November 14, 2024 – In the second episode of TechnologIST Talks, host and CEO of the Institute for Security and Technology Philip Reiner sits down with Dr. Manish Kothari, founding managing partner at First Spark Ventures and former president of SRI International. Manish shares his journey from a biotech startup founder to a leading deep tech investor, offering insights into the unique challenges and opportunities within the deep tech ecosystem.

Manish and Philip explore the history–and cyclical nature–of Silicon Valley, the unique funding environment for deep tech startups, and the key factors that Manish looks for in a company specializing in deep tech when thinking about investing. “One of the challenges of deep tech relative to tech is scaling these quickly is challenging, because it costs more money. You’re still in the early development phase. We really want to make sure the underlying component technologies are mature.”

How would Manish assess the government interventions that have been made so far to accelerate American competitiveness? How can the United States maintain its competitive edge in the global tech landscape, especially in light of advancements made by countries like China? And how are machine learning and automation playing a central role in the deep tech renaissance? 

Join us for this and more on this episode of TechnologIST Talks. See the transcript.

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Transcript

Philip: I’m Philip Reiner, CEO at IST and your host today. 

Venture capital is a key driver of technological development here in Silicon Valley, particularly in fields such as biotech or quantum, otherwise known as “deep tech.” 

Our guest today is Dr. Manish Kothari, founding managing partner at First Spark Ventures and former president of SRI International. Manish has personally incubated and invested in over 25 startups, including co-founding Mytrus, a software startup that was the first in the world to use FDA-approved electronic informed consent. 

Manish and I sat down to discuss the deep tech innovation ecosystem.  

“Hardware, or biotech, or drugs. These are all deep tech. They’re fundamentally complicated to make. They require a lot of intellectual property, a lot of resources, and a lot of capabilities there. 

In the software world, you know, things are more nebulous. I mean, if you think about, let’s just take OpenAI as an example. I mean, five, six years ago, OpenAI was very, very much deep tech. It is transforming from deep tech to tech as we speak, right?”

Deep tech has exploded in recent years. What exactly contributed to the accelerated speed of innovation?

“In the past, you had to do small sets of experiments to understand the underlying mechanistic cause. Today, you can use AI to quickly get a sense of what the hypothesis or the mechanisms could be, and then apply those mechanistic approaches. It’s so fundamentally different than how science was done over the centuries and millennia before, that you can now accelerate the development and learning by an order of magnitude in terms of time.”

How can the U.S. government galvanize deep tech? What has China done to gain the tech advantage, and can we win it back? 

“We cannot expect to do things exactly the same way and catch up. We will not. What we do have right now is a renaissance in a couple of areas like machine learning, in robotics and automation, that are allowing us to maybe leapfrog past the conventional ways of doing things and go to a different level.”

Join me for this and more on this episode of TechnologIST Talks. 

Philip: Welcome, Manish. It’s really a genuine pleasure to have the opportunity to speak with you here today. So we can just, we can really just jump right in here. You have such an incredible background, as a biotech startup founder, deep tech investor at SRI, and now as a general partner at a deep tech investment fund. You’ve also spent time serving as an advisor at DARPA.

So I wanted to maybe kick this off for our listeners just to ask, you know, how did you get interested in deep tech? And how have you, over time, maintained your interest in the field? 

Manish: Phil, it’s a real pleasure to be on. And one, how can you not be interested? Let’s just start there. 

The world’s changed. Yes, when laptops came, but when the smartphone came, all of us now have deep tech in our pockets. All of us are using it. It’s gone from being a thing that was an esoteric thing, scientists used here and there, to something that everybody uses every day, and everybody’s familiar with. So, actually deep tech is part of our world in a way that it has never been throughout humanity in the last 15 years.

So, in many ways, it’s a wonderful time to have been part of it before, and it’s an even more wonderful time to have been part of this transition into almost a consumer-centric mindset around deep tech. 

Philip: And if I could, just to take a moment, and again, as someone who – I’m trying to make sure that we clarify things for our listeners here. When we talk about deep tech, maybe for those who are out there who aren’t as intimately familiar, what is the difference when we say deep tech from just everyday, otherwise well-known technology?

Manish: You can break it into many different categories, but let’s start talking about the more obvious ones. Hardware, or biotech, or drugs. These are all deep tech. They’re fundamentally complicated to make. They require a lot of intellectual property, a lot of resources, and a lot of capabilities there. 

In the software world, you know, things are more nebulous. I mean, if you think about, let’s just take OpenAI as an example. I mean, five, six years ago, OpenAI was very, very much deep tech. It is transforming from deep tech to tech as we speak, right? One way to look at it is, the differentiation between many of the models is starting to diminish. Whereas even two years ago, the differentiation between the various models was very significant.

So deep tech usually involves a strong IP base. The need for really expert IQ, so individuals with deep domain expertise, usually PhDs in many cases, but not always, that have the ability to harness that intellectual property and create something fundamentally new. 

And in software, deep tech tends to move to tech over a three to five year period. In the other areas like biotech or hardware. It frankly stays deep tech over the entirety of its period. 

Philip: And I think we’ll have the chance to kind of talk about why that is with that different growth path, what sorts of challenges and opportunities that creates. Maybe though, before we dive into it, just to talk a little bit more about your background, your experience as a founder. 

How has that experience, for you, helped put you in a position where you could understand how companies are built?How did it help you get to a position where you could evaluate companies while you were sitting at SRI? And of course, now as you’re sitting at First Spark. 

Manish: I was a postdoc and briefly an assistant professor at UCSF, University of California, San Francisco, when I took a leave of absence to do my first startup.

My first startup was, initially using advanced image processing to look at very small changes in your body in response to drugs. So think about things like, if you’re doing an Alzheimer’s study, you may be studying the hippocampus, which is a small section of your brain around the side of your thumb. The first digit of your thumb. And we’re looking at 1 percent changes over three years in hippocampal volume on different MRI scanners. 

So you can imagine the complexity of the image processing, you can imagine the complexity. So that was my origin. We ended up actually acquiring blood and urine marker companies as well. So you were very quickly expanded out to understanding the biochemistries of these diseases.

I fell in love. I fell in love with it. And I never looked back. I then did startups in hardware, and another startup in software. In fact, we created the first telemedicine system back in 2007, for my third startup. So at that point, telemedicine was most definitely deep tech. Tried to do video feeds on the fly in 2,000 seconds.

Philip: We remember.

Manish: From there, I ended up at Stanford Research Institute, or SRI, which was really a 75-year-old venerable institute that has been at the source of innovation, probably at the center of some of the biggest discoveries, over the last 75 years. 

I mean, just as some examples – I mean, if you’ve ever had a child, ultrasound came from SRI. If you ever had robotic surgery, intuitive surgical came from SRI. If you’ve ever used a voice assistant, which in theory came from SRI, nuance came from SRI. And if you’ve ever been into sports, the 10 yard line, yellow line on the football field came from SRI. 

And by the way, that was a very difficult deep tech challenge at the time it came out.

So these are some examples of, and I had a chance to start around 40 companies there, everywhere from voice assistants, like we discussed, all the way through surgical robotics, apple-picking robots, space situational awareness phased array radars. So I really had a chance to both succeed and fail in applying different strategies to successfully creating deep tech startups.

Philip: So this all positions you with that incredible breadth and depth of experience to be in the position as the general partner that you are now at First Spark. If I may say it’s the rare VC investment fund that really focuses on, on deep tech. Can you tell us a little bit about the fund and what technology areas that you primarily focus on?

Manish: You’re absolutely right. The vast majority of funds are tech funds, not deep tech funds. They’re looking for very rapidly accelerating, sales oriented companies that can dominate in the marketplace quickly. Deep tech is very different. At the beginning, you have to build out the IP and IQ, and at the same time, keep in mind at all times that you are ultimately a business and you have to grow sales and you have to grow customer base too.

We invest across the spectrum here. So we will do software deep tech, we will do hardware deep tech. And we will also, a large portion up front, is also, what I call wetware, so you can think about biotech or medical devices or diagnostics, or even digital health. We do all three components. Again, only where the IP and IQ provide you a sustainable, long-term advantage.

Philip: I think there’s a lot of listeners out there who are going to be knowledgeable of probably what you look for. But then I think there will be a good number of folks out there who maybe don’t. What are the key things that you’re looking for in a company as you think about investing, in terms of the technology, the team, the potential market size, or the services the company is building? What are the key factors that you’re really taking the time to dig into? 

Manish: So I think there’s a few things that you have to look at right at the beginning. So first of all, technology is fabulous, but can the technology actually do what the product needs it to do?

So it’s this sort of overlap between understanding the product needs very well and understanding what the technology can do, what it can’t do. What it can pivot into, right? Because there is, as you’re doing your early stage, you will be learning new requirements and you have to adjust. Some technologies are very rigid. They work beautifully in one use case, but the smallest pivot, they’re brittle. They crash. So we look at a lot of this sort of concept of brittleness of technology, the ability to morph as the product requirements become clearer, especially at the early stages. 

Another failure mode that’s really relevant, and actually is, we’re seeing this happen in the software side right now, is often technologies get superseded by new technologies that will follow it fairly quickly.

So, some of the things we look at is, will this technology sustain itself over the 3 to ten-year cycle it needs to become the dominant paradigm? As opposed to otherwise, right? So these are some of the things we look at where we – again, I’ve sort of mentioned, there’s one of our mantras is, there’s no IP transfer without IQ transfer. So that’s where we like to see that the people who invented the technology are also participating in the creation of the venture that occurs. 

So these are some of the characteristics we look for across all of our companies. And finally, one of the challenges of deep tech relative to tech is scaling these quickly is challenging, because it costs more money. You’re still in the early development phase. We really want to make sure the underlying component technologies are mature. 

So as an example. So CRISPR, which has been talked about a lot for gene editing. Even as of two or three years ago, success rates in CRISPR, in successfully doing what it was supposed to do, was not very high. So you’d need to do a lot of experiments to get to the cases you need. Which is great in an academic setting, but not great when you have a run rate and you have to run through, develop things, products. 

Today, CRISPR works extremely well. CRISPR and its variants, to be clear, work extremely well compared to three years ago. And so today that component technology is now eminently scalable. So one of the things our fund really looks at carefully is, are the underlying component technologies ready to scale? And if they are, we’re very excited about those areas. 

Philip: And as you alluded to, one of the most critical elements is the funding picture.

So when it comes to Deep Tech, and perhaps the challenges that Deep Tech faces relative to other folks, maybe who are working, as you were describing, just the regular tech side of things. Why is it so much harder for these deep tech companies to raise funds, whether it be in biotech, quantum or energy?

Manish: So, let’s maybe take a quick historical tour of VC and Silicon Valley in particular. 

Silicon Valley started off very much as a deep tech venture place, right? Think of all the chips, you know. Silicon Valley, the chips were what did it, and the VC environment grew under that. Along the way, as chips evolved, things moved slowly and surely, especially at the beginning with the dot-com boom, but then after that, continuously on to more and more software centric applications. Software was very quick to scale. Software has been great to get to the customer as fast as possible. And frankly, everybody who’s putting money into this is looking for a return. And the returns were quicker and eventually bigger. So it was a very rational basis. 

These things are cyclical. What we’ve seen is that we’ve now hit a point where the cloud was an excellent place to invest for the last 10 years. It’s starting to asymptote, and the need for new, deeper technologies is emerging again. Obvious ones have been things that have been very common in the public thought process for the last decade. AI as an example, which was very much deep tech in 2015, and then now is moving squarely into the tech category. The other is autonomy. Think about autonomous driving. It’s very much deep tech, and still very much deep tech. And so what’s happened right now, is that there’s a resurgence, as we’re looking at the new platforms, the new opportunities, whether in biotech, whether in technology to go back towards deep tech. That’s one element. 

The challenge with deep tech is traditionally, I mean, it takes a lot of money and it takes a lot of time. The returns ultimately are great, but if you have to wait 15 years and put in hundreds of millions of dollars. Worst is, wait five years and put in 50 million before, you know. We know how every investor is going to go.

So the real trick in being successful in deep tech is how do you convert those dynamics to more tech-like timelines and scalings? Maybe not exactly the same, but get similar or close to. Can you get to a SpaceX within 10 years? Can you get to a Tesla within 10 years, or eight years? 

And I think the opportunity right now, this is probably the best time in the world ever for that sort of renaissance. Maybe the 70s were equivalently good, but that’s so long ago, I don’t know. But as of the last 30 years, I can safely say that this is probably the single best time to engage in deep tech across all spectrums, whether they’re hardware, software, or wetware. 

Philip: Talk to us a little bit about what you’re seeing that actually makes that the case, and how perhaps you see things shifting, such that those who are looking to build out deep tech technologies or companies, how the funding landscape might be adjusting to their needs.

Manish: It boils down to a few things. One is the underlying component technologies are much, much more mature, much quicker to develop. So, I’ve mentioned CRISPR. CRISPR is technology for gene editing. It is now able to do things – you are able to do things in biology you could not do 10 years ago. 

Let’s talk about things such as quantum-sensing as opposed to quantum-computing, which I already talked about. You can now do things at such a small scale. You can look at firings of single neurons in the brain. You can look at defects in, in materials in an order of magnitude finer than you could before. And these are all available and relatively, I won’t say commoditized, but usable. This enables things to, 

The third element of the technology revolution has to be AI.

In the past, you had to do small sets of experiments to understand the underlying mechanistic cause. Today, you can use AI to quickly get a sense of what the hypothesis or the mechanisms could be, and then apply those mechanistic approaches. It’s so fundamentally different than how science was done over the centuries and millennia before, that you can now accelerate the development and learning by an order of magnitude in terms of time.

So, first of all, at the basic research level, things are going much faster. And the second thing that’s changed in the last few years, if you compare 2020 to today, the amount of money that is being poured in, what I would call the transition from technology to product, at the university level. It was effectively close to zero before 2020. It is in the order of 40 to 50 billion a year in 2024. So the transition from zero to 40 billion, and this is not focused on basic research. This is focused on the ability to take basic research, and apply it to a product. And there are institutions like DARPA, the institutions like NSF, institutions like ARPA E and ARPA H, and this is true even in Europe. There’s NATO DIANA, doing these transformations, which are now enabling that transition to become more robust. What does that do for us as investors? It reduces the risk considerably, and reduces the time to market considerably because somebody is paying for that development, not the VC.

And the third bucket of this is there is starting to, as a result of this – it’s just starting and we’re part of this wave, a growing group of investors who are now becoming more interested in investing in deep tech. And to some extent, some of the revolutions in biology, some of the revolutions in space, and some of the revolutions in autonomy have driven this consumer interest in a very strong way. And consumer interest translates to LP interest, which translates to investors, which translates to an ease to get it funded through to the plane it needs to get to where it’s working off revenue and the consumer. 

Philip: Absolutely fascinating. I think one of the big picture issues that we’re grappling with as part of this series of conversations that we’re putting together, and that we, you know, we’re very happy to have you to be a part of and get your insights on, is exactly what you were just describing. And it’s fascinating to hear that underlying technologies are perhaps getting a bit better and again, you know, things are moving a bit faster. 

So this increases the rate of how quickly people can see returns. Those bridging mechanisms that you’re talking about are there. The heightened investor interest, you know, more consumer possibilities.

How’s the U. S. government doing here? In terms of – I mean, everybody knows the U.S. government is hyper-focused on technology and technology leadership. Lots of it is being driven by competition with potentially adversarial states like China. And I think we should, we should come back and talk about China a little bit. But this really has presented a significant challenge for the U.S. government to move at the pace that’s necessary.

I think there’s some, upwards of 20 technologies, including biotech, that the U.S. government has identified as absolutely critical. How would you rate, kind of, their performance if you will, in terms of supporting everything that you’ve talked about, in terms of getting the right levels of investment to, into the deep tech space?

Manish: I have to say, you know, it’s very easy to always knock on the government, on any government, to be honest. But your own government for sure, talk about how bureaucratic a process might be, or otherwise. But I have to say in this case, the U.S. government has shown really strong leadership and it shows, like in the numbers. Let’s just take DARPA with its embedded entrepreneur program, the ability to start working with academic groups that are building great technology, but saying, ‘hey, what market are you aligning it for?’ Not everything should be aligned, but of the ones that are, ‘what are you aligning it for? And let’s help you. Let’s give you some capital to bring in,  maybe, an entrepreneur who can help you think through this.’ 

NSF is doing the same thing. Then you go to post company formation and you see what groups like AFWERX have done in the past, the recent past. So I think they’ve tried very hard to identify the real value they can provide, and to do it in a way that’s as streamlined with the corporate and commercial world as possible. And DIU is another example of that, of course.

So there’s many positive sources here. Can it improve? The answer is absolutely yes. Can it always get better? Yes. But I honestly have to say, it is a tremendous start. And I think the dividends are not yet being fully demonstrated, because it takes three to four to five years before you see it, and we’re probably three years into that five-year period right now.

Philip: Well, in terms of potentially, we’ve got the IRA, we’ve got the Infrastructure CHIPS Act, the stuff that’s coming out of DOE, DOD, HSS, we’ve got STRATFI, TACFI, all of these different mechanisms. From your perspective in thinking about how to galvanize in the deep tech space, what more perhaps from your perspective, could the U.S. government be doing? Or maybe a better question there is maybe not what more, but what could they change that would make things more efficient? 

Manish: I would say, you know, there’s really three components where one can help, and they’re already helping in some of them. So let’s walk through them. The first one I’ve already discussed, which is that translation from basic research to, let’s call it use inspired basic research, with that helping, bringing an entrepreneur in prior to company formation. I think there’s a very robust practice there, I think it can continue to improve, but it’s doing really well as a starting point. 

The second element is, especially in deep tech, you often have fairly significant capital expenses to get to your first commercial product. So let’s say you want to bring a particular mineral, alloy, material, home. Magnesium as an example, right? Where the bulk of it is made outside the country, maybe in some adversarial countries. You need it for everything from good alloys for your cars, to make your cars lighter, to improve your fuel efficiency, all the way to storing hydrogen, right?

How do you bring that home? Often you have to build a plant here, right? So the ability to offset some of those capital expenses through non-dilutive capital, and-or the ability to make it easier on the regulation side, to try those pilots here or in countries that are more amenable, is something that the U.S. government is engaging in. But maybe a concerted focus on this area, very continued concerted focus on this area, or even an expansion of focus on this area, is probably going to be very helpful. 

The third bucket is, okay, you do one, you do two. The company starts looking successful. How do you manage to retain its presence in, let’s say the U.S. and, and grow the presence in the U.S.? That requires not some non-dilutive grants, not some research credits, but maybe it requires even things like advanced market buys, or something like that in areas that are critical. And  that’s a relatively – not a new concept, but a newer area in deep tech and the government interaction that is still, I would say, developing.

Philip: When it comes to things like advanced market commitments, what do you see perhaps could help – what might be the obstacles, from what you’ve seen over time, that keep more of that from happening? 

Manish: I’m not sure I can answer that question exactly, but maybe I just highlight this point a little bit better, right?

Like, let’s say you are on the board of a company, and you have a fiduciary responsibility to deliver the best value for your shareholders, and you receive an offer for the company that may have been funded by the government along the way, and almost certainly was funded by the government during the research phase, to be acquired by an adversarial nation. There’s many ways – it’s very challenging. The concept of an advanced market buy has so many advantages, it addresses so many issues in this circumstance. It helps the company get its bearing. It helps us raise our series B and C, which are still a bit tense in the deep tech world, and it allows you then to scale up. By the way, some of the most successful companies have essentially gotten this, even though it wasn’t called this in a particular way in, you know, whether NASA placed orders for rockets or otherwise, it’s essentially been the conduit of some of the most successful transitions we’ve seen in deep tech.

Philip: I’m going to pivot back a little bit because China had come up and I wanted to get your insights in this regard. I know it comes up quite a lot in the spaces that you invest in, with biotech and energy in particular. We’ve had really fascinating conversations over time about where China may have, not just a competitive advantage, but may, you know, have an almost somewhat surprising role in the success of where American investment and companies are looking to grow. 

So at this point, how do you see China in terms of where it has, maybe advantages, in fields like biotech and energy? 

Manish: The U.S. over the last 20 years, the investment environment moved squarely, or 95% of it, moved into the software area, leaving opportunities and a vacuum in other areas, right?

And what’s happened is, we’ve had countries, like China, with extremely talented workforce, extremely focused deep tech, high science caliber teams, grow these areas and fill in these areas and do a very good job in these areas. I mean, one of the examples I would give here is, I was recently looking at a company in the biotech space in the area of bispecific antibodies. 

Antibodies are, you know, used all over the place. There’s not a single largest selling drug right now in the world. Humira is a monoclonal antibody. So just as a matter of interest, I asked, where are your precursor compounds coming from there? And it was coming from a very large Chinese company that all of us know. In this case, Wuxi, it was going to come from there. So I said, okay, what if you had to pick an alternative? And as we went down the list, the first five sources of the best precursors were all coming from China in this case. 

So when you are in this position where it’s very easy to target one company or one concept, but we have to acknowledge that there’s an underlying change that needs to occur, right, here? And we need to start thinking of this. 

So, yes, they have played a really critical role in the success of many, many of our drugs. I mean, we saw a press release or a commentary just recently about AstraZeneca’s decision to separate drug supply chains for the U.S. and China. Novartis is weighing in changes, about to do it.

You know, BMS just did an upfront partnership with a manufacturing startup to try and bring things back. So, we are trying, but I think the situation where we’re having to reestablish ourselves in these areas, which we didn’t even, I don’t think most people had even realized we had ceded, until probably a few years ago.

Philip: Would you say that the, really what needs to happen in order for perhaps a shift back in the other direction – what’s the capacity that we have to even do so? Or is the fundamental infrastructure in place? Are the barriers too great for that to actually happen? What does need to happen in order for that sort of shift back to take place?

Manish: The good news right now is, we are able to do that. If you had asked me five years ago, I would have said it would have been very challenging, and it squarely boils down to the following. We cannot expect to do things exactly the same way and catch up. We will not. What we do have right now is a renaissance in a couple of areas like machine learning, in robotics and automation, that are allowing us to maybe leapfrog past the conventional ways of doing things and go to a different level.

So let me just be a bit more specific with an example here. For example, typically when you make something in bio, you use extremely high quality, predictable precursors that you then convert into a very high quality drug. Now, with machine learning and automation, you may be able to use slightly lower quality precursors, and still get a high quality drug out. That would be very difficult to do on that basis without these techniques. 

So, even at the fundamental level, using these new technologies allows you to do things different ways. Everybody talks about the labor cost arbitrage. Of course, that’s there as well, but that’s not the only variable. And I would argue for these high margin products, that’s actually in some ways the least important variable. The more important variable is, can you do it and get a high quality output at the other end? And I think the new tech stacks enable us to do that in a way that couldn’t be done before, even five years ago. 

So I think there is a moment in time here where we’ve acknowledged the challenges, we understand it, and there the underpinnings of technology translate. And this is partly why these companies, even large pharma and biotech and small pharma and startups, are all starting to revisit this issue in a way.

I think the one other piece of really good news is we are, at least in bio, still the single largest market in the world, right? So that doesn’t hurt you at all. When you’re one of the largest markets in the world, the world does respond to what you are asking for. 

Philip: I’d be curious in terms of what you see, from maybe via your portfolio companies and what you’re seeing from an investment perspective – from everything you just shared, how much of that does, does China already also understand? How AI and automation is going to help it continue to advance at a much more rapid pace, where while we’re trying to do that here, and there is a case for optimism, clearly, how much of that do they get? And how much of a headstart, or maybe not a headstart, but how far down that path do you get the sense that they are as well, to have that speed of innovation? 

Manish: There’s lots of government reports and analyst reports that talk about just how far they are. And the simple answer is they’re quite far along, right? And I think we would be remiss to not acknowledge that fact, and we would be remiss to not acknowledge that there are certain areas they have an advantage. 

Even if you have an equivalent tech stack, aggregation of data is a simple one where they have been aggregating data, let’s say in bio, for a long time. There’s also sometimes sharing of failure modes to company one, try something, it doesn’t work. Sometimes that information is shared across to other companies, even competing companies, more effectively than it is here, where we may not share that across competing companies ever, right? So they have a data aggregation advantage.

They have some competitive intelligence advantages, but there’s also fundamentally different things we have here. We, you know, it’s very easy to overemphasize the value of a single homogenous data source. Whereas we know, if your customer base is not homogenous, very differentiated, very discrete, maybe the data sources need to be in the similar way too, right?

So there’s a paradoxically an argument that the diversity and the inefficiencies of our data sources are actually a fundamental advantage as you consider the markets we operate in. This is a very subtle concept, but we’re starting to see it emerge. We also have a much more diverse and discrete population set than some other countries may have, which means our solutions have to be tailored to that discrete population set.

It also has an advantage that the data we have is much more comprehensive across those data sets. So I think it’s not as simple, one group is clearly ahead of the other. I think there are pros and cons on both sides, and to some extent we have to leverage our strengths and, you know, minimize our weaknesses.

Philip: That leads me to kind of one big, maybe final question for you in terms of how to leverage power strengths and address our weaknesses, maybe to even summarize all of this up. What kind of a framework do you think the US government should be considering for developing a more robust partnership with the deep tech and the investment community? 

So we’ve touched on 

Manish: So we’ve touched on some of these, but let’s recap it. 

I think one, right at the research level and the translational research level, continuing to provide as much support as you can, and helping them think through the translational elements. Really, the advanced –since I pointed out in my last minute ago that, you know, one of the big advantages we have is we’re still the single largest market. This is why those ideas of advanced market buys or market commitments of some form in that early stage are so, so valuable. 

This is probably the single best tool that plays to our strengths. Everybody wants to come to the market, including the companies from adversarial nations to this market, right?

So we need to start finding ways to emphasize and strengthen that approach. And I think it’s, all the other building blocks are continuing to be built successfully and we should keep working on them, but this is probably the area where one could focus a lot. 

Philip: One final piece on this. Do you feel that we have the capital and can we effectively allocate it well enough in order to do the things that you’re talking about?

Can we utilize private capital in the ways that could be most impactful? Do you feel like that we’re, we’re positioned for that? 

Manish: Great question, and there is still a shortage of deep tech capital available. There’s no question about it. Again, it boils down to where our individual – and so can the government help in that area? Absolutely, yes. 

This is not going to change by itself, and any support here is actually very meaningful, and I think critical. Because deep tech will never be without support, more than a few percent of the total VC investment environment. So, and that’s too small a percent for the changes and the impact we’re hoping for.

So that for sure, is something that the government can be very, very helpful in. 

Philip: It’s an incredibly important final note to, to end on, I think. Manish, thank you so much for sharing your wisdom and your experience with us here today as part of the podcast. We really value your time. And thanks again for being here with us.

Manish: Phil, it was a real pleasure. Thank you.