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About Tanya Ramond

Dr. Tanya Ramond resides at the intersection of science and engineering; business and entrepreneurship; and investing & mentoring. She earned her PhD in quantum physics from the University of Colorado Boulder and her postdoc from the National Institute of Standards and Technology.

Her technical career spans roles in aerospace systems engineering, engineering management and product development. As her passion morphed from technology creation to technology adoption, her interests pivoted to the business space. She earned her MBA from the University of Denver and moved into senior product strategy roles in various startup companies in aerospace, laser communications, and quantum technologies.

Dr. Ramond offers an investor lens, as a reviewer for startup grants at the state and federal level and mentoring for Techstars and various other startup accelerators. She is the CEO of Sapienne Consulting which specializes in commercialization and product strategy in deep tech companies.

Episode Highlights

Why commercializing deep tech is challenging
The one mindset shift technical founders must make to succeed
How to leverage the biggest element of your technical education to drive your startup’s success
A simple approach to mastering new skills
How to overcome the lone wolf mentality and activate progress
The secret to finding the best market for your innovation
How to bridge the gap from lab to market
Insider tips to successfully cross the “Valley of Death”
How to design successful pilots for funding and traction
The trick to engaging with the right industry accelerators
How to build the right team for your deep tech startup
How to demonstrate traction to investors with your deep tech startup

Links and resources

Sapienne Consulting: Dr. Ramond’s consulting company
SBIR / STTR: The federal government’s research funding programs
Where to Play: A company that helps entrepreneurs and business managers identify, evaluate and prioritize market opportunities for their business
Luminate: The world’s largest business accelerator for startups with optics, photonics, and imaging-enabled technologies.
GrantMatch: Software-enabled service enabling founders to research, identify, and execute a winning grant funding strategy.

Interview Transcript

Shubha K. Chakravarthy: Hello, Tanya. Welcome to Invisible Ink. We’re super excited to have you here today.

Tanya Ramond: Thank you so much for having me.

Shubha K. Chakravarthy: I know we have a lot to talk about, specifically in terms of deep tech, but to get started, can you just give us a little overview of what got you into deep tech and how you came to do what you’re doing today?

Tanya Ramond: Well, today I’m an independent consultant and my company is called Sapienne Consulting. I specialize in commercialization and business strategy for deep tech applications. I moved into this area of work, I’d say, during chapter two of my career. So I’ll start with chapter one.

Chapter one was very technically based. I earned a PhD, and completed a postdoc in what was called atomic molecular optical physics. Today, it’s referred to as quantum with a capital Q. But after that phase, I moved into the aerospace industry where I helped design, build and test new remote sensing instrumentation.

So things like LIDARs and spectrometers, people might have heard of. My roles were things like systems engineer, engineering manager, and product developer. At this time, my interest started to move away from pure technical contributions and more towards the business side.

The catalyzing point for me was an assignment that I had, where I was asked to lead an engineering team in an internal R and D project for a new business pursuit. The direction we were given was essentially, “build us a widget”. In this case it was a type of LIDAR.

So I said, Okay, great. I know this team. We’ve worked together before. I know we can do this. We went off to determine exactly what we needed to build. And we came back with questions like, “Who is it for? Why do they want it from us? Why can’t they get it from someone else? How will they use it? What will they pay for it? What will they not pay for it?”

Because all this drove engineering design requirements: Do we shell out for the top of the line laser? Or is the middle of the line laser good enough for what you want. I mean, all of this drives things like cost, risk and schedule.

So that was very much the engineer mindset there. But it was the result of not having a business case for this pursuit. And we received just no guidance in response to our inquiries.

And that I couldn’t articulate at the time, but that never sat well with me. The fact that a company can approve funds for a project without a business case – that bothered me. So as I went on in my career, I saw that this “build it and they will come” mentality was less of the exception and more of the rule. And I thought there had to be a better way.

And of course there is. So that was the slingshot into chapter two of my career. My roles morphed away from pure technical more into product strategy. And I worked in a series of startups in aerospace and quantum technologies. I earned an MBA along the way and dove into the entrepreneurial ecosystem to learn as much as I could about best practices, commercialization strategies, innovation strategies, product strategy.

I also devote a good amount of time now to volunteer work doing things like mentoring startups, serving on grant review boards like SBIRs, that sort of thing. It’s been a bit of a journey.

Shubha K. Chakravarthy: So it’s fascinating to me. It’s clear that you came from a hard STEM, hard tech kind of background. And now you’re very much embedded in the startup ecosystem, so to speak. And in that mode of operation, from a deep tech perspective, what’s the most unique or challenging thing you see in terms of commercializing some groundbreaking technology into an attractive business?

Challenges in Deep Tech Commercialization

Tanya Ramond: There are many challenges that I would say are unique to deep tech. First a deep tech startup has to grapple with technology risk that just doesn’t exist for a traditional, tech startup or even a non tech startup. So if you were building a dating app, let’s say, you’re not worried about whether or not you can actually make the app.

You’re worried about, well, “Can I reach the right customers? Can I get traction?”. In a deep tech startup, you worry about all those things too, but you have to worry about, well, can I build this thing in the first place? Is it going to be the right format that a customer’s going to want to buy?

“Do I have to get around the laws of physics somehow?” Those are things you have to worry about in deep tech. And to mitigate technological risk, it simply takes more, a little bit more time. It takes more money and capex, because usually deep tech is very hardware-based.

And that necessitates a dependence on a key role for things like non-dilutive funding, like state and federal grants. Finding and applying for grants is a profession in and of itself, sometimes. It takes time and energy. And it’s also true that most investors invest in just funding.

Numbers wise, most of them invest in non deep tech, and the metrics are just completely different. You have to either find investors who do understand deep tech investing, and that’s in the minority, or else the burden’s on you to educate them, if you will.

Shubha K. Chakravarthy: And the points you made really struck home with me because first of all, you talked about technology risk. I assume part one is scaling, meaning that it may work in the lab and it may not work at factory scale. I don’t know if there’s a context risk too, meaning it works in the lab, but is it going to work in the grimy, for lack of a better word, the grimy world of the real world, like in factories. And this third point is about investors. But I want to start at the beginning. Say you have a scientist who has this innovative new invention, some new thing that they’ve just come up with. They have to make a transition, like you just mentioned this yourself.

You have to go from, “I’m an engineer, I’m a scientist” to “I’m now a business person”. What is the single biggest challenge you’ve seen in the founders that you worked with, in making that transition?

Mindset Shifts for Technical Founders

Tanya Ramond: Yeah, I think probably one of the biggest challenges for a technical founder is moving from a very technology-centric mindset to a customer-centric mindset. So in school and graduate school, for example, you learn to pursue research that’s interesting for its own sake or because the technology is cool, from its own merits in business, the mainstream market.

And customers couldn’t care less about how cool the technology is. They need to just solve their problem. And so it’s very humbling for someone who say, finishes a Ph.D. and emerges as the world’s expert in whatever it is, a very niche area typically. But now they have to realize that there is much they don’t know, and that they have to learn. They have to start from scratch when it’s talking about who might need their technology and the people, the market segments that might need their technology. That becomes, that’s hard.

Shubha K. Chakravarthy: Yeah, so I think what I’m hearing you say is you’ve become used to being the expert and the go-to person in something, the person that everybody else turns to for answers, and kind of going back to beginner status where you don’t really know much other than your technology, which is now this much and not this much of what you’re talking about.

Are there examples or, maybe approaches that you’ve seen that worked very well to get over and make that mental leap and get comfortable with that level of uncertainty and lack of knowledge?

Tanya Ramond: Yeah, I think, a couple of changes in, in perspective, I think that help. So one is that you want to think more broadly about the skills you acquire in your scientific degree program. So all scientists learn the scientific method. This means that you form a hypothesis and you go out and gather data to test that hypothesis, right?

That data is used to either prove or disprove your hypothesis. Then you refine your understanding and gather new data. So it may say, nope, you’re completely wrong. You have to go in a different direction or, yeah, you’re onto something. Let’s tweak this a little bit, right?

Business is no different. Technical founders have to understand that their assumptions about who’s going to want to use this, who’s going to pay for it, are just that. They’re assumptions, they’re hypotheses, and you have to do the same thing, you have to go out and test them, test these hypotheses and retest them, just kind of generalizing a little bit those skills that you learn in graduate school can help with that transition.

Shubha K. Chakravarthy: So it sounds like staying in the same skill set but learning to get comfortable using them in a different domain, i. e. in business. And perhaps become comfortable with a little more, fuzziness around the results versus what you’re used to in your engineering or deep tech field. Is that a fair characterization?

Tanya Ramond: You do have to get more comfortable with uncertainty. I think, especially in say, let’s just say physics, right? You learn to design your experiments to control for everything. You put these things in chambers so that you don’t have effects from the outside world.

You want to isolate as much as possible so that you can just test one small behavior in the absence of any other context. Business is not like that. Human systems are not like that. You can’t isolate that way. You have to get comfortable with a little bit of uncertainty, a little bit of messiness.

From that standpoint, you’re not going to find perfect equations that describe everything. So thinking about it that way sometimes helps.

Shubha K. Chakravarthy: Is it just a matter of getting used to the discomfort of the fuzziness? What is the actual process by which you as a deep tech founder can get to that place where you get comfortable with that level of uncertainty?

Tanya Ramond: One thing that always helps me is having something called a growth mindset. This is an idea I think coined by this – there’s a psychologist called Carol Dweck. She’s a Stanford psychologist. She’s written this book called Mindset in which she talks about two groups of people, one who are fixed mindset, one is growth.

Fixed mindset says that basically, the way you look on paper determines your worth. Essentially, okay, what degree did I get? What school did I go to? What titles have I held? How much money have I made? All of that should, in that world, lead to certain levels of success, certain levels of authority, whatever it is.

Growth mindset is the opposite where you’re just a learner, you’re just a student in this world, and you’re free to pursue the chance to understand any new question. It doesn’t matter what your background is or what you know about something. It’s the fact that you just want to learn.

And that may mean that you start from zero and in some particular area. That may mean that, wow, you realize how little you know in one area, but it’s great to keep learning in this area. You have to have a passion. I think anything entrepreneurial, no matter deep tech or not, you benefit from that kind of a mindset.

It’s not so much what you don’t know, but the fact that you’re gaining knowledge as you go, I think, is its own reward.

Shubha K. Chakravarthy: I love that. I love that. I love Carol Dweck. So there’s one other concept that I’ve heard pop up in the context of deep tech, which is this idea of being a lone wolf. You’re typically the only expert in your field, no matter how narrow or broad it is.

You’re used to being on your own. You’re used to not being questioned. How much of this is an issue, once you move to commercializing your technology, and what should I do as a technical founder to overcome this and learn to work with others?

Tanya Ramond: It’s a good question. In graduate school, again, from my perspective, say a PhD program, you learn to be a one person show. You learn to be very self-reliant. So if something’s broken, you have to go learn how to fix it, and move on with your experiment.

Or if something, say a vacuum system is broken, you learn enough to be able to fix it. It doesn’t have to be an elegant or robust solution, but just enough to keep moving forward and gather enough data to publish your paper. Well, when you move into the business world you can’t be a one person show anymore.

You have to form a team because you can’t do everything yourself. Knowledge starts to get a little bit more compartmentalized in people. So you have to understand that there are people who are experts in electrical design, experts in vacuum systems. Although you may have a general concept of how that stuff works, you have to defer to subject matter experts to be able to operate at the level that you need in business because, in a PhD, it’s very much, I call it bubble gum and duct tape mentality.

You just have to do the minimum to be able to hold everything together for the time you need to take the data and move on. When you’re designing a product, for a business endeavor that doesn’t cut it. You can’t have a pizza box with wires coming out of it. You have to have something that’s the right size, shape, the right amount of power to the right amount of weight, all of that, to work for a customer. And that takes just a completely different mindset and it takes different skills that you can’t find in any one person.

So you have to be okay with that.

Shubha K. Chakravarthy: Which gets back to that growth mindset that you talked about. Just having that makes it easier to get. So you talked about a few differences between the world of deep science and the world of business. Have you seen among the founders you worked with, is there like one single biggest misconception that tends to trip them up the most or that hampers their progress the most?

Tanya Ramond: I’ll go back to this concept of changing mindsets from technology-centric to customer-centric. Technical people, a lot of the time, they just have a hard time understanding that, wow, this technology is so cool. Everybody’s going to buy it, or everybody’s going to want it. Everybody’s going to plunk down money for it.

It doesn’t work that way. Yes, there are people, probably more on the side of early adopters, who may buy one because it’s cool technology and they want to play with it.

But if you want to break into a mainstream market, mainstream customers couldn’t care less about the technology. They are not going to buy it. They’re not going to fork over their money unless it solves a problem for them. That’s very humbling. It’s like calling your baby ugly.

It’s a little bit disheartening to learn that but then you have to get past that and say well, okay, could this solve a problem for someone, what would that be? And you have to think about it that way.

Importance of Customer Interaction

Shubha K. Chakravarthy: So how do you develop that muscle capability? In theory, I can understand what you’re saying, but I still have to make it happen in the real world. What are some things you’ve seen founders do to get there?

Tanya Ramond: This is one thing that pops up from time to time. A lot of the time, the technical founders will think of the business side as something they can outsource, something that’s separate from them.

And that’s not the case, at least not at the start. At the start, the founders have to go out and talk to the potential customers in person. They cannot outsource that. Why is that? That is because you have to understand very intimately, if the pain point you think you’re trying to solve resonates, and you have to do this in person is very important because body language and the reaction is important.

So in other words, if you talk to somebody about a problem, and they’re very, maybe they’re just very monotone about it, even keeled about it and there’s really no animation, not really much body language about it. Maybe they think, yeah, okay, this is a problem, but maybe it’s five years down the road. It’s not something that I worry about today.

But on the other hand if you’re talking to them about something and suddenly their eyebrows shoot up and their eyes get really big and like they’re waving their hands around, that’s important information. That’s important you’ve tapped into something there.

That’s a pain point right there and that’s an urgent problem that they’re trying to solve now and that they probably have money to solve now. That’s the kind of problem you want to be solving. And that’s not something that you get unless you actually go out and have these conversations with people.

You run a really huge risk by saying, Oh, well, I’m going to outsource that to someone, to the business person in the company. No, you can’t do that. If you’re going to lead the company you have to understand that customer intimately.

Shubha K. Chakravarthy: What I’m hearing you say is, I don’t care how deeply technical you are and how great of an asset you are from a technical standpoint to your startup. I don’t care if you have a business partner, CFO, or even a CEO. But you, the inventor, need to be out there listening to customers is what I’m hearing?

Tanya Ramond: Yeah, if you’re leading the company, if you’re the CEO, you have to be doing that. You cannot outsource it. So if you want your role to be, okay, I’m a technical advisor to my company, and then I hire a CEO to lead the company, okay, they can do that.

But if you’re the technical founder, even if you’re the CTO, anybody at that high level needs to be intimately connected to the customer and their pain points, to be able to understand what to design, what to offer to that market.

Shubha K. Chakravarthy: So there’s a critical point here, you’re making a call about, Hey, listen, what’s my involvement in this company? To the extent that I have any line, or I’ll call it a line responsibility, I could be a CEO, I could be the chief technical officer, chief product officer.

But if I have a line role in the company, and if I’m not an advisor, I mean, I’ve crossed the line into being part of the company. I’m hearing you say that you need to be eye to eye with the customer. You need to be understanding at a visceral level. What is it that you’re hearing from them? What are you feeling from them in terms of that need and that urgency?

Tanya Ramond: Absolutely. And if you don’t feel comfortable doing that, then you should not be in that role.

Shubha K. Chakravarthy: I love that point. So the risk is, I’m going to push you on this a little bit. So the risk is, I’m a technical person. I’m happy in my lab. Okay. I know my technology. I know it like the back of my hand. And my tendency is to go out and talk to people like me, maybe other nerds who get it and who have some kind of a business connection, let’s say.

How do you suggest that founders or, these technical founders force themselves to find who these right customers are? What is that granular process? What is that tactical process by which they find the customers? Because if I take a technology, I could have five different applications here.

I talk to founders who could be selling to three completely different markets. This happens a lot in cleantech, for example. So how do I then pick the right customer and then go talk to these people and prioritize it and find the exact person within a large organization that I should be talking to?

Tanya Ramond: Okay, so that’s a good question. So there are a couple things there that you talked about. One is, so say you have maybe three markets you’re looking at? You want to down select. You don’t want to be pursuing multiple markets at the same time.

That’s a different conversation, but like, say you have a market that you’re targeting, how do you figure out who to go talk to? My advice is to find the end user. The end user is the one that’s doing the work, that is experiencing the problem first hand.

So you have to figure out who those would be in a different organization. So if it’s a very large organization, it’s probably not the CTO, it’s probably not the CEO. If it’s a small organization, it could be. But, it depends on what problem hypothesis you have, figure out who would be experiencing the problem, but who would benefit from your potential solution.

It just depends on what the application is. And then you have to figure out, okay, well, what is that person? What’s their job title within an organization? And then you go from there.

Shubha K. Chakravarthy: That makes sense. Is there any other method or technique or approach or behavior that you’ve seen that allows somebody with just technical acumen to develop more what I’ll call business acumen to be more successful with their startup?

Tanya Ramond: So we talked about the growth mindset, which is great. There are things that you just don’t know, that always helps, relying on your subject matter experts. It is very typical to say if you’re the CEO, but you’re a technical founder. Maybe you have a business development person who is more on the business side. You can work with them. You don’t outsource everything to them, but you use them to help you on whatever it is, find those resources that can help you. And, it’s not something that you’re going to develop overnight either.

It’s like anything else. It’s a muscle that you have to exercise and it just goes from there. There’s no reason why that can’t happen. You’re a technical person and you know how to do it. You deal with complex subjects. In principle, if you have the right mindset, you can do that.

Shubha K. Chakravarthy: I have one last question on this topic of transitioning personally from scientist to entrepreneur, and then I have some other fun stuff to ask you.

So the first thing, women obviously are a rarity, or at least they’re a minority in deep science and deep tech and STEM. So you’re already kind of going against the grain a little bit, if you’re a woman who’s come up with some amazing new technology, what have you seen in terms of challenges women face specifically?

Outside of just general deep tech founders. And what have you seen work well for them to get over these challenges and still be successful with their startups?

Tanya Ramond: I don’t profess to understand why, but what I’ve observed myself is a big stumbling block is less about securing a technical position than moving into more leadership roles and managerial roles. Someone else may have a different experience, but, those individual contributor positions are, I won’t say it’s not problematic, but it seems like it’s less problematic than moving into management.

I think there’s a lot of zero sum thinking when you come into managerial positions, but, also in startups, you don’t have the same protections that you have that are more common in larger organizations. I think sometimes that can, that can be missing.

Empirically, anecdotally, I think sometimes it’s almost easier to start your own company than it is to kind of work your way up, I think. But the other thing too, that I have seen from my standpoint, one of the loveliest and most treasured threads of my career is the network I have fostered with other women working in this field.

In an absolute sense, there are not many of us out there, but when they are there, we seek each other out. And so from that sense, there are lots of us, because that’s who we look for first. I feel like we trust each other. We rely on each other and we value each other. It’s invaluable.

And I almost feel like there’s almost a, unfortunately, I think there’s a selection effect. That if you’ve made it to this point, these people are high quality and I think that comes through. So I feel very fortunate to have just a fantastic peer group of women in deep tech that I rely on all the time.

Shubha K. Chakravarthy: And I would imagine that if you were, for founders too, that would be a pretty invaluable resource because they have other women who are familiar and can talk the talk about deep tech with them. And so are there resources that you or groups that you would recommend or point out that other women founders in deep tech can access?

Tanya Ramond: There are various professional societies, and most of them do chapters that are, women in whatever it is. So women in optics or women in, engineering so those can be really good. Some organizations are better than others in, putting women on panels.

I still see so many panels that are for guys. But when you do see women on a panel, reach out to them and just try to connect because I do a lot of that. And people do that with me. And it’s another way to build that network. So that’s not a formal organization, I’ll say, but that is another way to do it.

I do a lot of that through LinkedIn as well. Not only who’s giving women a chance to have a voice, but what platforms. keep an eye out for that.

From Lab to Market: Engineering and Manufacturing

Shubha K. Chakravarthy: Changing gears slightly, this is the other interesting thing, which is, at least from an investor standpoint, like you yourself mentioned it’s challenging to take deep tech from lab to market. So I want to talk to you about taking a regular technology versus taking a deep tech technology from lab to market.

What are the biggest challenges? You alluded to them a little bit before, but can you just maybe talk a little bit more in detail about what are the big barriers that a deep tech startup has to face before they can commercialize whatever it is they’re bringing to market?

Tanya Ramond: I would say the transition into an engineering approach, and then another one would be a transition to a manufacturing approach. Going back to what you get, especially when you start out in a university lab or research lab, again, you’ve got something that’s a pizza box with wires sticking out of it, just not ready for a commercial market at all.

And like I said, the mindset’s very much around bubble gum and duct tape, whatever you need to make it work for the 10 minutes that you need to get the data. Obviously for a product, that doesn’t work. So what you need to start incorporating is engineering.

The people who are trained in engineering know how to design something. So it’s going to be robust. So it’s going to be rugged enough. So it’s going to be able to withstand whatever the temperature range is that you need to work over so that it’s small enough so that it’s whatever it is.

When you get a PhD in physics, for example, that’s not a mindset or that’s not a skill set that you learn. But it’s something that you have to transition into. Again, that’s a question of bringing in your experts, bringing in people outside of your expertise to guide you in that process.

But there’s also a difference between, okay, say you’re engineering something for maybe small volume manufacturing. But if your market is, large volume manufacturing, say for the automotive industry or for, consumer appliance or whatever it is, that’s a whole different beast. Because now instead of just, maybe I make 10 per year or 100 per year, I have to make thousands, if not millions of whatever it is.

And that’s a whole new set of constraints and a whole new set of skills that have to be incorporated. Especially like in clean tech, for example. Like, you want to build a new battery for a car? They’re gonna buy these in mass, right?

So it has to meet a certain price point or like for example automotive lidar. That requires a lidar system that has to be under a certain price threshold. And the focus has to be on that as opposed to maybe, improving the performance by a certain amount. It’s a very different skill set.

And so you have to bring in that manufacturing mindset. Those are quite removed from just what you learn in a lab environment.

Understanding Your Market

Shubha K. Chakravarthy: So this is a tall order. I mean, you’re completely focused on making this little piece of technology work. So I have two questions for you. First is I have to know that I don’t know those things. And then secondly, I have to know what to know and then go find them. What is the typical or effective approach you’ve seen where somebody with who’s maybe a pure PhD with this fancy new whatever widget it is that they invented, what works to get to that level of awareness and then get access to those kinds of resources?

Tanya Ramond: First of all, knowing that there’s a lot you need to learn is the first step. And then now you have to just go out and expose yourself to various people, go to conferences. If you’re targeting a certain market, go to conferences about that market to understand, what are people worried about?

What are the constraints, what’s hard, what’s not hard. And then start talking to people. This is another reason why you want to talk to people as much as possible and say, look, this is the problem I think I want to solve – what’s been tried before?

What has failed? Why has it failed? What could be easy now because of some new improvement in technology? That’s just a lot of, it’s not just research at your desk. It’s also research, but it’s called primary market research talking to people.

Shubha K. Chakravarthy: Steve Blank says, get out of the building, right?

Tanya Ramond: Exactly

Forming Hypotheses and Validating Problems

Shubha K. Chakravarthy: So you have this technology, you go to all these conferences. Now you start to form a couple of hypotheses where I could either deploy it into this market to solve this problem or into this market.

So you make a call based on whatever objective criteria you’ve got, or maybe you have contacts into the industry, whatever the case might be. So then what happens and where are the pain points in that process of getting from this amazing technology to something that it can actually sell and make money off of? Are there critical points in that process?

Tanya Ramond: Yeah, there are different steps along the way. So the first step is you want to validate that you’re solving a problem that people care about. You never stop validating that. You’re always validating that. But then there’s some work you need to do to understand your customer and develop a persona around them.

And starting from an end user perspective, what are their motivations? What did they get punished for versus what did they get applauded for in their company? And then you have to think about the workflow that this product would plug into because you want to design something that has the minimum amount of disruption to what they already do.

Because if they have to completely retool everything and completely redo all their processes to use your product, even if it saves them some money, that other stuff is going to cost them money and they’re not going to do it. The closer you can get to what they already do, the better your chance of building something that will be adopted.

And adoption means traction, it means money, et cetera. So that’s a big part of it. So once you understand the end user, then there’s everyone else in the company who would be involved in a purchase. Usually the end user is not the one that will sign off on the sale.

Who is that? And what are their motivations? Who else in the company has a stake in this? Because sometimes there are people in the company who will make it hard for you to sell to them because they perceive your product as a threat to whatever they’re doing.

Shubha K. Chakravarthy: Any examples that come to mind?

Tanya Ramond: Say you’ve got a product that’s going to make something more efficient, or even eliminate some process. Well, if you’re somebody whose only job is to do that process, you don’t want that, right? so that happens. if you’re just comfortable with the status quo and you just don’t want to do anything different there’s not a lot of motivation or reward system to do something different in the company, then, why do you want to do it?

So these things are real and these things happen. This is why understanding your customer is important because there may be two customers where you have the potential to solve the same type of problem, but one is more averse to innovation. One is more friendly to it.

Take the friendly one. So there’s that aspect of it too.

Navigating the Valley of Death

Shubha K. Chakravarthy: I’ve heard this other term that I want to ask you about, which is, I’ve heard about this valley of death between research and commercialization.

Can you talk a bit about what is that valley of death? Why does it come about? And how can founders get over that valley of death?

Tanya Ramond: Valley of Death, I think, means different things to different people, but I think in general terms, in this context, it’s the fact that, in deep tech, there’s a period of time where you have to mitigate your technology risk before you can really have anything to sell.

And that means well, how do you fund that effort? This is why the role of non- dilutive funding is so important in deep tech, which is usually government type of grants and SBIRs. They play such a huge role in getting technologies to market.

That is one of the general concepts of the valley of death. And then the other side of it is, okay, then you get to a maturity level, you get to like a technology maturity level, but also a market traction level where somebody might actually invest in you now.

So your funding sources can start to come from other places like VCs, angel investors, that sort of thing. So the Valley of Death, I think, is kind of in between those two areas. You want to extend your non-dilutive funding into the other region as much as possible, to bridge it.

Funding Strategies and Pilot Projects

Shubha K. Chakravarthy: So I’ve actually seen quite a few founders who are kind of in the middle of the valley of death because the technology is proven and there’s somebody standing on the other side saying if you can get it to this level of scale and prove it to me that it still works at whatever 10x the scale that they’ve proved at lab scale, then I’m all in, I’m going to order XYZ. Often it’s also the VC who’s willing to say that because then they’ve got validated demand, but my SBIR or whatever gets me only this much.

What have you seen working for that part where I have to get from 1x at lab scale to maybe 10x at even a pilot factory scale?

Tanya Ramond: Yeah, it’s a really good question.

Sometimes people do more than one SBIR, for example. You can string those together a little bit. You can’t do the same one over and over again, but you can maybe address a different aspect of the technology. And there’s just different grants that you can go after as well that are not just SBIRs.

You can also do an accelerator, for example, because these different accelerators will actually invest in your company. One example is Luminate, which is an accelerator in Rochester, New York, and it’s specific to optics, photonics, and imaging startups.

At the end of the cohort there’s a prize for, I think, top three companies there. That’s an example. You can also try to find partnerships or pilot programs. So pilot projects with potential customers where you can say, “Look, here’s my vision. Here’s where we’re at. Here’s where we want to get to. And, can we set up a pilot project so that we can work with you to understand what the use cases are and how the workflow goes?”. And they fund that. And then if it works, then they get a cut of whatever the product is or whatever the agreement is.

Shubha K. Chakravarthy: So you mentioned some grants in addition to SBIR. What are good sources of those grants that are not SBIR that founders can access?

Tanya Ramond: Yeah, it’s a really good question. There are various tools actually that are out there just to search grants. There’s one that a colleague of mine just recently launched called GrantMatch, for example. There are a lot of grants out there, I can’t tell you like any one specific source, but there are ways to search and find ones that pop up, and of course there’s SBIRs through different agencies, so maybe you have something that the NSF would fund, but maybe the deal is you would fund it, too.

So you can play around with that. So there are grants strategies as well here in Colorado where I’m at. The state of Colorado funds basically their version of an S. B. I. R. So it’s a really fantastic program and specific to deep tech because they realize that these are challenging products to get to market, and they need a little extra boost. So, a great reason to move to Colorado.

Shubha K. Chakravarthy: Nice plug.

Tanya Ramond: Yeah. But you know, these are the kinds of things that are out there.

Shubha K. Chakravarthy: One other question on the pilot project angle. Which is, clearly there are certain industries which have a bunch of money that they don’t mind making bets on these kinds of technologies because that’s kind of where their next big thing is going to come from. Are there principles or approaches that you’ve seen that work well to get these kinds of pilots?

Tanya Ramond: I think with any customer, the more you can understand them and really understand what their problem is and what they’re trying to solve and what their motivations are, the better you’ll be able to talk to them and approach them and tailor your approach to them. With a pilot project, a lot of people sign up for pilot projects where there may not be any money exchanged there, maybe it’s just okay, we’ll do this for free for you.

And then, whatever the agreement is afterwards, you should always try to, there should be some money exchanged because it’s a form of validation. So two things, it’s a form of validation. So if they’re not willing to put skin in the game, then you have to wonder, well, do they really care about solving this problem?

If they do, it means that it’s a real problem for them, and they’re interested in finding a solution. But also it’s a way to test your pricing strategy. For example, if you say, okay, I have a hypothesis about what the market will bear in terms of how I price this product.

In this pilot project, I’m going to say this is what it would normally cost. I’m going to red line that for you for 50 percent of that. And you can use that to get feedback. Okay, well, What do they think about that?

Do they choke on that? It’s also a way to say, okay, when this thing works after the pilot project, this is what I’m going to charge you. So it’s a way to get some validation for just that pricing model.

Shubha K. Chakravarthy: And those are great points. Are there other strategies or stipulations that you see worked into pilot projects for deep tech that get them maybe not just money, but somehow help them advance their paths to commercialization?

Tanya Ramond: Yeah, this is why picking your pilot customer is really important because and this goes for strategic investors as well. You want to make sure that who you’re working with is in your road map, your market road map. Because what they bring to the table is the market understanding is needed from your product to make it attractive.

How will it be used? What are the constraints on it? What are the must haves versus the nice to haves? So a well chosen pilot partner will be able to give you that. And then that gives you a pathway into more customers in that market sub segment.

But if it’s just okay, someone approached me to do a pilot project and okay, maybe we’ll try this market out, it’s less potent, because, you just don’t have that plan to pursue that market per se. So you’re not going to get as much out of it.

Engaging with Industry Accelerators

Shubha K. Chakravarthy: So it sounds like, the way you’re funding it, the way you’re validating your customer need and your ultimate go to market strategy all have to be really tightly integrated together. And the more they speak to each other and work with each other, the easier your life is going to be and the easier it is going to be to get that investment down the line when you’ve proven your technology and made the inroads that you do.

I have a couple of questions on that.

I’m seeing more and more accelerators being run by industry giants in specific industries. for example, like the Shells and the Marathons of the world, if you’re talking about clean energy, and I’m sure there are others in other deep tech. Do you have a point of view on how they work and how founders should view these accelerators to advance the technologies.

Tanya Ramond: Yeah, it’s interesting because many of these really big companies, their innovation strategy is maybe less to do it internally, but more to acquire that particular technology or that particular capability. From that standpoint, it can be good because that can be a potential exit for that startup, like if Shell is running an accelerator, then that means you start a relationship with Shell and if they like what you’re doing you could potentially be bought by them. That’s your exit. So it can be a good pathway.

It can be an excellent choice. Same as with institutional investments. So, if Shell has a venture arm, for example, should you take investment from that then? Well, if your market roadmap is to go into oil and gas absolutely.

But if your market road map says, okay, oil and gas is kind of a tangential market. Don’t do it because what’s going to happen is the center of gravity is going to shift over to oil and gas because that’s where their influence is. That’s where their customers are. That’s where their interests are. Especially if you’re early, it’s going to be really hard to shift out of that.

So if you’re ready for it, great sign up for it. But if that’s not the direction you think you need to go, then think a little bit more about it.

Shubha K. Chakravarthy: So it sounds like the, they might make sense as long as that industry is squarely in your crosshairs from a go to market strategy

Tanya Ramond: Yeah, exactly.

Convincing Investors and Building Traction

Shubha K. Chakravarthy: One other question on that. Since you’re talking about funding, I’m just curious, founders need to make a case to investors to invest.It feels like it’s much easier to make an investment in a high margin, low risk business like software or maybe AI today.

Deep tech is always going to be challenging for all the reasons that you talked about. Right now you’re sitting along the side of the founder and you’re helping them bring their technology to market.

What have you seen work well for a founder to first of all make investors understand what exactly they’re doing and what’s unique about it? And how they can get them excited about the potential enough for investors to consider and say, okay, fine, let’s have a conversation.

Tanya Ramond: Investors want to see traction. And traction for a software startup is different than traction for a deep tech startup. So for a software startup, by a certain point you want to have, I don’t know, 100,000 subscriptions or whatever it is. For deep tech, It might look very different.

It might look like, “Hey, I have a pilot with Shell”, for example, which is a huge feather in your cap. Or I have a government contract with the DOD. So that might be one, but it’s still a big one. So traction looks a little different from a deep tech standpoint, investors want to see that sort of thing, especially if they’re used to working in deep tech.

I remember hearing about one investor in aerospace who talks about companies that are phase three-able. What he meant by that is, if you’re familiar with SBIRs, they go in phases. So there’s a phase one, which is basically a small feasibility study. Phase two is a prototype build. And phase three is basically where you get contracts.

So that’s what he meant by phase three, both something that can get beyond prototype and get commercial traction. They’re looking for that traction, but not just, saying that I have X contracts or I have these grants.

And I think also, depending on the stage you’re in, SBIR awards are forms of traction. I think also what’s really important is to understand why. Why are you getting these awards? Why do people want to fund you? It ties back to what problem are you solving for whom? And not just what that looks like qualitatively, but quantitatively.

So if you can paint a picture of, yeah, because of my product, Shell can save X number of dollars per month doing this, or maybe it’s not dollars, maybe it’s, they can avoid, X number of hours in compliance work for a year or whatever the currency is that speaks to the particular customer. But quantifying it, having that understanding is really powerful too, because that’s something an investor can look at and it’s very tangible.

It’s tactical and they can turn that into “okay, I have a feel for what this could turn into if it works out”.

Shubha K. Chakravarthy: That’s an excellent point you brought up. So some of that could come from the pilot studies too, right? If I can prove to you that I have improved your efficiency by X or reduced by cost by Y or whatever the case might be, that’s a real number. I can extrapolate to a larger market and say, hey, look, I did it for Shell and imagine I can do it for the whole world.

My question to you is, if I’m looking at it from the point of view of the founder, I’m trying to get to the point of, I’m trying to connect the dots from where I am today to getting an investor excited.

So I’m just going to play back what I heard, which is first of all, understand where your technology has potential. You do that by getting out of the building. You go listen to all these people and the potential deployment markets that you can get into.

Then you figure out which is your best bet, either because it’s a friendly market and they’re less averse to change. And/or, they might have less to change, and yours is an easier integration to do.

And/or, are they have more to gain or more losses to avoid by adopting whatever it is you bring to the table. Okay. Check that box.

Now, your next question is to say, okay, what technological risks do I need to prove or de-risk for me to make a case that this could become something big.

And from there, I heard making sure that you’re getting the engineering skills, making sure you’re getting the manufacturing skills and proving the case that this you can actually deliver at the scale where this could be exciting to an investor. That’s like the next big step.

Then the other big step I heard was, once you’ve done all of this, the big thing that matters is you actually need to go find a market and actually need to prove that this actually works in the real world.

Which gets to the point of validating that the market choice that you made in the first step is actually still going to work because here’s a customer who gave you XYZ. And to be able to convince them with whatever technological proof you have, what picture you can paint for them of what might be possible for them and get that kind of traction.

Once you have all of these things, while you’re also trying to solve the funding puzzle through SBIRs or this, whatever patchwork quilt you can get between grants and accelerators and whatever else, industry accelerators might be the case.

You have to bring them all to come together in the same way, in the same time, in a way that makes a compelling case to an investor that says, Hey, not only is there opportunity, but I have the means and the skill to be able to pull it off and look, it could translate into really big money for you.

That’s the totality of the picture that I’ve heard from you so far. How does that land with you? Does that sound like a fair characterization?

Tanya Ramond: Yeah, I think so, and there’s kind of a hierarchy of traction, when you’re really, early, maybe it’s a letter of intent, like somebody will sign a letter of intent, hey, if you do this, I’ll buy this, right, but that’s not as good as, well, maybe somebody’s paid me for a pilot project, but that’s not as good as, the ultimate traction is sales.

And you can’t do sales right away.When you’re a software startup, you can. And you get judged on that, but you can’t do that as a general rule for deep tech. So there’s traction and then you say, you have to have this path to the scale that you need to produce a return, essentially.

And maybe you don’t have all that figured out yet, but you have to have that plan. Sometimes that means, well, talking about the manufacturing aspect of things, maybe that means you bring in someone to your team who is an expert in manufacturing for these kinds of products, and maybe you don’t have it all in place, but here’s your plan for, okay, this is the facility that I need, or this is the services that I need, and these are the, this is what the materials cost will be, and this is the cost of labor, and this is how I can get to X number per month that I need to produce the numbers that I need. So short of actually doing it all, you have to show that you have that plan in place.

Shubha K. Chakravarthy: So, I’m almost hearing, I love the point you made about how traction changes at every step, especially for deep tech. So I’m almost seeing it as like a ladder with two big poles. One pole is the traction and one pole is the funding, and then you have these rungs that are stepping up and that’s what you have to hit in terms of funding and in terms of traction depends on which level of the ladder you’re at.

And you have to constantly keep ratcheting up both your funding access as well as the traction that you need to hit appropriate to whichever level of the, whichever rung you are on the ladder. That’s kind of what I’m seeing. So it’s a much harder and much different proposition than just a general tech software or any kind of startup, which is why all of these skills matter a lot.

Building the Right Team

Shubha K. Chakravarthy: I just want to touch on one last thing, which is the team.

Obviously you need a very diverse skill set, you need finance people, you need business people, marketing people. What have you seen, as the blind spots in terms to the extent that you’ve seen, with these deep tech founders in getting a team and what are some of the things that you’ve seen founders do well to find the right people to staff their ventures?

Tanya Ramond: As a general rule, I think especially technical founders don’t have too much of a problem with the technical team and hiring that, at least at the start. but like I said, sometimes the blind spot is really, oh, that I can outsource the business stuff. And I don’t have to get my hands dirty with that.

That’s one pitfall. One thing, and this is not necessarily specific to deep tech, but hiring salespeople too early can be a problem because if you haven’t yet figured out your customer and if you haven’t really done the work to research the customer and understand, what value proposition you’re bringing to them and what that looks like qualitatively and quantitatively, you can’t sell to them because you don’t know why they would buy.

You don’t know how to sell something if you don’t know what problem you’re solving, the salesperson needs to know this. So some salespeople can function in that way that they can help with that process and that discovery. But a general salesperson, they’re very skilled at certain things, but they need to have that information first, to understand, okay, who am I selling to?

What’s their motivation? What is the economic buying unit look like? Who has to sign off, they need all that stuff done ahead of time ideally to be the most effective. So I think I see a lot of, Oh yeah, we’re working on the technical stuff and then we’re going to hire salespeople.

And I’m just like, well, to do what, what are they actually selling? If you can’t answer that, then it’s not going to go too well.

Shubha K. Chakravarthy: Yeah, and I totally buy that. I’ve talked to quite a few investors. So like, if you’re showing me any plan with hiring any salesperson before the first 18 to 24 months, it’s an automatic no because you need to be out there. If you can’t sell it, then nobody ain’t going to be able to

Tanya Ramond: Exactly.

Shubha K. Chakravarthy: So I love that. I think that’s a great point.

Key Takeaways for Deep Tech Founders

Shubha K. Chakravarthy: So in conclusion, if I’m a deep tech founder in the trenches, I want to make sure I’m on the right track, what three actionable takeaways would you leave me with?

Tanya Ramond: We talked about getting outside the building. Get your hands dirty. Go out and talk to your customers. It can be uncomfortable or messy, but there’s no substitute. You can’t get around that. Second would be don’t fall in love with your solution until you understand, until you’ve explored that problem space.

Customer mindset, the problem space always comes first and put the solution to the side until you understand that. And then just learning to be able to function without perfect information, without 100 percent intelligence about whatever it is that you’re trying to decide on, learn enough to and then make a decision.

All your decisions are not going to be the right ones, but it should be close enough. And then, you iterate, it’s always an iterative cycle and you’re always going to have to take a step back from time to be able to go forward. That’s just how it is. But just keep that motion going forward.

Deciding How to Decide

Shubha K. Chakravarthy: Awesome. I love it. So you’ve been incredibly helpful and thorough in walking us through what it takes to bring a deep technology to market. Is there anything you wish I’d asked, but didn’t, that you think founders should know about?

Tanya Ramond: I guess one topic is, I talked about being able to operate with incomplete information. One of the skills I think that’s important is deciding how to decide. My background is systems engineering. And this is something that you learn as a systems engineer.

For example, you do a trade study. So you’re trying to decide between two fundamentally different architectures for a certain instrument. And that means that you kind of have to look at them both from a 30, 000 foot level, and then you make a choice. And then you dive in and investigate the one that you’ve chosen.

So don’t do all the way down to the ground level for both of them. Decide up here, you know what’s going to make the most difference between the two architectures, and then you dive in. That kind of mindset is really helpful.

Shubha K. Chakravarthy: I love it. So you talked about this, you’re learning to decide how to decide. I love that. And you talked about this 30, 000 foot view, and you talked about having a few key criteria that tell you which way you’re going to pick. Can you just give me a concrete example, so it makes it more easy to understand.

Tanya Ramond: So this is something I see all the time. And we’ve talked about this. the concept of pursuing multiple markets at the same time. This is very common. People don’t want to feel like they’ve left money on the table. They want to make sure they cover all their bases.

What if I choose something and it’s really the wrong thing? But the reality is if you’re pursuing more than one opportunity at the same time, you’re not doing any one of them justice and you’re not going to get the traction you need and it’s going to fizzle out.

So what do you do? And this is where you can figure out how to do essentially a trade study of your different market options. There’s a wonderful, entrepreneurial tool that’s called Where to Play, the market opportunity navigator. If you’re familiar with this, that goes through this process where again, you’re not diving down to the zero, to the ground level for every single market, but you’re doing enough due diligence up here to make a choice as to your target market and maybe a follow on and backup market and then you commit to it and then you move forward. It doesn’t mean you can’t reiterate, and moving forward means you dive into that target market. But the point is you’re not doing that for three different markets or five different markets.

You’re doing it for one that after a certain level of top level due diligence. That’s a process that can really help founders’ confidence that they’ve done some due diligence. They’ve made a choice and they can move forward with confidence that they’re going in the right direction.

Shubha K. Chakravarthy: That’s a great example. And a particularly important one in terms of where to pick your marketing, I’ve heard about Where to Play. We’ll feature a link to that as well, but in terms of how do you pick those, let’s take some company, you talked about a trade study. I’m not familiar with what that is, but how do you pick those two or three?

Can you give us an example where if I don’t have an existing framework to rely on, I have to come up with it myself? What guidance do you have or what example do you have in those instances of learning how to decide right.

Tanya Ramond: So I’ll just use this example of the Where to Play methodology. This is a method that was generated by two business professors, Sharon Tal and Mark Gruber. and this came out of I think Sharon’s Phd in business.

So she’s actually done research to back up these criteria.So the criteria they use are, basically, product market fit or, solving a problem for a customer. Then just the market itself, the size and growth of the market. Then the unit economics, like, are you going to make a profit at all?

Could you possibly make a profit at all? Then in terms of the implementation side, how hard would it be technically to bring it along? How long will it take? Because we know that time is money. If it’s too long, you’ll run out of money.

The last thing has to do with the external world. So things like, maybe government regulations or market trends or,, some kind of ruling that might make your wellness app go away. So if TikTok goes away, then you know your TikTok-based app is dead in the water. Something like that. For medical device, it could be FDA approvals. Something like that, that you can’t control. So you look at those. Those are in general terms, those are six criteria that can be used.

Shubha K. Chakravarthy: Excellent.

Thank you very much. This has been a really interesting and informative conversation. So I really appreciate you taking the time Tanya. Thank you so much. I know that deep tech founders will find a lot of value in it.

Tanya Ramond: You’re so welcome. Thank you for having me.