Investing in Emerging Technologies - David Mandel, Managing Partner, Emerging Ventures
David Mandel is the founder and Managing Partner of Emerging Ventures where they invest in startups operating in emerging technologies like AI, ML, NLP, Robotics, etc. Previously David founded, operated, & exited 4 startups in the finance space. He has since invested in over 500 startups as an angel or fund manager.
David's story & how he started investing?
Will AI take our jobs?
Will there be new types of jobs for humans?
What do you look for in a company before making the investment decision?
Perspective on the slowdown
I'm David Mandel, currently Managing Partner at Emerging Ventures Capital. Long story short, back from in the late 80s and early 90s and university I was in applied Math and Computer Science. Wanted to work in AI and it just wasn't the right time. Dropped out of the doctorate program, got my Masters, but never stayed for the doctorate and went into business and spent the next 25, 28 years building 4 separate companies in insurance and finance, most of them underwriting type businesses, risk bearing entities in finance and insurance. And fast forward and around 2011, 2012, so many years later, I started to see as an insurance executive and a finance executive, vendors coming to us with what seemed like some true artificial intelligence kind of applications, different kind of vendors than the traditional. I was really intrigued. I started to get very intrigued by what was now possible.
It's like, wow, what I wanted to be doing 25 years earlier now seems to be finally possible. That got me very excited to go back to my roots of technology and explore what's out there. And I started angel investing. I started attending conferences about AI and Natural Language Processing, Machine Learning, started talking a lot with these, what you would now call InsurTech and fintech startups and doing some pilot programs with them, with our insurance businesses and finance businesses and just got all in on that.
Within a few years, I became a very active angel investor in that space and the rest is kind of history. By 2014, I decided to exit the businesses that were remaining. I've already exited previously to other businesses and I had at this time a subprime auto lender and auto insurance underwriter, which was the second largest nonstandard auto insurance writer in California at the time, alliance United Insurance Company. And we got investment bankers, did a process, exited both businesses and I became a full time investor, deliberately so. I deliberately exited my operating businesses so I can become a full time investor in technology. Because I just thought that technology was at an interesting inflection point at the intersection of the new emerging technologies between Artificial Intelligence and 5G that was on its way and just the improvements in GPUs and processing power and then blockchain that was emerging and everything else seemed like, wow, this is an amazing time. We're at one of those points in time where this intersection of new technologies are enabling tech startups to combine them so easily and create disruption in almost every vertical of every industry that there is and solve real world problems or improve processes for just about every business out there in one way or another, often in ways I cannot even imagine, solving problems that we don't know exist. And when you dig deep into these different businesses so it's just amazing to hear these stories each time. And I love going around to all the pitch events accelerator programs and learning what these businesses are solving. I became a very prolific angel investor. I've invested by 2019, I've already invested in over 500 startups. And that's when, you know, I told the story to enough former associates from my prior life where they're like, oh, that's cool, I want to do that too. And that's where I said, okay, I need to make a fund so others can co-invest with me. I also wanted that structure and I wanted to write some bigger checks. So it was getting very competitive by that time. So that's where Emerging Ventures was born from.
Fund 1 was a small kind of proof of concept fund. I pulled together under 30 former associates as limited partners. So we formed this partnership and pulled our money and that was Fund 1. And I just kept on doing what I was doing as an angel investor. But what was on thesis, I did through the fund. And we wind up making 28 investments in about a year and a half from Fund 1. Needless to say, that cut into COVID. We started pre-COVID and actually seeded most of the fund during COVID. About a third of it was pre-COVID and two thirds of it post COVID during COVID . So that was interesting to watch that happen. And then that was done and the automatic response was, okay, Fund 2.
So we started Emerging Ventures Fund Two in early 2021 and decided to make a slightly larger fund and that will have a 3 year investment period, kind of a traditional structure, and that's where we are today. Fund 1 has raised and deployed more than half of it. So we have slots for about 75 investments in fund two, and we already made 41 investments. And there's three that are going through due diligence for the past few months that we may or may not pull the trigger on at least one or two of them. But we already deployed 41, and we're still looking to bring in a few more limited partners on to invest with us.
We're still looking to fund more. But that's kind of the origin story and where we are today.
So I don't have a crystal ball any more than anyone else, but it does seem like each generation is just incrementally better and incrementally slowly improving the work efficiency so that the humans are doing less and less and the machines are doing more and more. And it's kind of a gradual slippery slope. It's not a switch that's flipped all at once where it goes from human to machine. It just slowly more and more is being assisted by machines and machines are more intuitive and takes less effort from the humans.
For example, our fund 1 has a startup called Gapify, which is process robots for accounting departments for CFO units. So we had all these accounting packages and they do all this work and humans had to do a lot of taking data out of one system and running Excel and then creating journal entries and other systems and then maybe doing some other double checking with third systems and balancing and work. And the CFO's role is still the CFO's role, but maybe a CFO instead of a team of 30 people can have a team of ten people. Because these new softwares that kind of behind the scenes do a lot of the manual labor, what used to be manual labor, really.
It's not a lot of thinking labor. It's a lot of repetitive tasks of taking data from one system, matching it with data from another system, doing the reconciliations and so forth, taking, say, banking data and matching it with general ledger data. It's all just manual labor. It's a process, it's mechanical and looking for discrepancies and trying to follow up on discrepancies all that. Slowly the machines are doing more. They're not just reporting exceptions to humans, but now they're actually figuring out what the exceptions were the way a human would maybe even sending emails internally to other departments to follow up and say, hey, what was this expense? Can you look at it's?
The same thing a human would have done. The computers are doing it and it seems so obvious and natural that no one thinks about it that oh my god, the machines are taking over. But in fact, as the software gets smarter, the humans become more efficient and as a result, you eventually can do a lot more with less humans. It's really just a slow gradual process and a slippery slope where it'll be normal, just like we no longer have armies of typists. I come from the insurance business. When I started, insurance companies literally still had rooms full of humans typing up policies. They weren't all computer generated. There is actually, in some parts of the industry, rooms full of humans that were typing up policies until many, many until just, you know, the early 2000s, almost all indexing of images was done manually. They got into digital. They went from paper files. When I started, it was paper files. We had file rooms that were humongous, and we would have maybe 20-30 file clerks running around, pulling files out, giving them to underwriters, bringing them back to the file room, putting cards in to show that a file was checked out. I mean, it was a crazy operation that obviously went away and got replaced with kind of clunky scanners and large hard drives to store them on.
And then in the document management system, the indexing of all those scanned items was manually. It had rooms full of people typing in, labeling those with policy numbers, insurance names, whatever. And the same was true in every other industry that, of course, eventually got replaced with the system automatically using OCR, using barcodes and everything else. Just today, there's practically no human indexers for that kind of work. It's almost all automated. It all gets matched up to policies, and it's expected to be that way. And no one ever expects to have a room full of humans that are doing that data entry per se.
Slowly but surely, the human labor is moving upstream and the computers are taking over. The scary part that we all talk about is that our jobs kind of what we consider the thinking part, the decision making is also being automated. There's some law tech out there. So today in law firms, not only is the data entry and the file clerk and all that, but the research is getting automated. There's some great AI law tech that is doing the research better than any human can. It can scour through much quicker and much more volume to find every possible precedent, case law and so forth. It can find any prior trial. It can find everything much faster than a human can. Before, a human would go to a search engine that was a legal search engine for whatever subscription they had, that they pay a lot of money for and search for those and then find those cases, look at them, read each one, try to find the relevant material. Today, the computers do that. They check the cases, they find the relevant part. They quote you what you need. They kind of write up your paper for you, give you the answers.
The same thing with responding to routine inquiries, filing inquiries with the courts. There's a startup that we looked at that was automating the whole process of managing court filings, logging into every portal and dealing with managing appearances, responses, everything, instead of paralegals more efficiently. Nothing slipping through the cracks, no one ever forgetting to follow up on the deadline so slowly. In every field, everywhere that's being done. And we've seen it not just the cool stuff, not just the automated drones and the autonomous cars and all that, which is coming too. And obviously truck drivers being replaced with autonomous semis. That's the things you see on TV and that's the media headlines. But the boring stuff behind the scenes is being replaced as well.
AI is coming for the lawyers. It's coming for the analysts for sure already, it's also coming essentially for the executives, for the decision makers. It's already replaced supervision in many call centers. The supervisor is actually an algorithm or in a sense AI robot that is doing the live supervision of call centers.
Every prior revolution in technology has created new jobs. Every time they said, oh my God, there's no more factory work. What are these people going to do? And there was always something and it was always better. There are a lot of experts that are today saying that this time might be different. If there's so many jobs being displaced all at the same time, nothing is safe. Like even the creative jobs are not safe. We have tech startups that are doing replacing ad agencies.
They're doing automated content generation and automated high volume ad testing on social media and with say, Google Ads and everything of different content, both graphic and text to see which gets the best response and a much higher volume of frequency and efficiency than humans could. So it replaces ad agencies and the analyst at ad agencies that would do this work. And then replacing, we have, for example, a startup called Gocharli AI, that is a generative AI. It creates content, it creates ads. You say, okay, make me an ad for this can of soda. And it will come up with some options just by looking at the image. And it will give you content, it will give you proposed captions and so forth. And they're doing pretty well. They're relatively new, but they're doing pretty well and it's amazing. It's quite mesmerizing like you're talking about the GPT3 and all that. This is the same kind of technology but it's their kind of narrow application. So the actual creative part, I'm not an investor in any of these, but we've seen the Generative AI that generates art and it can generate art in the style of any master and some of that is pretty darn good. I'm not a huge art aficionado, but from those who are, there's experts who are going gaga over this, where they look at some of that and they're like, wow, that looks like so and so's work.
So the question is even originally they would say, oh, well, the computers are going to take over all the boring stuff and we're just going to be creative. But the computers can generate the output. I don't want to say the computers can be creative. That maybe is not a true statement today, but the computers can mimic the output of what you expect from a creative if they can generate original music, like original, there is a startup I looked at that does original music scores for movies much, much more efficiently. You know, as far as creating just the right ambience, the right dynamics, the right like Crescendo is where you want to emphasize some dramatic scene coming up and so forth. It completely generates original music that is completely synced to a particular scene. And it does it instantly work. That would take days for a human. It does in minutes and it does it often, but they would say better, depending on how you find better for that. But that the end. Quality is more than acceptable to the highest standards of film production.
So we look at usually a minimum of 300 pitch decks in a month. It's a combination of a lot of direct inbounds. I'm an investor in many, many startups and I've looked at many others. Every startup founder has friends that are startup founders. I get a lot of direct inbound and then besides that, other VCs are always sharing deals and they want co investors or stuff that's too early for them they send to me so I can send it back to them a year later. There's a lot of that kind of synergy going on and then most of our deal flow, at least the higher quality deal flow, is from the accelerator programs. Over the years, I've looked at hundreds of programs throughout the world and found the ones that I really like over time and continue to attend those and follow those and provide mentoring and so forth, get involved with those and shy away from the ones that obviously weren't a good match for us because we don't have infinite time.
So we see hundreds of pitches and can easily dismiss most of them because first we need them to be on our thesis and then to say what we're looking for. What we look for is execution. It really comes down to one word, it comes down to execution. I want them to do a lot with a little, a little time and a little money. It's just some startups are amazing at just getting shit done. It just completely amazed me. I'm completely in awe and I'm kind of jealous compared to when I was in business and how fast they can get stuff done. They can form a company from an idea and six months later they can have a product, they can have an MVP, they can have some Fortune 100 pilot programs going on and traction is like, how did you build this and ship it and get people to pay for it all in such a short time? And six months is really, really fast and that's it. And usually it's a year and a half to two years, but they just do it before even often raising any money, because you don't need a lot of money to get started. They just go get some often free cloud computing because they all have programs for startups where they subsidize the early years to get startups hooked on their system, whether it's Google or Amazon or IBM and so forth. They're all giving away resources.
So you have almost unlimited free resources initially and they just go and they set up a startup and spin it up and they build and they start just iterating, they show it to early customers, they find contacts and they do it. And that's amazing. That's what I look for. So I'm looking for startups that are solving real world problems using emerging technology. That's our thesis. But then what we look for within that, my underwriting is 100% about execution. I'm looking for those who can execute, which is why I can't invest prelaunch. I can't invest in ideas I don't know how to look at a founder and their idea and know who's going to succeed and who won't. So I would be horrible at running like an incubator kind of program because I just can't tell them apart. And what I've learned over the years is the ones who succeed are often the ones who would have been the least likely to succeed. It's like, I go back, I look at these and I say, okay, if I would have looked at them a year ago when I invested in them, and often the answer is no way. They didn't seem very likely. They didn't seem like the most articulate and the most together. They're often the oddballs. The ones are at least likely to succeed both on where they are physically, geographically, how they present themselves, and they often just somehow get it done. The ones who did get it done are amazing.
I can give you an example in the drone space, which is a difficult space because it's hardware and software. It's taking AI and adding to it all the hardware mechanics, which is a lot more difficult than just software. There's autonomous delivery to hard to reach places using drones. There's a startup in our fund 1. When I first met them, I was introduced to them by another startup portfolio founder of mine, and this was a friend of his in a similar space, but not competitive.
So first of all, it was introduction by someone I trust. But when I met them, they weren't very articulate. It was in the early days of Zoom, and he wasn't making great eye contact on the zoom, had a strong accent, I can barely understand him, and yet so I would have dismissed him a year earlier, but yet they assembled the team, they built an MVP, and that's all. Okay. It's like, okay, you built an MVP, but how do you get to anyone to pay for it? Well, what sold me is he had two multimillion dollar contracts. He had with the top, and not just pilot programs, but actual contracts. And one was with big oil, one of the three big oil companies to deliver parts to their oil rigs out of the ocean. The other was with the US Navy to deliver obviously to aircraft carriers and so forth, which is a paradigm shift. Instead of taking a helicopter, it costs $4,000 back then, probably more now to do a round trip flight in the helicopter. It doesn't matter if you just want to deliver a $5 part if you need that screw to fix something. The only way to get there was a helicopter, and that would be a $4,000 round trip. They were doing it for $200 as a service with their autonomous drones. And what got me excited on that one, and here's what I tell you, what I look for is, they did a lot with the little, the perfect example of it. So we came in for the seed round at their Pre Seed.
They raised a total before us of about $2 million. And they got to where they are, meaning building a working prototype and then winning a contract from the Navy. They did that with basically $2 million total spend to date over about a three-year period. I believe before we came in, two and a half, three years, that they were working on this in some garage in Austin, Texas. At the same time, a very well funded Silicon Valley startup has just finished raising a Series B, a $30 million Series B at a 200 million dollar valuation. They beat out that company for this Navy contract. That was their main competition for the Navy contract. So it's a David and Goliath kind of story, and I always bet on the David. So the hungry, less funded, but yet very high execution. I bet on them.
Since they've done well, they've gone on to raise a large Series A about a year later, and they've renewed their Navy contract. They eventually last year got full authorization to autonomously land. They were the first private company ever to get, according to one of their press releases, to get permission to land autonomously on an aircraft carrier, on a live working aircraft carrier in the ocean. Nothing is for sure. We'll see what anything can happen. But that's an example of execution. They took a lot less money than their competitions and then a short period of time built something that outperforms the competition. So that's who I choose to bet on. I bet on the guys who do more with less.
And when someone comes to me, I see a pitch deck that says, oh yeah, we've been doing this research and we have. We spent $5 million in six years. We secured all these patents, and now we want to raise a round so that we can hire a team and build an MVP that's like, no thank you, because I have all these startups that in 18 months just built it with a lot less money. So I don't want to invest in the guys who may be coming out of academia or maybe coming out of another enterprise where they're doing things slowly, where they feel like, oh, we write these business plans and then we do our research. And then eventually, five years later, after we spent $5 million, we're going to hire a team and build an MVP. It's like, no, you just build it. So that's a disconnect in mode and thinking process. So 100%, we look for execution, nothing but execution. And of course, that's not enough. Of course they have to be able to be venture scale. It has to be the right kind of product for the right industry. It has to show that they can scale. Everyone shows that all this could be billions of dollars and this and that. So that's kind of a given in every startup pitch. But what differentiates them is about how much have you done, how quickly have you done it, how little money have you done it with, how nimble are you? And it's especially going to be important now when fundraising is harder, they need to be able to make money stretch out. They need to be able to make what they have in the bank go further.
I'm glad in some way that the crazy Silicon Valley rush and valuations is over. Everyone being able to leave a startup and start their own and get instant funding was kind of crazy. It was unsustainable. So I'm glad that that era is over. I am concerned about what next year brings. I hope it doesn't just go to the other extreme. The pendulum always swings too far in the other direction and that's what I'm fearful of. I'm hoping we have a soft landing and that, I guess, for selfish reasons. I have a portfolio of hundreds of seed stage startups and I want them to be able to raise their Series A. We already had two this year that were supposed to raise a Series A. They were on track for it to hit all the milestones and then as they were going through the process, the kind of goalposts moved under their feet and they either had their term sheet withdrawn last minute or were just unable to even get the term sheet that they were kind of promised before. That would be a no problem if they hit those milestones and they're now scrambling and at least one of them is going to shut down because they ran out of cash to make payroll, which is unfortunate. It was a really good company.
The other one is raising an insider bridge round and it seems like they're getting support from that and we'll see what happens next. It's unfortunate. Hopefully next year doesn't get worse and next year goes back to some new normal. A lot of that I think, will depend on the macroeconomics and I have no idea where that's going to be.
So what worries all of us is if there may be a complete freeze. We hope there isn't. We hope that there's still a lot of money out there in venture, but the firms are sitting on it to kind of support their own portfolio companies. So they're holding back on making investments in new portfolio companies. At least that's what we're hearing. We're hearing that the startups that we're talking to, the founders I'm talking to every day when they go out there, they're all saying, yes, we're investing, but we're holding off right now because we're concerned, because we have our own portfolio of companies that we want to reserve this money to support. So we're kind of holding back on releasing money to new companies and adding to our portfolio, even though we're continuing to look at portfolio companies. If we have this conversation in three months, maybe we'll have some more clarity. As of today, it's a big uncertainty.
We invest in US, Canada and Israel based emerging tech startups, which we define as mostly startups, using the convergence of emerging technologies to solve real-world problems.
Pre-seed and seed. But pre-seed is not pre-launch. You need to have a real product and some early traction to show that there's demand for the product and that you know how to sell it.
What's the typical check sites you put in?
From our current fund, we're writing checks from 100,000 to 250,000. Our typical checks been around $150,000.
Emerging.vc is our website, and there's a contact us form, and you can fill that out and attach a pitch deck to it. I'm also on LinkedIn and my own blog, davidmandel.blog, or also just mandel.blog. I have both domains. You can find me there with links to everything else.
Linkedin or Twitter. I'm at @mandelangel on Twitter.
Emerging Ventures website - https://emerging.vc/
Follow David on Twitter - https://twitter.com/MandelAngel
Follow David on Linkedin - https://www.linkedin.com/in/david-mandel-/
Read David's blog - https://www.davidmandelblog.com/