How Khosla Ventures Invests In Deep Tech, with Kanu Gulati and Rajesh Swaminathan

Benjamin Joffe
Aug 24 · 33 min read
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This is the 14th episode of the podcast Deep Tech: From Lab to Market’ where Founders and Investors share how ‘deep tech’ innovation can go from lab to market. It is available on Apple Podcast and other platforms and hosted by Benjamin Joffe, Partner at SOSV, a global early stage fund focused on deep tech. SOSV runs multiple accelerator programs including HAX (intelligent hardware) and IndieBio (life sciences). To hear about new episodes, sign up to the newsletter or follow us on twitter at @LabToMarket.

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Kanu Gulati and Rajesh Swaminathan are Investment Partners focused on deep tech at Khosla Ventures (KV). The firm was founded by Vinod Khosla, co-founder of Sun Microsystems (acquired by Oracle for US$7.4 billion in 2009) and former General Partner at Kleiner Perkins, with the goal of ‘Reinventing Social Infrastructure with Technology’, to elevate the entire planet’s quality of life without destroying it.

  • Over the past 15 years, KV has raised over $5B across 6 funds and invested in about 400 startups including Impossible Foods, Rocket Lab and many more.
  • They invest mostly at early stage — signing checks ranging from a few hundred $k, up to $50 million — and without shying away from the high technical risk of deep tech.
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Rocket Lab, Impossible Foods, Blue River Technology, Flow Neuroscience

After an introduction and examples of KV’s many investments, the conversation covers very practical ideas on:

  • Why it is crucial to prioritize risks and retire them in the right order.
  • The 12 different technologies that can move the needle for the climate crisis.
  • Their approach to detecting startups from centers of excellence.
  • What sectors KV focuses on, including hyperlocal and bio-manufacturing, hardware acceleration for AI, and more.
  • What investment and operating partners do.
  • How they support their portfolio in particular with recruiting (white paper). Vinod Khosla even calls himself a ‘glorified recruiter’!
  • How conviction, immersion, patience and staying power matter more than a PhD to start investing in deep tech.
  • How more engagement between financial and corporate VCs, building more forums and reducing inefficiencies in the deep tech ecosystem could help.

(See below for the full transcript)

For disclosure, SOSV has a number of co-investments with KV including: Flow Neuroscience (brain stimulation device against depression, CE-certified and raised $2.6M), Joywell Foods (lab-grown sweeteners and taste modifiers, raised $6.9M), Opentrons (lab robots for life science, including a scalable testing suite for Covid, raised $12.3M), Prellis Biologics (organ and tissue engineering — also growing lymph nodes to generate Covid antibodies, raised $10.6M). The latter two companies are introduced in our ‘Startups against Covid-19’ episode.

Vinod Khosla’s talk on Reinventing Societal Infrastructure with Technology
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KV Portfolio

  • Blue River Technology: Building precise agricultural “see & spray” machines that apply chemicals only where needed. Acquired by John Deere for $305M.
  • DoorDash: Food delivery service. Raised $2.5B.
  • Flow Neuroscience: Medication-free depression treatment using a brain stimulation device. Raised $2.6M.
  • Guardant: Liquid biopsy for comprehensive tumor mutation profiling of solid cancers (NASDAQ: GH).
  • Impossible Foods: Plant-based meat that bleeds. Founded by Pat Brown.
  • Korbit: AI-based personalized tutor. Raised $2M.
  • LanzaTech: Carbon recycling technology. Raised $276.3M. Spun off LanzaJet to produce sustainable aviation fuel.
  • OpenAI: AI research and deployment company focused on general AI. Raised $1B.
  • Opentrons: Robot for life science labs. Raised $12.3M.
  • QuantumScape: Renewable energy company developing solid-state battery technology to increase the range of electric cars. Raised $300M. Founded by Jagdeep Singh.
  • Rocket Lab: Launch provider for small satellites (raised $215M).
  • Voyage: Self-driving cars with first deployments in retirement communities. Raised $51.2M.

Other references

  • Vinod Khosla’s post on Critical Climate Technologies.
  • Khosla Ventures’ Resources for Startups
  • KV’s white paper on The Art Science and Labor of Recruiting
  • Samir Kaul: Founding Partner of Khosla Ventures.
  • Nvidia Graduate Fellowship Program: awards yearly up to $50,000 each to five Ph.D. students involved in GPU computing research.
  • OmniSci: accelerated analytics and data science.
  • Applied Materials: global leader in materials engineering solutions for the semiconductor, flat panel display and solar photovoltaic (PV) industries.
  • Applied Ventures: venture arm of Applied Materials with 80+ companies in portfolio across 15 countries. Stage agnostic and investing up to $50M per year globally.
  • Drawdown: A seminal book listing 100 potential climate change mitigation solutions, and ranking them by the potential amount of greenhouse gases each could cut, with cost estimates and short descriptions.
  • Steve Blank’s Lean Launchpad class on entrepreneurship at Stanford.
  • DARPA: The Defense Advanced Research Projects Agency is an agency of the US Department of Defense (DoD) responsible for the development of emerging technologies for use by the military. Created in February 1958 in response to the launch of Sputnik 1. Influenced many non-military fields and provided the basis for the modern Internet.
  • ARPA-E: US government agency tasked with promoting and funding the R&D of advanced energy technologies. It is modeled after the DARPA.
  • Yoshua Bengio: deep learning pioneer, Turing Award winner, and founder of the Mila AI lab at the University of Montreal.
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Ben: [00:00:00] Kanu, Rajesh, great to have you today.

Kanu: [00:00:02] Hi Ben. I’m very excited to be here. I’m Kanu Gulati, my background is in electrical engineering. I did my masters and PhD in EE, focused on hardware acceleration and high performance computing. Nvidia and Intel funded my PhD — I was an Nvidia fellow. And then after graduation, I joined Intel as a research scientist, speeding up their new lines of hardware, and also focused on the application of EDA, which is a software which Intel uses for designing chips.

I have then been part of early stage companies as one of the founding employees or founding engineers, most recently a company called OmniSci, which uses hybrid acceleration for speeding up data query in visualization.

I’ve been on the investing side for over five years now, and at KV in particular for over three years, the introduction to KV happened through a cold email and I did an internship here, and then after a few other hops came back.

And at KV, I tend to focus on applications which involve data, machine learning, computer vision, speech recognition. I also focus on robotics and autonomous systems and hardware acceleration of AI. So I know my interest sounds very buzzwordy, but this is all the things I focus on here at KB.

Ben: [00:01:09] So you’re really coming from the chips and electronics type of hardware.

Kanu: [00:01:13] Yes, I’m trained on the deep, electrical engineering and in the signal processing side, but a lot of my PhD focus was on the software that’s built for leveraging hardware correctly, so what architectures fit well. So it’s really on the combination of hardware and software stack which, now with the advent of deep learning and AI, has just found renewed interest,

Ben: [00:01:34] There’s a lot of overlap with what’s happening around autonomous vehicles, robotics, and even in medtech and other applications where you need that combination of hardware and software. Rajesh, tell us also about your path to KV.

Rajesh: [00:01:46] Absolutely happy to. I joined Khosla ventures in January this year, primarily focusing on hardware, deep tech opportunities across multiple verticals. Before joining KV, I was heading Applied Ventures. It was a $300 million VC fund out of Applied Materials, but I focused on deep tech. Earlier it was cleantech then AI, AR/VR, 3D printing, medical devices. Essentially a lot of material science-based deep tech investments.

Academically, my background was chemical engineering and material science. I worked at Bell Labs with tons of optical startups back in 2001 to 2006, went to business school, worked with a summer internship in clean tech banking. During that time met with Vinod and Samir, worked with a couple of their portfolio companies — this is almost 12 years back — then I moved to Applied. I’d been there for 10 years, done a couple of co-investments with KV and, it’s great to join the fund. A few six months back, just before Covid.

Ben: [00:02:36] Wow. That’s a special time, but what’s interesting is that even though it’s been so challenging for startups and for investors it looks like everybody’s getting up to speed with new business practices and keep investing and keep growing companies despite the situation, but we’ll probably cover that in the later stages of today’s episode. for those, listeners who are not familiar with Khosla ventures. Kanu, if you can tell us more about the origins and the mission of the company.

Kanu: [00:03:00] KV’s mission at the highest level is reinventing societal infrastructure with technology. We think about getting into new sectors by starting with the GDP of the United States, what are the sectors that can gain from technology and advances in autonomous systems, figure out where there might be best combination of financial returns and improvement to society.

It was started by Vinod Khosla, who is a very successful entrepreneur. He was the founder of Sun Microsystems had an exciting run as a general partner at Kleiner Perkins and then started KV, more than a decade ago. We are currently investing out of our sixth fund.

Ben: [00:03:35] Typically what kind of investments stages on the check sizes do you do?

Kanu: [00:03:39] Everything from doing a first check, which is a few hundred k, to writing a large check. On the extreme we’ve done our largest publicly announced check is $50 million into OpenAI. Our sweet spot is writing a first check between 1 and 10 million, which loosely translates to seed and series A, but we have a lot of flexibility.

Ben: [00:03:56] KV is very famous in the US and North America at large. You’ve done also a few deals in Europe. What is your geographic coverage?

Kanu: [00:04:03] We can do investments everywhere. We have investments in North America, in Europe, all the way in Australia and New Zealand. Up until now, like the farther you are from Silicon Valley, the bar just got higher. I do think, the current Covid environment is having a role to play here because distance really doesn’t matter. Everybody’s doing a lot of the diligence online. We do have a larger, portfolio in North America, but we are open to investments everywhere.

Ben: [00:04:26] We have a co-investment in Sweden called Flow Neuroscience that does a brain stimulation device to treat depression. I also know that in your portfolio, you have a few really interesting companies overseas, Rocket Lab being probably one of them.

Rajesh: [00:04:37] Yeah, absolutely, we have invested in some very cool companies, across many sectors: CleanTech, AgTech, healthcare, enterprise, education, FinTech, robotics, big data … So wherever there is a strong opportunity to make a technical impact on large markets.

  • We made some good investments there in FinTech: we were one of the early investor in Stripe, we’ve also invested in Square.
  • In healthcare we have invested in companies like Guardant. It’s a public company, which is completely changing the world of liquid biopsy.
  • In AgTech we were one of the very early investor in Impossible Foods. It’s a plant based meat. That’s getting a lot of traction in grocery stores in Burger King. It started as an idea by a great entrepreneur, Pat Brown, working with Samir to make it happen.
  • In enterprise we have been invested in DoorDash. Particularly in the Covid times we have done a lot of investments in companies that are making a huge impact. We made these investments a while back, looking to change the world of infectious diseases, and those are coming very handy for what is happening in covert now. OpenTrons is an investment we have done with you guys at SOSV. It’s an open source, lab automation. They can automate PCR sampling; they can automate the NGS library prep. So a lot of companies are able to leverage that capability to scale up the Covid testing at this point. And as you mentioned Flow Neuroscience is another good example.
  • In the world of Cleantech, there are companies that we’ve incubated at KV and have grown really well. QuantumScape is one such example. They are going after the lithium batteries, far higher energy density than what is possible in the market today. It started with Jagdeep Singh as an EIR at KV, and they recently closed a $200M funding round from Volkswagen.

So there are plenty of examples in each of the sectors that I can think of, Kanu can perhaps add more to it.

Ben: [00:06:16] You mentioned that some of those companies were incubated at KV. Could you give more details about what that means?

Kanu: [00:06:20] So for KV to enter a new area or starting a new innovation, there are a couple of ways we think about it.

  • Like I said, starting with the GDP of the country and saying: what are the areas that can really gain from autonomy and advances in technology?
  • Another other aspect is we try to keep in close quarters with what we consider as centers of excellence for certain technologies. Like keeping up with the best-in-class academic or industry labs, and try to bring in people to help with incubation or help with diligence.

And so some of these companies, like for example, Blue River is a former portfolio company, that got acquired by John Deere. It started as a class at Stanford: Steve Blank’s class on entrepreneurship. One of my partners was actually the advisor, and the relationship just kept growing and we decided to lead that investment. So it started really early on, almost like an incubation, because we were part of that conversations pretty much from day one.

So there are several examples in our portfolio where we are not just the first financial check, but also the first kind of a brainstorming partner with those companies. And we continue to do several incubations. And that’s a model that we’ve been testing and optimizing over several years now.

Ben: [00:07:28] So keep close to labs and entrepreneurs and their work.

Kanu: [00:07:34] Yeah. they come hang out with us, up until Covid. They would spend time physically at our offices. but then work with them just visualizing:

  • What could be changed here in a particular sector, what should exist?
  • Where can autonomy or advances in technology really play a major role?

Incubate an idea of what a company should look like together. Examples of that include companies like Impossible Foods and Blue River and Rocket Lab, and in several more in our portfolio.

Ben: [00:08:00] I read Vinod Khosla’s post around critical climate technologies. Is this kind of thesis-based investment something you do a lot?

Rajesh: [00:08:08] KV has done a lot of investments in the past in Cleantech and the climate crisis continues to a major issue. So Vinod put out an article in a blog on medium recently where he talks about the 12 different technologies that move the needle. This includes:

  • Aspects related to electric vehicles and automotive batteries.
  • How could food and agriculture, change in behavior and in technology could make a huge difference in the greenhouse gas emissions (GHG).
  • We also talk a lot about low carbon transportation, jet fuel, for example, recently one of our companies LanzaTech, spun off an entity called LanzaJet with a large funding from some of the major aviation companies, to go to after the opportunity around jet fuel.
  • We talk about construction materials that could play a major role.

So there are 12 different, technologies Vinod talks about. And there are, 15, 20 other opportunities that are not as critical. So, that’s just one example for climate tech.

We also look at thematically, a lot of other areas.

For example, one of the areas I’m looking at is the whole concept of bio-manufacturing, what are some of the different sub-verticals within that?

And we have done a lot of investments in some of those areas, and we’re looking at new investments in some of those areas. So it’s a thematic approach. And I do believe in the view of ‘preparation meets opportunities’, the right sweet spot in terms of finding an investment.

Ben: [00:09:23] What I found really fascinating also about Vinod’s piece was the fact that of course, he mentioned some of the high visibility points a lot of people talk about, but also looks into the less visible, less glamorous sectors, including steel manufacturing, HVAC, energy consumption, and even cement, fabrication. As you described, it’s like looking broadly and systematically, what are the the high potential opportunities that are not necessarily visible or reported much in the media.

Rajesh: [00:09:48] Absolutely. HVAC is a great example. Venture capitalists don’t typically talk about HVAC because it’s not as attractive as some other technologies. But if you look at the impact, this is a number one. In the book called Drawdown that talks about prioritizing the list of opportunities and huge impact on the climate crisis, HVAC is number one, but you typically don’t see venture investments going in that area. We continue to keep our eyes open on some of those things.

Ben: [00:10:12] One of the challenges of innovation in those sectors is that there’s not many investments also because there’s not many companies, because it’s a low awareness sector among entrepreneurs, right? We’ve done a couple of investments related to HVAC technologies (Flair, Breezi), we saw that it’s a very specific market and a lot of investors just have no idea about what’s going on. The players in the sector are either large energy companies or contractors, and bringing them the technology is not necessarily very easy.

One fundamental aspect is also how interdisciplinary the whole area of deep tech is.

Rajesh: [00:10:37] Absolutely. And I think one fundamental aspect is also how interdisciplinary the whole area of deep tech is. If I look at HVAC, things like thermo-electrics or AI-based approach with quantum computing for materials discovery may not come to somebody’s mind right away. But because we are looking at so many different verticals, we always try to connect the dots between different disciplines, different technologies that we see in one market to see whether it could make an impact on a completely different vertical. And, there is an a-ha moment that you get in some of those cases. And we try to leverage on that.

Ben: [00:11:08] Yeah. So actually along those lines, Kanu, I’d like to ask you, what’s the approach for KV to enter new deep tech sectors? Because a lot of VCs get very intimidated with sectors they’re not familiar with. It’s much easier to stay in a well known territory, but you guys are pretty adventurous.

The bigger question is: we like to understand what is the biggest risk we need to retire and in what order

Kanu: [00:11:23] in part trying to identify where are the opportunities, that can have a big impact on society. and trying to identify also what technologies can have a real impact on adding efficiencies to that sector. We start from there.

The bigger question is: we like to understand what is the biggest risk we need to retire and in what order, in order to know that yes, you’re moving towards a successful outcome for the entrepreneur and us. And I think within the firm having a sufficient amount of expertise and then within our network — keeping up with people with that expertise — becomes really important to help calibrate what risks have already been retired and what is the next amount of time interval and the next pool of capital.

What is the risk that we want to retire and being very laser focused on that style of investing or risk retirement. I think is our approach across all of those sectors.

To give a couple of examples:

Rocket Lab is a company which launches rockets to deploy satellites in space. It took four years before the company first test launch, but we were aligned with the founder on what risks needed to be retired along the time. So for the first year of rocket lab’s life, they were basically retiring the engine risk, which we knew. And then it was the most important thing to understand and de-risk.

Another one of our portfolio companies, a company called Voyage, is a self driving taxi service in communities, starting with retirement communities. The idea there is you start with a more constrained set of the problem. So instead of trying to solve autonomous driving for every kind of road or every kind of city in the world, they’re starting with a constrained set with private communities, where you can afford to set up extra sensors. You can set up Lidars, speeds are lower, the roads are wider, the weather usually in these retirement communities is better. And it allows for the technology to be tested with a real business model in the real world. You’re not just running simulations, but it’s a more constrained version of the problem.

And so there are different approaches we apply, but for every company that study is slightly different, there isn’t one formula, but at the highest level, if I had to abstract, it would be identifying what is the biggest risk we need to retire and what’s the fastest and most cost effective way to get past those risks is our playbook, really.

Our playbook is identifying what is the biggest risk we need to retire, and what’s the fastest and most cost effective way to get past those risks.

Ben: [00:13:39] One thing we noticed with many of the companies we work with is that, even if we identify good business and technical milestones, you still have some kind of funding risk. To take the example you gave about Rocket Lab, and getting four years before the launch, what made you confident that the company will be able to finance itself through those different milestones? Essentially. You need to find other investors who think the same way you do.

Broadly, we prefer technology risk to market risk

Rajesh: [00:14:04] Yeah, I think it’s a function of what the sector is. but broadly, we prefer technology risk to market risk, on any investment that we make.

For example in med tech:

  • It’s about the fundamental science, right? So we look at one of the biggest technical risks that will cost us significantly more in the long run. We try to de-risk those first so that these are not expensive mistakes, or we don’t wait it out in terms of de-risking them.
  • The other key one in med tech particularly is things like regulatory risk, right? So how do you mitigate that? Obviously clinical trials may take some time for some of the companies and we invest early stage. So we look for results from pre-clinical trials, or kind of expectation on what the performance would be.
  • If the company were to go through some of the clinical trials later, the other key aspects we look at from a de-risking perspective is, strong IP. what does the company have in terms of IP that’s longterm sustainable? Do we have any blocking IP, do we have the freedom to operate? De-risking those things to be ahead of the market is very critical.
  • We also don’t invest in a lot of sub-component level companies. We look for kind of a minimum viable product system level, that customer can really test and provide early feedback on. So how early can we put a product that a customer can play around with get early feedback so we can iterate very quickly, and get a sense for who will pay and what is the willingness to pay. particularly in med tech, is an insurance company going to cover it is consumer going to pay for it.

Those things are very critical. So these are four or five things. If I just think of med tech.

It may be completely different for for a market like industrial technology:

  • Some companies may be stuck with, or starting to work with, slow-moving industries that may delay factories from driving robotics automation, 3D printing, et cetera.
  • In that case, de-risking the product-market fit is very critical. Identifying the beachhead market and first application that will get you to revenues is extremely critical.
  • So in those cases, we need to understand the customer pain point to pay versus competition. and the product market fit is, are some of the first things we try to do this.

Ben: [00:16:01] So another reason I really wanted to have you on the podcast is because you’re one of the few investors who invest at quite a large scale in deep tech. There’s very few specialists and most of them don’t do a lot of deals, but you’ve done many deals. So I’m curious about what are the lessons you learned from successful and possibly also failed startups through the deals you’ve done.

Rajesh: [00:16:25] That’s an interesting question. I can take a stab at some, recognize that I’ve been here only for six months. To me, having the right team is the single biggest factor, particularly for something like deep tech because you’re just dealing with so many verticals, even within a given industry, in a given company. I’m on the board of a healthcare company: you need expertise on optics, semiconductors, biological assays, AI and ML, all in one company.

That’s not the kind of expertise you need in a software company. So it’s very different and having a team that complements each other, with best-in-class people, good culture, is very critical if you want to de-risk on key elements across each of those verticals.

We do something called option value investing in our fund. So we have a seed stage fund and also a main fund. We do a lot of seed stage investments anywhere from $250k to $1-2 million. By the time these companies come for series A, we have a much better view on what works. What de-risking remains, et cetera.

So if you’re able to deal with some of those key elements at the seed stage, and it looks great, we invest heavily in the company. We take a much bigger stake in these companies, because our internal view is you can only lose 1x of your money, but the upside could be tremendous with the right companies.

So we do take effort to double down on the winners, even a series A or series B or later stages, and try to leverage the information we have with a small check that we have written. And see what decision we need to make at a later stage. the other key learning, certainly whether it’s in any of the deep tech is the need for high quality syndicated investors.

Deep tech takes more time, more dollars than you can ever plan for. It’s very important to have co-investors who have high conviction in the company and also good strategic partners who don’t drag down the company, in terms of collaboration or, potential corporate venture investment.

I think the biggest one to me is de-risking your most important question that has the highest uncertainty. And there is another axis on uncertainty around those answers.

You need to deal with the most important questions with the highest uncertainty first.

You need to deal with the most important questions with the highest uncertainty first, so that it’s not too late or too expensive by the time you come to those things. That could make a big difference between an early success and an expensive and slow failure.

Ben: [00:18:28] Kanu, would you have anything to add on that aspect of the lessons learned from you successes or failed startups?

Kanu: [00:18:33] One fundamental thing I’d say is, KV’s culture, obviously starts at Vinod at the helm of it, is seeking that asymmetrical upside. So if an investment has a 20% probability to become a 100x we would do those investments all day.

And yes, there’s a high probability that they might not lead to anywhere, but the asymmetrical upside is the name of the game here, and that in combination with really focusing on the initial hires, like trying to bring the best-in-class people we have access to, and surround the entrepreneur with patient and like-minded investors becomes key.

There are definitely learnings we have from previous sectors or investments we have done, which continue to help us grow as we make investments. There were companies that we learned from our CleanTech investments, which are learnings from there that helped us make better decisions when we were investing in Impossible Foods or some of our now, Cleantech or Climate and Sustainability tech.

So I think the openness to failing, but knowing exactly what we need to know at this stage and what risks we need to retire, is our playbook really.

Ben: [00:19:36] Yeah, that’s really interesting because I’ve come across a number of investors who tried some new sector they’re not that familiar with. And then things don’t necessarily go very well. And then they get very discouraged about the sector and their capacity to invest in it sometimes for a decade before the touch hit again. So your view on the risk. de-risking lessons learned is really interesting, because it enables you to keep investing, but knowing better and better, what level of risk you’re taking and building up on that knowledge.

So another question I have is regarding your team, because the KV team is quite large. There’s investors who are called ‘platform VCs’ and provide a lot of support and a lot of resources to startups. so if you can describe how, what kind of support the KV team is providing and how many people are doing what in general terms, that’d be really interesting. Rajesh, if you want to take a stab at that?

Rajesh: [00:20:28] So I think we have about a dozen people in the investment team, and 12 people as operating partners as well. And in the investment team, we have strong expertise in many of the verticals that we just talked about.

The operating partners are a fantastic resource for all our portfolio companies. Kanu can correct me, but I think we have about 350–400 companies in our portfolio overall, over the past 15-plus years that we have been investing in. So these operating partners are a great resource for the key management, and these are people who have built scaled businesses and can quickly jump on a call with the CEOs on some of the key operational challenges that they face.

We also have a strong talent recruiting team that we tap into to bring, some really good names, as hires for our portfolio companies. So I think overall it’s a very balanced team in terms of both investment and operationally how we try to add value to the company.

Ben: [00:21:18] That’s really interesting. Like I saw that even on your website, in the resources you list, you have articles on recruiting and you also mentioned something called the standard operating procedures to help startups. So can you elaborate on how you help founders in that way?

Kanu: [00:21:33] To give you a specific example, we have Irene who used to run design at Google. And now, for any of our portfolio companies who has a question or a problem they need to solve around the design or the user experience or the brand experience, she’s an amazing resource. Similarly we have people who can help mentor first time founders. They have more of a CEO coaching role because that’s what they have done in the past.

Other CEOs are [also] being really good citizens of our founders community and they can be a first call for any of these first time founders on some logistical challenges or day to day operating challenges.

Our biggest single value add is on the recruiting side.

I think our biggest single value add is on the recruiting side. We have four partners at the firm operating partners who help hire, both on the technical side and also VP-level and above executive hires, for our portfolio companies. And the one thing that impressed me when I joined KV was the amount of time even the investment partners — including Vinod — spend in helping portfolio companies hire, both on assessing candidates and helping them close.

And I think, if I had to like highlight just one single way we absolutely help our portfolio companies is on the recruiting side. Like that by far is our biggest value add. And then we do all the things which other VC firms I think are also doing in terms of like coaching and legal and design and everything.

But I think that the recruiting for us is probably key. And it’s an inordinate amount of time I’ve seen on Vinod’s calendar, helping with hiring.

Rajesh: [00:22:58] That’s so true. Vinod calls himself a glorified recruiter because of the amount of time he spends bringing top leadership to the companies we invest in.

Ben: [00:23:05] Would you say that this is the biggest challenge of most deep tech startups, or are there other specific challenges that deep tech startups have that you’ve seen across the board?

Kanu: [00:23:16] I think there are a few though recruiting the team, recruiting and bringing the best in class people you have access to, becomes one of the biggest challenges or the biggest risk factor that the company should retire.

I think the other aspects of which deep tech startups have over any other startup in general is one being very clear on the order of risk you want to retire. So retiring the biggest risk upfront. And oftentimes it may not be the best outside looking signal. Like it may not be the revenue which you’re retiring first and it may be harder to convince the next round of capital to come in. But because opportunity costs for these founders are so high, and often times these companies take longer to get to a successful state, it’s very important to not to lose sight of what is the key risk you need to retire at this stage. Prioritizing that list becomes another challenge.

And I think the third thing is just surrounding yourself with patient capital. So just getting the right investors or the advisors around you who like you believe in the big outcome, the asymmetrical upside, and don’t try to ask you for a quick exit or don’t try to force you into that, becomes very important. Specifically, for deep tech startups, those are the things I’d say they have to handle most.

Ben: [00:24:24] And Kanu, would you say that there’s more of this patient capital these days in the deep tech ecosystem?

Kanu: [00:24:31] We try to surround ourselves with other people who are like-minded, and who similarly believe in the biggest goal. There’s absolutely a brand that gets created and KV’s past successes helps give us credibility for the next set of investments.

And people do want to understand which companies have invested in what and our investment thesis here. I think there’s a lot of capital out there and you’ll find all sorts of investors, but picking investors is, I think, the name of the game here.

Ben: [00:24:57] What do you think would, aside from patient capital help improve the deep tech ecosystem overall?

Rajesh: [00:25:03] First, I think there needs to be a better engagement between corporate VCs and financial VCs. Having been in the corporate VC world for 10 years, looking to build a bridge with the financial VCs. And now I’m on the other side. There’s a lot of investors on the corporate side looking at deep tech opportunities, but, historically they’ve been very slow. That’s a big issue. And people have tried to ask for a lot of strategic rights that makes it difficult for bringing the right CVCs in. But that’s changing a lot with some corporate VCs, Microsoft explicitly saying that they don’t want to have strong strategic rights or rights of first refusal and all that stuff. So that’s encouraging.

So there needs to be more forums for these communities to come together. For example, there’s a global corporate VC event that happens in January every year. You almost find absolutely no financial VC there. So that bridge, I think, would be helpful. And I think what organizations like SOSV are doing is extremely valuable, whether these podcasts or the events. I’ve been to many of the events that you hold in terms of exposing those early stage deep tech companies that you have, showing what kind of financing happens across the world, what are some major trends across the world … It motivates a lot of people to spend more time in deep tech and see that there is an ecosystem of investors. So that kind of ‘bringing together the community’ is extremely helpful.

I think the third one I would say is, there’s value in removing a lot of inefficiencies in the system. I think for example, the SAFE structure that has come up in the last few years has been very useful for a lot of startups, in terms of raising money without having to negotiate a very detailed term sheet and all that stuff.

Similarly there are standardized approaches — and I’ve explored this with other larger institutions in the past — that help on commercial contracts for startups with larger companies, So they don’t waste significant time negotiating this, That would be helpful.

And particularly for the US I think more government funding on some of these deep technologies would be very helpful. The telecom, the semiconductor innovation, a lot of it came from the amount of money that DARPA invested, right? Similarly, the Cleantech success, with solar, Tesla, has all been from what ARPA-E and a lot of DOE government funding went into that area when everybody — I am thinking in 2008, 2009 — would spell doom for Cleantech industries. So it’s important for the U S government also to step up and start funding these deep tech startups in a significant way.

Ben: [00:27:12] Yeah, you’re right. in the US there’s a large amount of funding available through SBIR grants, but many startups are not aware of them or don’t know how to navigate them effectively. So that’s definitely a challenge there. What sectors are you the most interested in currently? I saw online, the post around climate technologies, I guess COVID might also have, stirred up some interest in particular sectors. could you give us a view of it? What are the most interesting ones for you at the moment?

Rajesh: [00:27:38] Sure. I can add a few ones:

  • Bio-manufacturing is an area that I’m very interested in and there’s a whole set of verticals and companies in that sector.
  • As you mentioned, Cleantech is something I’m still passionate about. KV continues to look for good opportunities. there are at least 12 different areas we are actively looking at.
  • And Kanu mentioned about automation with robotics, that’s a huge trend that’s happening right now when people are looking at social distancing, and what can you do to continue the path of robotics?
  • A couple of other trends I’m focused on as the whole concept of hyperlocal manufacturing, whether it is 3D printing or other approaches where countries want to have their supply chain overall manufacturing locally and perhaps in multiple countries, but in a distributed fashion. So that you are not significantly hurt when things go wrong in one particular country. Supply chain is another area I’m actively looking at, but those are just a handful of areas that I’m spending time on.

Ben: [00:28:27] Maybe on the topic of bio manufacturing, because I think a lot of people have heard now of ‘food in a lab’, but I guess it expands to many other applications, including industry. Could you give some examples of some applications of biomanufacturing you’re interested in?

Rajesh: [00:28:40] Yeah, it’s anywhere from:

  • Biofuels and other verticals.
  • For example, what kind of medical devices can leverage what is happening in bio-manufacturing?
  • What kind of textile innovations are possible that leverage some of the natural materials that we are looking at for sustainability purposes.
  • There are a whole bunch of verticals around carbon sequestration. How do you improve or increase the overall photosynthesis, so that you can have more carbon sequestration, at a much higher scale, to address the climate crisis?
  • And the whole concept of biocomputing is another vertical that we are looking at.

So there are a lot of verticals and the question is which one would we prioritize and where do we see the next big inflection?

Ben: [00:29:25] And Kanu, would you have things to add maybe on the software/hardware combinations front, and the robotics that you’re looking at?

Kanu: [00:29:33] Yes, absolutely.

  • So some of the things I think Rajesh mentioned — looking at it on the systems and their impacts on logistics on warehouse automation and supply chain as a whole.
  • Then there is, advances in AI: speech recognition, natural language processing, all of the recent announcements around OpenAI and some other AI first companies.
  • I also spent a lot of time on hardware acceleration for AI, to just allow more advance of AI around us, both in terms of faster processing and then also on the edge: being more aware of the power-constrained environments and still enabling edge computing.

So those are some of the areas that I’ve been spending my time in.

One of the companies I’d like to highlight, started out of a university of Montreal. This is a founder who works with Yoshua Bengio who’s one of the Turing Awards winner in AI. And there is so much advances happening in natural language processing and dialogue systems, and he’s very passionate about education so we invested in this company called Korbit and they were building an AI-based, personalized tutor.

  • The idea is that with a personalized tutor, you don’t run the risk of high dropout rates from an online course because the engagement and the retention can be increased because of curriculum, personalization of modality of which the students learn best in.
  • And just better framing what aspects in free-flowing text: can you understand what aspects of what concepts do student already understand and where they need to learn more?

So this is a combination of identifying where we believe that technology has a big role to play, and then also identifying a center of excellence and working with the right founder and the team in order to attack or address these challenges.

Ben: [00:31:09] How did you get in touch with that company? Because the timing sounds so perfect now with COVID and the work-from-home, study-at-home situation. It’s often interesting to understand the origins of those initials contacts to understand how you work.

Kanu: [00:31:22] So this one in particular was among the labs we try to keep up with — one of the areas we spend a lot of time is just keeping up with the latest in technology or just what’s possible today, and what’s real and what’s hype. This comes from us, as investors and our network, trying to figure out what is real and what can really be applied today towards an application. So this was a contact from the university of Montreal — Mila, this is their language learning Institute — and I got introduced to this founder, and he was very excited about his passion for improving online education.

Ben: [00:31:52] Sounds like perfect timing and perfect application. So maybe to conclude: the purpose of this podcast is also to help investors, gets more familiar with deep tech and investment in deep tech, for angels, VC, CVCs that you mentioned, for instance, but also LPs who might be more inclined to back, deep tech funds. What would you recommend for them to look into to raise their level of knowledge, and confidence about deep tech?

The whole deep tech ecosystem investment is a marathon and sometimes it’s a relay race.

Rajesh: [00:32:20] If it’s specifically a recommendation for investors new to deep tech, I would say the whole deep tech ecosystem investment is a marathon and sometimes it’s a relay race. So that’s why picking your co-investors syndicates with a longer time horizon in mind is very important.

Depending on your fund size, you may be able to go to only certain stages: you may not be able to allocate more capital, and so bringing in some of those larger investors and more importantly, investors with conviction in the area that you’re investing into well upfront, would be extremely valuable. So that your companies have a chance to be successful, to bring a strong syndicate of investors in the long run.

Have conviction and ensure you’ll have a staying power in a sector.

The second one that I would say is, have conviction and ensure you’ll have a staying power in a sector. Don’t jump into a particular deep tech vertical just because it’s hot today. Make sure it’s reasonably aligned with your longterm vision, your LPs interest areas, and the capital that you can allocate for the sector. Because this is not like a enterprise software or social media startup where you can throw a little bit of money at a lot of companies and, given the bubble of the number of investors that you have around the table, you may be able to raise money towards an exit opportunity. So having the conviction in a particular sector, I would say is very critical.

The third thing I would highlight, as you should recognize that hiring will be a critical part of your job. Not just because we do it and we do spend a lot of time, but it’s an important one. We should recognize that deep tech, companies need a lot of expertise, and just a startup’s founders may not have access to the talent across so many verticals that need to come together. And that’s one of the biggest value add for VCs. And that’s something you need to commit to in terms of time, and your expertise and network as well.

Ben: [00:33:55] Kanu, do you feel that to be a deep tech investor, you need to have yourself, as a start, deep tech expertise? Or is it something that can come along as you study different sectors?

What’s required is having a strong conviction or being immersed in an area. I don’t believe you absolutely need a PhD before you can invest.

Kanu: [00:34:05] I don’t think they do come in with an expertise already yet. It can help you get started, but I don’t think that’s a requirement or a prerequisite for somebody to become an investor in deep tech. I think what’s required is having a strong conviction in certain areas or at least being immersed in an area. So I think it all begins with immersion in an area, having original thought on what should exist, and then the patience on actually carrying those ideas out. I strongly don’t believe you absolutely need a PhD before you can invest in a certain area. It’s more about an immersion in that area and the want to learn more and be curious and just learn the most you can. And then building conviction in that area.

Rajesh: [00:34:45] I absolutely agree with Kanu, I think it’s about making sure you have the right questions and bringing the right people around you to help answer those questions, rather than having a domain expertise in all these verticals. So I fully agree with Kanu.

Ben: [00:34:57] That’s very encouraging for the future of the deep tech sector. We covered lot of topics, so I will just close by wishing that through your investments, you keep solving many of those tremendous problems that humanity is facing everyday.

Rajesh: [00:35:10] Thank you, Ben. Thanks for the great work you’re doing. It’s really good when people are shut down at homes that you’re trying to do a lot of connecting the dots between the investors, between the startups, and keeping the ecosystem active. So really appreciate your work.

Kanu: [00:35:21] Thank you Ben for having us. And we look forward to more co-investments with you.


SOSV: Inspiration from Acceleration

Insights from The Accelerator VC—including our programs…

Benjamin Joffe

Written by

Partner @ SOSV — $700m VC fund for Deep Tech (biology, robotics, etc.) | Digital Naturalist | Keynote Speaker | Angel Investor

SOSV: Inspiration from Acceleration

Insights from The Accelerator VC—including our programs (HAX, IndieBio, Chinaccelerator, MOX, Food-X, dlab) & our startups.

Benjamin Joffe

Written by

Partner @ SOSV — $700m VC fund for Deep Tech (biology, robotics, etc.) | Digital Naturalist | Keynote Speaker | Angel Investor

SOSV: Inspiration from Acceleration

Insights from The Accelerator VC—including our programs (HAX, IndieBio, Chinaccelerator, MOX, Food-X, dlab) & our startups.

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