Deep tech startups often navigate complex terrain, where groundbreaking technology meets entrepreneurial aspirations. In our recent panel discussion "Clueless about: VC Red Flags – What Scares Investors?" our Head of Research, Dr. Ana Koller, was joined by Steven Jacobs, Venture Partner & CPO at Lakestar, David Meiborg, Partner at First Momentum, and Paul Klemm, Partner at Earlybird.
Founders should communicate with professors and investors about the attractiveness of holding a small percentage in a potentially prosperous company. Why is this often the most discussed red flag for VCs? An imbalanced cap table can lead to founder dilution, potentially diminishing long-term incentives to remain with the company and continue building instead of just joining an established corporation and get a great pay check.
• Deep tech founders must focus on de-risking their ventures. Why? Because often deep tech takes way longer to generate revenue. Additionally, build solid proof points around you claims, regarding your tech, applications and customers.
• Founders should prioritize proving their value to customers in the early stages of their business. Key indications that customers are willing to pay for a product or service - they’ll knock off your doors asking for more. Do you need a commercial co-founder to pull this off? Not a must, but someone in the founding team has to be commercially-minded.
• Scientists-founders are the best equipped for finding a product-problem fit! Applying scientific method on your startup and for commercialising your tech, starting from forming hypotheses, experimentation, validation, and iterative problem-solving, can boost your chances for success.
• Embrace the risk, and be ready to pivot. Resilience and the willingness to endure failures are fundamental traits of the most successful founders.
Lack of progress over extended periods of time can raise concerns among VCs. This often happens when the scientists founders receive public funding in the earliest stages, and they continue business as usual. However, the expectation is that once you’ve started a company - nothing is business-as-usual in your life anymore, and the product and company development is on steroids.
The final 2 cents: Invest time in building a strong network within the startup and venture community. And cultivate self-reflection skills for both personal and business growth.
Learn more about our Clueless No More community for (aspiring) scientist founders or follow First Momentum on LinkedIn to get notified when our next panel happens.
Read below the full AI-generated transcript of the recording. 👇
01:06
Ana Koller
As I mentioned, my name is Anna, good to meet you all and thank you for joining. I'm here on behalf of First Momentum Ventures. We are an early stage VC firm that invests predominantly in software as a service, industrial tech and deep tech topics. And due to our early stage deep tech startup affinity, we have been particularly curious about university and research spinoffs. So this is why we have created and are fostering the network for scientist founders called Coolest No More. So if you're not familiar with it, by some miracle Lena will post a link to it in the chat here. So feel free to explore the landing pages, learn about it and join if you find it interesting. So this Coolest About panel is part of what we offer within the network and this session is recorded and the video is going to be available to the members of the network.
02:03
Ana Koller
So this evening is all about learning about what would VCs flag when evaluating an early stage deep tech startup, why so and how to better prepared for your VC discussions. So to help out with that topic, we have assembled an amazing panel and I can't wait to pick their brains on this. So these are three investors of incredible VC firms with great track record in tech space in Germany, Europe and beyond. We are talking today with David, my colleague and a partner at First Momentum Ventures with a background industrial engineering and management. David's obsessed with all things tech but also developed a crush into DevTools and B, two B SaaS topics as well. So David was kind enough to prepare some ground zero slides to make sure that everyone here is on the same page about the topic of this evening. And we'll be kicking those off in just a moment.
02:58
Ana Koller
So next up we have Steve from Lakestar. Lakestar is one of the oldest and largest venture funds in Europe, so with offices in London, Berlin and Zurich. So Lakestar primarily focuses on investing in European startups with about 20% reserved for the investments in the US. The firm is known for its thesis driven approach and it has seven partners who specialize in seven investment sectors. Stephen with his incredible experiences and background in product management and design from first gen iPhone to Beats Headphones Chromebook Pixel Kindle Fire tablet and his own smartwatch startup leaves the deep tech investing thesis at Lakestar and is helping their portfolio companies build better products as a CPO and you just can't have this discussion without Early Bird. Founded in 1997, it is one of the most active European VCs with a focus on European technology companies. They back exceptional early stage companies and support them throughout their growth and development phases.
04:04
Ana Koller
Early Bird invests out a family of independently managed funds and has backed so far over 100 companies. So we are delighted to welcome Paul, a partner at Early Bird and an investor dedicated to space tech adtech B two B SaaS among other. So welcome everyone and thanks for joining us tonight. If you guys want to learn more about the panelists and explore their amazing careers and achievements as there is much to tell about each and every one of them, stalk them on LinkedIn and get connected. But before we proceed I would like to provide a disclaimer so everything we will be discussing here this evening applies to VCs as a funding asset. This however is not the only way to fund your startup. There are other assets and they will have different options or different opinions and some will not even care about these red flags that we will be discussing here tonight.
04:56
Ana Koller
So some of you might find grants, crowdfunding or angel investors or even bootstrapping to be more suitable for your ventures and that is perfectly fine. I just want to make sure that it is articulated here before we kick off that everything here this evening is about how VCs may look and evaluate early stage tech startups. So with that I'm going to finish here and give the floor and screen to Dave.
05:27
David Meiborg
Thank you for the kind introduction.
05:29
Steve Jacobs
Anna.
05:29
David Meiborg
Hi everybody, nice to be here. Glad that so many people came and then joined us and yeah I think it makes a lot of sense to maybe kick things off with a very brief intro on what we are doing at first momentum I promise you it will be two minutes and then we jump right into the topic of discussion. I think it makes a lot of sense to highlight some basics of how VCs work and how they operate in their daily business life. To then go over to the red flag segment. Because a lot of the stuff that we will be discussing can be traced back to kind of how our days look like, usually as a VC and what we look for in companies. So let me check my screen 1 second. There we go you guys let me know when you can see anything.
06:30
David Meiborg
Thumbs up. Wonderful. So yeah just a brief intro to first mentors. We are B. Two B precede fund. We are very technical. So Anna for example our head of research, she's a PhD in physics, spent a lot of time also in research and applied research in a large fusion company in Europe. So we like to dive deep and usually invest extremely early in precedents tickets between 250K up to a million but rather up to 750K if we depends on the round setup that the teams are targeting. Our topics are deep tech, everything that comes out of science, out of universities, out of the research context and wants to get into an industrial application. We also do industrial tech, both hardware and software, lot of climate science as well. And what I also do a lot is technical SaaS so everything software that has rather deeply technical core compared to rather shallow software tools that are kind of commoditized.
07:39
David Meiborg
We so far have over 30 startups in our portfolio across two funds. We just freshly started with a new fund a couple of months ago out of which we deploy now. So there are lots of portfolio companies coming in the next few months and years. So I think that will be the ground for a lot of discussion between us probably in the future. We also focus on the DAC region predominantly but increasingly opt also for companies that are operating across Europe. So I hope some of this applies to you. We have backed companies at the forefront of deep tech in our two funds so you can see some examples here. We have stuff from quantum computing, AI and robotics, advanced materials, nuclear fusion, carbon dioxide removal hardware. So everything that you kind of have in the deep tech space, we don't do biotech, but that's just a small inconvenience, maybe for some of the biotech founders out here.
08:54
David Meiborg
But there are lots of people who do biotech in Europe. So that's it like a brief intro into what we are doing and how we work on the detect side. And I think now maybe getting to one abstraction layer above is how VCs work in general. So I think what everybody has to have in the back of their head is that we obviously invest in startups and we want return for our investors. So yeah, a VC fund also has investors and we are also frequently on the lookout for new investors that will join a new fund that we are setting up every couple of years. And it is historically and statistically the case that 97% of all profits from a fund that we are investing out of is generated by less than 0.1% of the startups that we back. So that means that each investment in a fund has to have the potential to become like this crazy outlier that generates a lot of the value and the return for the investors of the investors.
10:05
David Meiborg
So our investors basically and if a startup doesn't match this criteria to have the potential to become this huge success. It is a pass for most VCs. And this is kind of like the ground rule that also entrepreneurs have to have in mind when talking to VCs that this is what they're looking for and to give you an insight into how investors work and how their daily life looks like. Because I think that is quite opaque from the outside. As for entrepreneurs who maybe don't have friends who work in venture capital or have some insights into the industry, so on average there are around 10,000 startups a year that we could potentially talk to and engage with. I'm just quoting from our own statistics internally, maybe Paul and Steve, you have other numbers, but I think the ballpark is probably right. And out of those 10,000 companies, we actually talk to roughly 1500 companies a year.
11:13
David Meiborg
And what I mean with talk is that we engage in a call, we get a pitch deck, we look at the pitch deck at the website and submit some material and decide if you want to further engage with the team or not. And then out of those 1500 companies, we kind of synthesize an opinion on many of them, pass on most of them, and engage with roughly 200 that we really dive into and really analyze, do a lot of calls with do referencing, do, market research, all the stuff that we have to do in order to come up with a decision. And for us, we roughly do ten investments per year. So, rule of thumb, one investment per month, just with some volatility obviously, and also seasonality. And what this means is basically that especially at the top of the funnel, so when you look at the 10,000 companies and getting that down to the 200 companies that we actually engage with, you can imagine that with a team of, like, ten people working at a Vcpunt.
12:18
David Meiborg
The implication is that each investor doesn't have a lot of time to spend per company on average, especially at this top of funnel. Like I said. And so that means that deciding to deep dive into topics also creates huge opportunity costs, right? Because you dive deeper into one topic, there are thousands of startups literally that you don't spend time on. So each startup that you spend time on has to be very carefully selected, so to say. And as we have a lot of uncertainty with early stage startups, which is the beauty of our job, but also sometimes the kind of like the obstacle that we have to work with or the challenges that we face. We have very little data points and also there is high information asymmetry between startups and the investors because we cannot look into your thoughts and all the facts that surround you and all the history that comes with an entrepreneur.
13:18
David Meiborg
And so that means that most decisions early in the funnel happen extremely fast a lot of stuff happens, experience based, gut driven, and most of all heuristically. And that is kind of like the invention of the red flag that is really derived from how investors work and what they're looking for. Now, there is a slide with a lot of text, so I will not just read that to you, but I think it's worth sharing that afterwards with some examples. But I just spoke about the invention of the red flag and maybe it makes sense to define it on how we think about it. So for an investor, a red flag is basically a means to speeding up the time from looking at something and getting to a pass or no. And it's just like a heuristic that has proven to be indicators where you can derive an increased likelihood of startup failure from.
14:23
David Meiborg
And when you look at this indicator, it's usually a quick thing that you can check. It really speeds up this, hey, we don't want to spend more time on this because XYZ, there are so many examples of red flags and I listed some of them. There are some hard facts that you can just quickly check as an investor and there are more topics where you might have a certain also volatility in opinions when people look at this. But for example, too many founders in the team or a single founder set up can be a large red flag for many investors. Same thing about the cap table situation. If you have non operative founders that have more than, let's say, 20% in a company, that is usually not perceived well in the eyes of investors. And I've listed some other red flags here, but maybe that is a good point to dive into the discussion, jump into the questions later on.
15:24
Ana Koller
Yeah, exactly. Thank you, David. This was really incredibly informative and I hope not too scary for the audience also. Exactly. Great inter for the panel. So I'm just going to kind of dive immediately into the question number one that I have. So the first few examples that you gave as really hard examples of red flags are raised by the cap. So and since most of our audience are university and research spinoffs, I suspect that a lot of them would have to have this discussion with Vitavc at some point. So how do you soften this? What can you do when A, your research and product is built by standing on the shoulders of giants who pretty much developed the whole thing for the last 20 years and drove it to TRL four and B on the other side? That professor keeps arguing that they need to be somewhat incentivized to foster entrepreneurship at their university chair.
16:32
David Meiborg
Yeah, maybe I can take this first question. So I think it's obviously super important to have like a respectful conversation about those issues. But I think the main discussion that it condenses to in the end is that as a founder, you have to explain to the professor that it's a big difference in having a small part of a huge success or a huge cake, like a small piece of a large cake or the other way around. And usually investors and also professors have the ability to understand the attractiveness of an interesting technology that can become something big and something meaningful and that even a small part of having just a small part of this can be individually very attractive. And I think I've seen a couple of founders also introing their professor to other professors who have made the decision to not take 20% in a company, being very happy with this after a couple of financing rounds and started progressing into a certain growth stage.
17:52
David Meiborg
And then it becomes quite attractive also in monetary aspects. So I think it's respectful discussion, obviously, and pointing out this small piece, big cake issue.
18:07
Steve Jacobs
I can maybe add in there just to give some specific numbers. I think low single digit percentages should be the target. And if you want kind of the gold standard, I would look at Stanford's Tech Transfer office criteria 2%, 5%, maybe up to ten, rarely. I think what we're seeing out of the UK is trending more in that direction and getting more reasonable. What we're seeing out of mainland Europe is pretty out of whack. And the problem is where all this comes from originally is later on down the company's path, let's say six years later, we have founders that have a relatively small ownership percentage and then they have a decision to make because as a founder, as you continue raising funding for a company, you have to revest your shares, right? You have to kind of commit yourself back to the company so that your investors know that you're not going anywhere.
19:03
Steve Jacobs
And at that point in time, you have a decision to make do I want to basically reinvest myself in this company or do I want to google or some other company that's going to pay me a lot of money in liquid cash? And you can kind of run analysis on that, like adjusting for risk, right? It's a sure thing at Google and not a sure thing at a startup. And the amount of upside that you have from the startup needs to be worth more than what you could make at a sure thing at some large company like Google. And quite often it's not. And so if too much of the cap table is allocated to a tech transfer office originally and the founders are diluted by that, then as investors we know six years from now the founder might actually make more money by going to Google than sticking with the startup.
19:49
Steve Jacobs
That's a problem, right? So we want to make sure that the founders have enough equity in the business and have enough upside where adjusting for risk, they're still incentivized to stay with the company. And I think all the math kind of builds to that.
20:05
Ana Koller
Yeah, that's true. And Paul, do you have some examples maybe of companies that have made bad choices and then remedied this or is this kind of completely done deal?
20:21
Steve Jacobs
You can definitely remedy it. Paul, I'll let you chime in a second here. I see you have a comment. As you know, I've personally invested in a company that came out of a it wasn't a university, but it was an institute, one of the big scientific institutes in Germany. And they took a large percentage, right, double digit percentage. And when we invested, we said, hey, we love this company, we love the founder, we love the team, we love the technology, we'd love to support them in their growth. But we laid out exactly the argument I just made and we showed them the math. We said, this isn't going to make sense with the amount of capital that this company is going to need to grow, the founder is going to end up with 5% of the company. There aren't sufficiently incentivized, so we can't invest.
21:05
Steve Jacobs
Right, because we don't know if the founder is going to stick around because quite frankly, they're not incentivized to sufficiently. And as a result, we essentially created more equity for the founder and for the equity pool for the team and said existing investors, including the tech transfer offices, have to do this before we invest. If you correct the cap table, we would love to partner with you if you're unwilling or unable to, we can't justify that for the reasons that we outlined kind of in the calculus. So it's definitely possible to fix, but obviously it's way easier to fix this early on because, as David said, red flags.
21:45
Paul Klemm
Right.
21:46
Steve Jacobs
Like were patient because we chose to be. There's plenty of situations where we aren't patient. Right. And we say, hey, it's not worth the effort, we like the founder, but we're not going to spend time trying to remedy the situation. It's too out of whack. And that's a shame, right, because oftentimes it's a good founder with a good business, put a lot of effort into it and just didn't set things up for success. So I think it's a good thing to address early on.
22:14
Paul Klemm
Nothing much to add here, right? You want to have to be incentivized legally and commercially and maybe below 5% of times is the answer to your question, I guess, outlined.
22:38
Steve Jacobs
Right.
22:38
Paul Klemm
If you do the math and you look at that at a ten years horizon, there will be redundancy of the founding professors on the cut table. So why should she or he have so much fake in the beginning?
22:52
Ana Koller
Yeah, exactly. And the same goes to not only tech transfer offices, but the incubators and accelerators at the early stages. Same story, like evaluate the value of it. But Paul, then to allow you to have some space in, like, what are your views and kind of shave off criteria for their early stage deep tech startups. And of course, what advice do you have for founders to proactively address and mitigate these red flags of yours?
23:27
Paul Klemm
Sorry, just there are many red flags, right. We just talked about cat table, which I guess is less the better when it comes to stakeholders in the beginning and keep it simple. And then a second red flag is something about derisking.
23:47
Steve Jacobs
Right?
23:48
Paul Klemm
When we talk deep tech, all of us know around here that probably deep tech companies or frontier tech companies will take longer to commercialize and actually have revenue. That is something what growth investors would love to see. So how can you actually communicate and raising more and more capital? And we all know. So the big question is, how can you actually communicate and show that the business is derisking while it's not shown commercially that it's actually developing because it takes more and more time. And that's something where you need the team actually to be able to communicate on a product or textile while you are actually derisking the business. And that is something a red flag. Or let's say it's not about community post. Right? And I guess, Steve, back then, the Series B investment in our joint co investment at ISI Aerospace, they had no revenues.
24:49
Paul Klemm
We knew we needed 100 million funding. We will have a team by them, but not a person, by the way. So what is the way of leveraging the assets further? And farmers are not able to tell that story and actually depict and break down that vision, that ten year vision into single fundraisers. And that's sometimes a red flag for us.
25:15
Ana Koller
Yeah, that's actually a very good translation to what I was about to ask Steve. So in our earlier discussion, Steve, you mentioned that one of the red flags that you often see with deep tech founders is that they're developing technology without a clear problem hypothesis. And then you developed this whole kind of mental framework of how to help founders remedy that, which is like how to use scientific method for developing a business besides just developing great tech. Do you mind sharing your insights about that?
25:55
Steve Jacobs
Sure. Assuming this audience is mostly kind of deep tech and technical founders, like everyone's familiar with the scientific method, you generate a hypothesis, you design an experiment, you run the experiment and collect data. You then look at that data, form a conclusion, and then generate a new hypothesis. I think very often we see especially technical founders who have developed some really exciting new technology and are very excited about it, really focus on the technology itself instead of the problem that their technology can form a unique solution for. And obviously, anytime you're starting a business, at the end of the day, it's about identifying real world problems that exist and then identifying a solution that you can develop that's ten x better than anything out there to solve that problem, right? Some people call it a painkiller for a pain point, the technology is really in service of the product, right?
26:54
Steve Jacobs
The technology is what differentiates and makes your solution defensible and not the other way around. The product isn't in service of the technology and the way in which to kind of translate from having developed an interesting technology into figuring out where you have product market fit is really, in my opinion, the same as the scientific method, which is you raise maybe some precede funding, which is essentially an investor saying I believe in you. Figure it out. And you're stumbling drunk through the forest and you say, I have a hypothesis for a problem that some user has out there and I have a hypothesis for a solution that I can build that will solve their problem or be a painkiller for their pain. Point. Using my technology, what is the fastest, cheapest way I can run an experiment to validate or invalidate that problem is a real problem and that my solution actually solves that problem?
27:55
Steve Jacobs
And then that could be phone calls, that could be a survey, that could be running a really hacked together experiment. One of the examples I like to give is DoorDash in the US. A food delivery company, their minimum viable product or their experiment that they ran was essentially they put up a flyer that said, if you want a burrito delivered, call this number. It just called the founder's. WhatsApp? Right? And he answered and said, okay, you want a chicken burrito? Great. He biked to the store, pick it up and delivered it to the person. That was the fastest, cheapest way he could figure out to determine whether or not there was actually a problem around delivering food or delivering burritos and how he could prototype his solution of someone's willing to just ask someone random to deliver it to them. And from there, he generated a new hypothesis and said, okay, that worked for me.
28:49
Steve Jacobs
I'm going to now create an app that does what I used to do on my phone, right? And it's a more sophisticated product and a more sophisticated solution for the same problem. And he kept validating that and then he said, okay, is this only a problem for burritos or is it also a problem for sushi? Right? And so he's now testing a hypothesis against is the problem space bigger than the initial problem that he tested? So it's just constant iteration. And a lot of the terminology that we use in the startup world around Minimum Viable Product, around Product Market Fit, you can map to the terminology in the scientific method. Minimum viable product is a cheap experiment. Product Market Fit is a positive result to the experiment and the hypothesis that you ran. And then if you end up running an experiment and it turns out you were wrong, you thought people needed something that they don't need you misunderstood what the problem was in the market.
29:44
Steve Jacobs
That's okay. You say, okay, I'm going to learn from that. And I'm now going to generate a new hypothesis, a new problem that I think exists and I'm going to test that as fast and as cheap as possible. And in the startup world we call that a pivot. So the Lean Startup Handbook or kind of that set of terminology is really the same thing as the scientific method but applied to building a successful business and determining what works and what doesn't because most people who are really good at developing technology don't also know the ins and outs of every customer need out there. Right. That's a very different set of expertise and we might touch on that a little later in this call when we talk about kind of ideal founder team compositions, but I hope that kind of covers what you were referring to there, Anna.
30:28
Steve Jacobs
But I think in general the advice is and this isn't really a red flag so much as it is something that we look for it's when a founding team kind of comes to us and says, hey, here is my hypothesis. Here is the experiment that I want to run. I'm looking for you to invest capital in me to run this experiment. And if my hypothesis turns out to be true, then here's the next experiment I'm going to run and I'm going to raise more money to go do that. And you keep doing that at larger and larger scales and that's essentially what the different tranches of venture capital are designed to do. So precede is like I'm randomly trying to find a problem. I don't know what it is yet. Seed would be, I have some indication that there's a problem here. I want to run a test to validate it.
31:13
Steve Jacobs
Series A would be, I got positive signal from my test and now I want to test it on a bigger market with a more sophisticated solution and really see if there's kind of as strong a pull as I believe there is. And from series B and beyond, it's really there's. Clearly product market fit. Now we're scaling it. Right. And the risk we're taking is kind of pouring jet fuel on the flame.
31:38
Ana Koller
That's excellent. That's exactly what I was looking for. Thank you. I was just wondering, I mean, a lot of deep tech founders and especially research and universities, Pinoff founders, they have been working on this tech that they're developing for quite a while and for them the whole career has led to that moment. It's not that easy to pivot or is it? And this is sort of like how should they go about taking a risk and embracing that? They should not be afraid of testing a hypothesis.
32:17
Steve Jacobs
Yeah. I think one of the characteristics I hope Paul and David would agree that are fundamental to a successful founder is this concept of grit or resilience. Or willingness to kind of endure a lot of challenging situations to get to a positive end result. And I think that's important because most of the tests you run are going to fail. That's the nature of science in general, right? And that's not any different when it comes to the science of building a company, right? And just because a technology works doesn't mean that the company works as you think it might. And so I think there should be an expectation, especially for a deep tech founder who's kind of starting with a technology and looking for a problem to solve, there should be an expectation that a lot of the hypotheses are going to be wrong. You might get lucky, that would be wonderful.
33:13
Steve Jacobs
But most of the time, actually, to be totally honest, the vast majority of time the founders never find a problem to solve. Right? 90% plus of the time it's a technology looking for a problem that isn't able to find one because it's actually not better at solving certain problems, even if it's a really interesting technology. I think Betamax and VHS are a great example. Like Betamax was actually a superior product from a technology perspective, and it never succeeded in the market because they didn't build a successful business behind it. So I think using the scientific method isn't about a guaranteed success, it's about giving yourself the highest probability of success by following a rigorous method that accounts for the fact that most of the time the answer is going to be no, right. Any other method that is used is likely to be less effective than the scientific method at figuring out the answer to an unknown.
34:12
Steve Jacobs
And that's why, in my opinion, it's the most efficient way to figure out is there a problem that my technology can make the best solution for or not.
34:24
Ana Koller
Thank you. And do you guys have anything to chime in? Paul dave.
34:34
David Meiborg
Yeah, I believe I totally agree with you, Steve. I think when we are looking at deep tech founders, what really amazes us and what really gets us excited is when very scientific founders, PhDs, postdocs, whatever that come with an IP essentially out of their research. But have. Also this product centric, customer centric view and really have done their homework talking to different people in different industries, validating use cases, running some experiments on a very small scale and that really gets us going in a process. At the same time, I think we have to also acknowledge that there's a certain path dependency when you are coming out of a research context where you have a very narrow scope in terms of what you are building, and you might have a very limited scope on the application side of this technology. And I think you can obviously iterate a lot regarding use cases, industries, et cetera, but I think it's a very hard thing for founders to do to kind of scrap out everything on the technology side and just start from the beginning again.
35:54
David Meiborg
Because like Anna said, the hyper specialized people in their field, in their subset, maybe of a certain field. And I think moving away from where they have like to call it superpowers is also maybe not the smartest move for them individually. I don't know, a PhD, like a biochemistry PhD building something is probably not going to pivot into an AI developer tool because he has found a problem in this space. This person will probably not be the best to do it in this space when he or she hasn't found something to build, something meaningful to build with his or her superpower expertise. And I think this is something that I wanted to also highlight a little. And I think one other aspect that we internally like to talk about is those two dimensions of technology risk and market risk and how they interplay. So what I like as a thought experiment is to look at cancer treatment technology.
37:03
David Meiborg
Basically, I think there is an application where you have zero market risk and 100% technology risk. So if you find a cancer cure, you don't have to worry about product market fit, right? Like there will be a market. It's kind of like a binary thing. If you solve the tech, you have the market. But obviously this doesn't apply to 99% out of technology startups. But I think to various degrees you can put yourself on the spectrum of how many market risk and how much technology risk are you actually having in front of you as a founder. And I think founders that have a really differentiated view on those aspects typically know what drives their business and what they have to look out for in order to make it a success.
37:55
Steve Jacobs
I would add to that, David, if a founder says, I'm going to develop a therapeutic to address cancer, you are starting with a problem, right? You are saying like, cancer is a problem. We need more solutions. I'm going to identify a molecule that is a good therapeutic. The other way around would be I invented a molecule. I have no idea what it's good for. Now I need to figure out, is this good for cancer? Is this good for heart disease? Is this good for a plastic material for food packaging? Right? I think your example actually was an example of starting with a problem, right? Sometimes you don't even have that.
38:36
Ana Koller
Yeah. Guys, do you need to see a commercial founder on the founder slide to get reassured that the team is going to pull it off and find a good product market fit in the end? Or do you believe that a techie can pull it off too?
39:02
David Meiborg
Maybe. Steve, you want to go first?
39:05
Steve Jacobs
Doesn't matter. I can jump in quickly. Short answer is no. It's not a requirement that there's someone who had like an MBA in their resume in the team, but there has to be someone who's commercially minded, right? Someone who has a deep understanding and empathy of what it takes to identify problems with a customer, to design a solution, to build a business model and identify willingness to pay and all. It's very hard to build a business. I come from the engineering world. We always made fun of people who weren't engineers because it seems easy what they do. I very quickly realized how hard a lot of the elements of building a business are. They're just different than engineering. And someone has to kind of be there to represent all the challenges associated with designing a business model that allows your solution to be viable in a marketplace of ideas.
40:02
Steve Jacobs
That skill has to be represented. That can be an engineer. It can be someone who is a PhD in physics who just happens to really have a deep appreciation for that. And if it's not the technical co founder, then it should be someone that they brought on as a co founder to supplement what they're strong in with this other skill set.
40:22
David Meiborg
Nothing to add.
40:26
Paul Klemm
Maybe even thinking, I don't know, two to five years ahead of the road, right? You will never have a complete or let's call it a team set up that will last for the long run.
40:41
Steve Jacobs
Right.
40:41
Paul Klemm
So we are actually looking for founders that have that forward looking hiring mentality, meaning, hey, I have just hired and complemented myself because maybe I have a technical background. I need a co founder with commercial skills. But the guy who hired commercial skills also needs further complementation down the road, right? And I think that's a skill or a mindset early bird would be looking for, right? How can I actually onboard hire, attract talent? That is someone.
41:16
Ana Koller
Yeah, that's actually a great add on. So with that, I think we are halfway through and we have already quite a lot of questions. So I would be giving the audience a chance to chat with you. So I'm going to share my screen again, script number two. And yeah, by the way, guys, have we hit all of the important topics as indicated by the word cloud from the audience? In the beginning, we have captain and so on. So there go questions. So the first question that came in from the audience is have you previously had a red flag that you later revised and they're not sticking to anymore?
42:14
David Meiborg
Yeah, maybe I can share a story here. In startup statistics, it's usually portrayed that you would want to avoid single founder setups where you just have one person starting the company. There are various reasons for it. Besides the data point. There are also narratives around this that make a lot of sense intuitively. Maybe to just pull out one of those is like it's very important to have a sparring partner as a founder, to run ideas with, to have complementary skill set, et cetera, et cetera. And we had this kind of like as a principle at first, momentum early on that, hey, we want to avoid those setups. Let's look out for two, three, four people, founding teams. Then one founder came along and we just had to invest in this person because he was so outstandingly, smart, driven, ambitious, very well skilled and experienced in multiple dimensions that were super important for this company.
43:19
David Meiborg
And let's put it that way, we are extremely happy about making that decision and crossing out this red flag or this principle very early on. But I also have to say that it was the first and the last one single founder set up and that we invested. So there are exceptions to the rules. I guess.
43:49
Paul Klemm
I don't have any more stories at hand. Sorry. Yeah.
43:54
Steve Jacobs
Maybe the other example I could give is sometimes a market will seem a lot smaller than it ends up being. And so very early on you do kind of analysis of how big can this business get? And then a few years later you realize, man, it's gotten much bigger than we had expected. And then you kind of go back and revisit your market math and did.
44:19
Ana Koller
You ever go back to a startup after parking it?
44:25
Steve Jacobs
Personally, no. That doesn't mean that I won't in the future. Those feedback cycles are very long in venture. Right. Like I imagine if there was a company that I met at Seed Stage in order to see enough data to kind of convince me that the model should be different. Usually takes four or five years for them to kind of get big enough to prove that they're outside of the error bars of the math. And at that point, it's a very different stage. It could happen, but it hasn't. Personally, I'm sure it's happened many times to other investors, though, it's a very common kind of case.
45:04
David Meiborg
Yeah, maybe one short story here, I'm still super pissed at this development. So you maybe know, like vector databases, they have become a very important thing now with LLMs and the advances in AI. And I've looked at a lot of companies before GPT-3 came out and made such a big splash and everything. And before I thought, hey, who the hell would need a vector database? That sounds very nichey, very too small of a market to address. And then there was a single moment in time where everything has changed from and now those companies are taking off and we are sitting there without a bet in our portfolio. And as we only do precede investments, I think the train has left the station.
45:59
Steve Jacobs
Great example.
46:03
Ana Koller
Thank you. And then the next question is how important is revenue for a Seed Stage investment? Can use it by pilots without payment, compensate for the lack of revenue. This is attraction question, right?
46:17
David Meiborg
It really depends on the company, right? So if you are looking at a deep tech company, I wouldn't care about revenue at all, at least in the precede and probably also in the seed stage around. Also then it depends on the commercialization path of the technology. But overall, I would stick to this rule. Obviously, if you are building a very, let's call it, standard application layer software as a service solution in an industry where you can validate some of your key hypotheses extremely fast, maybe even without the product, like Steve pointed out before with the DoorDash example, you would want to see revenue at a certain point in time. After starting the company and running those first experiments. But I think for this audience, raising a pre seed round or a seed round, I think most of the discussion in a fundraising process should be around technology and the application and proof points that you can gather from customer conversations, maybe on pilots that you have closed Lois, that you have references from champions at companies that would maybe later use your technology, that you can pull out stuff like this to validate your claims.
47:39
Ana Koller
Thank you, Dave.
47:41
Paul Klemm
Steve traction never lies. And I would very much agree with David when it comes to precedes, but the more data points you have and it doesn't need to be necessarily revenues, but the more data points you have, order, intake inbound, request everything that might point to further getting cash in the company from customers at something super valuable. That's also what David is saying, right? It's about more indications that someone is willing to pay for it. But think about it differently. Can you actually prove that you can bring value to the table for customers in the early stage of your business? And that's something that should be at the core even when rate precedes, right? It's the basis of every business and that should always be.
48:35
Ana Koller
Yeah. And I guess with that we are kind of back on how do you prove your hypothesis? And this is one way, right? Steve, sorry, they meant to cut you.
48:44
David Meiborg
No, no, all good.
48:45
Steve Jacobs
Yeah, exactly. So I would agree with what Paul and David have said. I think if you're at a stage where kind of the appropriate experiment to run because it's the fastest, easiest thing to do is an unpaid pilot. Awesome. Do that, right. And if at the end of that they come back and they say, hey, we want to keep using this product, we're willing to pay for it, like let's move to whatever. That is huge validation that what you've built has product market fit and has value and you can then go to the market and say look at this feedback we got from our unpaid pilot. There's real traction here. If at the end of the pilot they say thanks, we'll think about it, well, okay, you've learned something, right? Like they're not willing to convert. There isn't really indication of product market fit. You're not going to get a whole lot of credit on the traction front if they're not going to convert, right?
49:38
Steve Jacobs
I think you should get a lot of credit for running a good experiment because you learned something. Hopefully it was the fastest, cheapest way to learn what you learned. But eventually you do need people kind of moving down this pipeline of saying, I really need this. This solves a big problem for me. I'm willing to pay for it. Let's move to the next step in the relationship. And the last thing I mentioned is I get asked the question a lot like, how do you know if you have Product Market Fit? The answer is, you'll know it when you have it, right? It's very obvious. Like a company is beating down your door saying, how can I get more of this? Can I give you more money? How can we deploy it faster across more people? How can I get more involved with what you're building?
50:19
Steve Jacobs
And if they're not shaking down your door to get it, then they're not that motivated to have it, right? Every company is going to try and maintain a good relationship and say, oh, I love what you're building, let's stay in touch. It's definitely interesting for us. It doesn't really mean that much. That's just people being nice and trying to keep the door open because maybe something might come out of it. So that transition point moves from people being nice to people pulling at your jacket saying, hey, I really want this. How do we make it happen?
50:47
Ana Koller
Yeah, exactly that. Next question. I think we answered this during the panel, but in case anyone of you wants to add something to this is Captain Fixable After. And I guess we answered this before.
51:06
Paul Klemm
Paul, maybe just to add, maybe external trigger points can actually help you to fix your internal cap, right? Basically, MVC is just coming in. If we get the cap table restructured in the following way, right? Otherwise the business will run out of cash. So you as founders can actually set or use external figure points to help you restructure your cap table in some of it.
51:33
Ana Koller
Thank you. Let me go to the next one. Are there any CV or background related red flags for founders?
51:45
David Meiborg
Maybe indirectly so it's not like that. We have certain universities that are kind of like a red flag or something. I think that would be pretty stupid. But what we look out is, and also very early on in the process of screening a startup is what we call or many investors call Founder Market Fit or Founder Product Fit along any dimension you want to have. So I like to explain this with like a counterexample. So I think I used that before in one argument. If you have like a founder where you're just asking yourself, why the hell is this person starting this company? And there's no solid explanation for how that got into existence. I think that is something where we want to take a closer look or see that as a red flag. So, for example, if you have a biochemistry PhD, like I said, building an AI tool and telling you that he's had this cutting edge research breakthrough in a field that he's not acquainted with, this is obviously something where you would look out and see some adverse effects.
53:06
Ana Koller
Yeah. So it's pretty much about answering the question why you right.
53:12
David Meiborg
Exactly.
53:13
Ana Koller
And this is the best way to show it with the founder.
53:17
David Meiborg
And I think especially in deep tech, as the fields and the products are so narrow in terms of the skills and the research that has to flow into the productization of a technology.
53:32
Paul Klemm
It.
53:32
David Meiborg
Can become quite obvious early on if the necessary skill and experience is in the team or isn't in the team. And if this kind of like the CV of a founder makes sense in that context. It's not a hard factor, but it's certainly a data point that you consider.
53:51
Ana Koller
Thank you.
53:52
David Meiborg
Maybe there are also other opinions here in the realm.
53:58
Ana Koller
Ardedo?
54:00
Steve Jacobs
No, I think Founder Market Fit is important. And Founder Tech Fit is also important. So we look for founding teams that really understand the technology and we look for founding teams that really understand the problem space that they're trying to address with the technology. Those both have to exist for us.
54:23
Ana Koller
Right. And then I will jump to the next one. We have quite a few. What if the University transfer office asks for royalties instead of equity? Is this a red flag? And what are acceptable royalties for a software product? Do VCs actually interfere in that reasoning? Dave?
54:49
David Meiborg
Sorry. Yeah, I think what the Tech transfer offer offices, they have certain instruments, let's call it, to kind of get what they want. I think equity is the instrument that has the highest cost for founders, especially in the long run. I think royalties can also be crippling to a company when they have a certain percentage point that will hinder the company from investing their revenue back into the organization. So I don't have any concrete numbers where we have like a strict red flag, but I think it really comes down to the individual characteristics of what they are offered, basically. So I think royalty contract is nothing that we would kind of just blindly agree to and move onwards because it's not an equity component. But this would be a factor where we want to dive in and really understand the mechanics of the contract. I don't know if Steve paul, you have more data oriented answer here.
56:03
Paul Klemm
I don't. It's a good orientation that equity is something which is the most valuable to you as a founder. Then when it comes to royalty, it's a very individual question. How is it just structured? Do you actually want that?
56:18
David Meiborg
Maybe one thing to add here, I think it's a similar topic that is usually discussed when it comes to the IP and the tech transfer offer offices is the IP protection or the IP transfer part. If you have like exclusive or non exclusive licenses, I think here the rule of thumb is that you 100% want exclusive licenses if you don't get kind of like the whole IP transferred so you have some protection against other people coming and rebuilding what you're building. So a non exclusive license is a big red flag for us.
57:02
Ana Koller
I want to just add here we had on our last panel discussion, coolest about IP transfer. We talked about this, and one of the panelists was the founder of Vondelbots, and he said explicitly that for them it didn't make sense to argue as a software spinoff to argue for the IFP transfer. In any case, because in two years, you're going to reshuffle your software completely anyways, so anything that was original tech that originated maybe at Uni would not be in your product anymore. So that might be helpful for some. I would jump on the next one. To what extent are you using data to define the red flags? So we mentioned like threw in some numbers and statistics here, but how about the general data and what data would you use to define it?
57:57
David Meiborg
I'm looking for the book that I want to yeah, it's called Super Founders. There's a lot of data on stuff that works for startups historically and statistically and stuff that didn't, and also the biases that investors have towards some data points. Age is for example, a very interesting thing where you have kind of like a curve that you have some outliers that started companies extremely young. For example, the founder of Canva, I think she was 19 or something when she started the company. That would probably be a red flag for many investors and was probably also a factor back then of many investors saying no, but obviously we all know Canva, so turned out to be quite nice. Same on the other edge of the spectrum and there are lots of data points in this book. I can highly recommend it to investors both and entrepreneurs.
58:58
David Meiborg
But data here on the red flag side, I think the definition of data here is somewhat fluid. So I think there are some hard facts that we can check when we have a pitch deck. And the pitch deck is somewhat holistically clear regarding the content that is displayed. But we are not using any kind of scraping technology and filtering to rule out the companies. Even before we talked or looked at a company's website, a pitch deck or whatever.
59:38
Steve Jacobs
We do use quite a bit of data, especially when it comes to market analysis or competitive analysis, to kind of understand how many other people are doing this. What do we think the size of the market is? I mean, there's a lot of assumptions that go into those models as well. Also like how much capital will be required for this company to kind of get to scale, and what does that mean in terms of dilution? So I don't know that those really qualify as red flags because they require quite a bit of diligence work. So they're usually flags that come up a little bit downstream. But you can't know those things without kind of pulling a bunch of data from industry, for example, to inform your analysis. In an ideal world, the founders have already done that work. Like, the best case scenario is the founder says, I have a really robust market and competitive analysis that I've run.
01:00:32
Steve Jacobs
Here are my sources. Here's the data I used, here's how I did the math, and we take a look at it and we say, this is great. We believe kind of what you have, and you've cited your sources so that we can validate it. I think that actually can be a very strong positive. But if it doesn't exist and if we have to do it ourselves, obviously it can be a red flag. If it doesn't add up, that will.
01:00:54
Ana Koller
Be like a good and clean data room. But in general, do you use that data before you talk with the companies to shave off already or during the diligence process?
01:01:08
Steve Jacobs
Well, there's two ways that we kind of get in touch with companies, right? Like, way one is the company contacts us and says, hey, I'd like to talk to you. Or some mutual contact says, hey, you should talk to this company. And then path two is we have a hypothesis or a thesis that we look at internally. We say we think fusion energy is going to be a big thing. Who are all the companies working in this space? We talk to each of them, and then we approach them in that context, I think. In the context where we kind of do a bunch of homework on, like, a deep dive on a market or a sector initially. Then, of course, we've gathered that data already and we've analyzed it deeply, and we've reached out to companies based on what we learned from that. If it's a situation where a company approaches us, our first point of contact is likely going to be that first meeting with the founder.
01:02:01
Steve Jacobs
Like, very rarely is there time to even read through the deck that they send before that first meeting, let alone do a bunch of additional work on it just because of the sheer volume of inbound that you tend to get. So typically the first screening is, what do we think of the founders? What do we think of kind of how they communicate, what do we think of their characteristics, what do we think of the vision they've outlined? And then only at that point do we okay, like, they've passed those red flags. Let's actually study the deck, do a screening, start looking at the market and other factors.
01:02:33
Ana Koller
And that ties nicely to the next question. But yeah, sorry Dave, go ahead.
01:02:38
David Meiborg
Maybe one little thing to add something that we regularly check without even talking to the startups is one red flag would be a multi year company existence without any kind of movement, traction, whatever. So if we have an inbound from a founder, we go up to his or her LinkedIn and we see that they are working on the company for, I don't know, five years. But they haven't raced around, they haven't hired people. The website still looks like it is like day two of the company. That's usually something where we are a little bit skeptic about is this really a company that has the venture trajectory and the ambition in mind that we are usually looking out for? But I'm pretty sure there are also exceptions to this rule.
01:03:33
Ana Koller
Yeah, very much so. And that actually reminds me, I wanted to ask you the question about the grant hypnosis. So when a lot of researchers would kind of get a grant in order to kind of set up the business, like Exist for example, which can be a great asset, but then somehow they just prolong, not really gaining traction, not really kind of moving on with the company. So we often talk about this as a red flag as well. Right. So you explained once to me Dave, about your love hate relationship with the Exist. So would you like to share that with the guys here as well?
01:04:15
David Meiborg
Yeah, sure. I mean, exist is a wonderful program to support founders that come from science and give them salary, give them runway, et cetera. But I think like I said, I have a love hate relationship with it because obviously it has a good meaning behind it. But I think for many founders it is very comfortable with those type of grants. And I really don't like if the founder life does not change at all after starting the company. Right? So if there's like the smoothest transition ever from sitting in the research lab, doing nine to five, et cetera, and then starting a company, there should be kind of like a lightning going through the daily life and kind of like how the person thinks about building this company. And I think for Exist founders, sometimes the notion of hey, you cannot actually start the company before they exist is through, you cannot get revenues until they existed through some of those contract characteristics.
01:05:26
David Meiborg
They are just not in favor of people really building companies quickly and with a high ambition and drive. And so that's the kind of friction that I sometimes observe from Exist programs and other grants.
01:05:43
Ana Koller
I mean, people call them sleeping beauty phases for a reason, but then moving on. And this tied up nicely to the answer from them and before the cold. Outreach is fast but least effective in comparison with networking and trust building. With investors, warm interest and personalized connections are. Effective but slow. So what should be the ideal strategy here? Which ones you prefer?
01:06:14
Paul Klemm
Just why are warm interest and personalized connections slow?
01:06:19
Ana Koller
Sorry?
01:06:19
Paul Klemm
What can happen to why are warm interest in person like personalized connections slow? It's the best thing that can happen to you, I guess.
01:06:30
David Meiborg
Yeah. And I mean, nowadays as everybody is scraping LinkedIn and are with data driven approaches, you can just put stealth founder in your LinkedIn profile and they will come anyways so you don't have to go to them. So I think that is a good tactic and otherwise I would fully agree with Paul. I mean, it's a little bit more tedious to get those intros, but when you get those intros, they are high value, high quality, and you will increase the likelihood of getting into that first call by an order of magnitude, probably.
01:07:10
Ana Koller
I would even argue that the cold insurer is or just dropping a pitch in the mailbox is lower because of all of the hundreds of ones that you have to go through to get there.
01:07:21
Steve Jacobs
Steve yeah, no, I would agree. I think different sized funds probably have different answers here. So I don't want to speak for everyone, but I could say for our team, we can't respond to all the cold inbound that we get and so most of that stuff doesn't get responded to. Like if someone pings me on LinkedIn and is like, hey, check out my company, I don't know, I get hundreds of those a month. So it's not possible to kind of go through it. The absolute best path is to build a network, right, to start meeting people that are like minded, that understand what you're doing, to get to know them, to build a relationship, to ask them who are the one or two people that they think you should meet and to start kind of building into that. Like, take it from me, I knew nobody when I moved to Germany three years ago, literally zero.
01:08:19
Steve Jacobs
And very quickly was able to meet with hundreds of people in the startup and venture community just by having lunch with someone and asking, hey, is there one person you think I should chat with? So it's absolutely possible for anyone to do. And once you've built up kind of a critical mass in your own personal network of people who have gotten to know you, gotten to know what you're doing, and are willing and think it's a good investment of their time to connect you to someone. That's a good metric for you to gauge. Kind of how compelling your idea is and how good your communication is. If no one you're meeting is willing to make an intro, then there's probably a reason behind that too, and you should reflect on that pretty seriously. That could be a function of communication. It could be a function of the idea.
01:09:08
Steve Jacobs
It could just be the wrong node in the network. So building a network is a good reinforcement function. It's definitely a good investment. Like if you're a successful founder, you have a massive network that you can tap into for hiring, for future investments, for all sorts of things for customers. So I think especially in the technical community, it's a very underrated use of time and skill set that I would only highly recommend at any stage of one's career, especially to people who are interested in entrepreneurship.
01:09:42
Ana Koller
That's a great summary for this answer. Thank you, Steve. So we are running out of time and there is still a lot of questions. So what I would suggest is that we do an asynchronous AMA with the questions that remain whenever you guys have time, and then I will just kind of put it in the show notes that we publish after each of the events. But that still means that I would like to use my time to ask my closing question, and that will be to all three panelists. What is one piece of advice or one kind of key takeaway to early stage, deep tech founders and research spinoff founders to make their VC contacts bulletproof and make their venture bulletproof. Who wants to start?
01:10:47
Steve Jacobs
Happy to go first. I'll go with the last thing I said. Build a network. Invest in your network. Like carve out, I don't know, 10 hours a week. And if you're actually trying to be an entrepreneur, carve out 40 hours a week on top of the 40 hours a week that you're doing other things. And invest in building the network. Reach out to people, find entry points and then work with those entry points to expand it as rapidly as possible. Try to meet with, I don't know, a few hundred people every year that you don't already know and then nurture the relationships that are kind of the most valuable to you. Like your network is one of your biggest assets. It's way easier to successfully found a company with a big network and no idea than it is to found a company with a big idea and no network.
01:11:34
Ana Koller
Thank you, Paul.
01:11:37
Paul Klemm
Yeah, and I guess that is pretty much what I had thought out. But networking is a sales job. We receive many no's, I don't got frustrated by it, I guess. Obviously also receive notes when reaching out to founders. It's just part of the game.
01:11:55
Ana Koller
Thank you, Dave.
01:11:58
David Meiborg
Yeah. I think what is super important as a founder and what we also very much value in founders is if they have a very high level of self reflection on a personal level, but also on a business side. So when we are talking about founders internally, we like to use the word superpowers. And I think it can be very critical for founders to have an understanding of what their superpowers are and what not and what critical factors they maybe need in a company to complement them, what skills they have to develop to a certain hygiene factor to just have a base level of them and on what skill levels they have to double down. I think that also applies to various that can be abstracted to various business functions and product functions as well. And I think people who have this level, like a high level of self reflection, usually good leaders and can build interesting companies.
01:12:59
Ana Koller
Thank you, Dave. Thank you, everyone, for joining the three panelists for the great discussion and invaluable insights and for the audience for the great questions and patience. So thanks all and have a wonderful evening. Until next time. Ciao. Bye.
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