no, I think that this type of innovation, autonomous mobility, innovation is taking the world by storm and pushing some very interesting ideas from around the world. Yeah, and can you talk more about how you at synapse identify the areas to invest in, you know, create value for your port codes and, and also how you help them drive exits.

34:34
So the fact that we we focus in one in this area of Big Data exclusively on big data applications, either horizontal or vertical, that only target the enterprise allows us to greatly constrain the universe that we are considering the second aspect, and we know the technology very, very well. I mean, because of our backgrounds and the years that we’ve been investing

35:00
In this space, even before starting synapse, the work that we’re doing with our corporate partners not only helps us in identify areas to invest and big strategic problems to try to solve, but also once we make the investment, we have this, the portfolio companies have a great initial target opportunity with his partner compound corporations. And by the way, we absolutely lean on our corporate partners during the pre investment diligence process together perspective to maybe sometimes do a POC to see how the startup performs. So so it’s a great, great advantage that we believe we have interesting, but the way that we identify targets also is, you know, target investments is by using this database that I mentioned before that we have that we constantly update and score. So again, the investment that we made in understand Dota AI, we identify the space through this type of interactions that I mentioned, but we identify the company, which is based in Karlsruhe, Germany, Southern Germany, through our database, and we started working or talking to them in June of 2017. And we made the investment in December of 2017. Similarly, when we were looking, I mean, it’s an earlier story, but the decision to invest in renewable came under that type of analysis and set of interactions. And once we make the investment, in addition to the connections that we make to these corporate partners, sometimes by the way, these corporate partners end up either co investing with us or coming to invest in it a later round, I think because of the subject matter expertise that that we have, as a firm, we get very involved with the, with how the the offering of each of these portfolio companies evolves. And you can think of it is both of us rolling up our sleeves. But also thing our, our opinion and insight is sought after by our portfolio companies. And so we think there is value that’s being created not only by through this corporate introductions, but also through the work that we do as a firm.

37:24
And evangelists remind me, what stage do you like to enter? And what just kind of your standard check says?

37:32
Yeah, so so we invest in what is now being called post seed and series say, again, it depends very much how what has been the startups funding history. And there is a very simple reason for that. It’s in these corporations we work with who like the portfolio companies to have a little bit of, quote unquote, meat around the bone. So that means that they maybe there is a first version of the product, maybe there are a few POCs, or some maybe even some paying customers before we come in. And our initial check size tends to be a few 100,000, depending on the on the company and the opportunity that we put, we perceive. And then we tend to again, for the reasons that I mentioned earlier, we tend to continue investing in later rounds as well. And what advice would you have for founders that are working on AV and or mobility tech? So I think for me, I’ll tell you that something that I learned through the readers of the book, okay, it is very surprising to me how many founders start companies in this space, and they do not really understand the dynamics of the automotive industry, which is, you know, over a trillion dollar a year industry, excluding the energy side of the automotive industry. So my advice would be that they really need to understand how the industry works today, because we hear a lot of statements and we have companies coming to present to us and you know, you have this statement, such as I’m going to disrupt the automotive industry. And many times, once you start digging a little bit into the presentation, you realize that these are vacuous statements that these people do not really understand the ins and outs of the industry in order to be able to make that type of statement in an authoritative manner. So I think that’s that’s important. The other thing is, this happens in every other space is you really need to understand how crowded the space is subspace will be I mean, right now, I mean, I’ll tell you just in our database, we have over 1000 startups working in auto tech that we track, okay. We have invested five, right, but we track about 1000 and every

40:00
A single sub space is, you know, whether it is mapping, whether it is, you know, planning. They are there’s a lot of competition, right. So I think the companies that are that either want to enter this next generation mobility space, or even ones that are already in this in this area, and they’re trying to determine what will be their next steps, they really need to understand what is their competitive advantage and whether competition does and also who is their their competition, because it is a lot more crowded than I think many founders that at least we meet with, seem to believe. Right, right. Evangelist, if we could cover any topic here on the program, what topics do you think should be addressed? And who would you like to hear speak about it? I actually believe that it will be great to talk about how we avoid the AI winter airy peak rather of the AI winter that we had in 9192. Again, I got into AI actually 1982 and lived through the previous AI spring that was followed by a very long winter, we had at that time, we had similar scale excitement about AI as we have now obviously, we had fewer people working on it from fewer countries with less venture investment going into it. And ultimately, we had said certain failures that led to a very long winter, I think given the amount of money that is going into artificial intelligence from all corners of the world, from corporations and individuals and venture funds and private equity funds and governments and all that. I think we if we can have some more discussions about that, and how to avoid another debacle and have more sustained success. That’d be great. And finally, evangelists what investor has inspired and influenced the most and why, you know, when I got into the venture industry,

42:09
I was I consider myself very lucky to have been mentored by Alan Patricof was one of the founders of APEX partners. And Alan actually taught me both how to look at deals and not get overly excited, even though I believe that sometimes I do get overly excited. But also how to persevere through an essential, thick and thin if you will. And I see Halon obviously retire from APACS, and now he he built yet another success with great cross partners. Yeah, he’s been able to attract some really talented people around him and continues to build a great company. So to me, he’s actually a great inspiration. And as I said, I consider myself lucky to have been mentored by him as I was making my first steps as a as a venture investor. Excellent. And then what’s the best way for listeners to connect with you.

43:12
So I’m very good with email and by email is Ec Moody’s synapse partner.co, not Nm at the end, I also have my blog, it’s corporate innovation.co. And I get messages there. And finally, LinkedIn, I get quite a bit of traffic there, and I’m trying to stay on top of it. Great, well, evangelists who was great to read the book and even more of a pleasure to get a chance to connect with you here and get your thoughts on the program. Thanks so much for making the time and I look forward to connecting again soon. I really appreciate the opportunity. Thank you.

43:55
All right, that’ll wrap up today’s interview. If you enjoyed the episode or a previous one, let the guests know about it. Share your thoughts on social or shoot him an email. Let them know what particularly resonated with you. I can’t tell you how much I appreciate that some of the smartest folks in venture are willing to take the time and share their insights with us. If you feel the same, a compliment goes a long way. Okay, that’s a wrap for today. Until next time, remember to over prepare, choose carefully and invest confidently thanks so much for listening