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Differential invests in B2B data technology startups using AI and machine learning
Venture capital used to be a cottage industry, with very few investing in tomorrow's products and services. Oh, how times have changed! While there are more startups than ever, there's also more money chasing them. In this series, we look at the new (or relatively new) VCs in the early stages: seed and Series A.
But just who are these funds and venture capitalists that run them? What kinds of investments do they like making, and how do they see themselves in the VC landscape?
We're highlighting key members of the community to find out.
Nick Adams is a co-founder and Managing Partner at Differential Ventures.
Previously, Adams was a Venture Partner at Supernode.vc, f.k.a. Flatiron Investors, where he evaluated seed-stage tech companies and led the due diligence for multiple investments. Before joining the venture capital community, he held senior sales, marketing and product management roles for software companies that have realized over $1.3 billion in exit value, including: Opower (IPO), RAGE Frameworks (acquired by Genpact), Basware (Publicly-traded on OMX), and Comverge (acquired by Itron).
Adams sits on the board of several tech companies, including Ocrolus, which was named the '2020 Fastest Growing FinTech Company in the US’ by Inc. Magazine. He is also on the board of ‘Off the Record,’ a non-profit dedicated to the support of venture-backed startup founders. He has a MSc in Global Finance from NYU and Hong Kong University of Science and Technology, a MBA in Corporate Finance from Northeastern University, and a BA in History from Brandeis University where he played four years of varsity baseball.
VatorNews: What is your investment philosophy or methodology?
Nick Adams: At the highest level, we are a New York-based seed stage fund, focused on the next generation of data tools. What we mean by that is that most projects using AI and machine learning still fail, and AI and machine learning are still in their infancy. The reason why we started Differential was really based on my experience working in the startup world, selling a number of data-oriented, machine learning solutions from the last iteration of what was possible with technology from the early 2010s, combined with my partner David Magerman, who’s an extremely accomplished data scientist as a practitioner; he has a PhD in computer science from Stanford, but he also was one of the primary architects behind Renaissance Technologies and their quantitative hedge fund that's set records for returns for the last few decades. So, we put our brains together and saw that the next iteration of tools that will be deployed at the enterprise level, beyond what's installed today, which is mostly still on-premise software and some cloud software, will truly be a substantial leap forward in terms of machine learning and data driven capabilities compared to what was possible the last time enterprises upgraded their infrastructure. Couple that with the modernization and the consumerization of business software tools to match what we gotten used to as consumers 10 or 15 years ago, as mobile phones and smartphones became much more pervasive in society.
Those two things combined are really what brought us to our investment thesis, which is really two things at a more practical, granular level: one is applied AI solutions. So, in a business function or vertical where the AI and machine learning is solid, both from a process perspective and from the tools being used at the startup, so that, on a transaction by transaction level, they're implementing scalable and explainable AI solutions and gathering really interesting proprietary data sets for each of those implementations. The next layer of that are tools to help professionalize and institutionalize data science and machine learning as a function within companies. So, there have been a number of horizontal platforms and tools to get started with machine learning and AI, but when you really want to succeed at scale within a large enterprise, in particular, you really need to have a bottoms up infrastructure that facilitates professional machine learning, that helps avoid a lot of the issues and concerns around cybersecurity, around explainability of algorithms, and certainly around user privacy as well.
VN: Obviously, AI and machine learning are becoming pervasive in basically every startup that you see coming out, so are there specific verticals that you'd like to invest in right now?
NA: There are certainly a lot of opportunities in the FinTech world. Financial services as a whole is definitely a laggard with new technology adoption; they’re starting to catch up quite a bit due to the pressure from FinTech companies that are out there and moving a lot faster. And then just any business function; we certainly have seen things across sales and marketing and the acceleration of product growth as a sales and revenue driver of companies. How do you become much more data driven in your sales approach and implement tools and features and functionalities to facilitate more organic growth and usage of your product? So we're very interested in sales and marketing, and also in the hiring function: we've invested in a couple of companies recently that are actively involved in enabling more efficient and modernized hiring practices, while reducing bias and discrimination. Those are a few we've been looking at, and have deployed capital in already. Certainly, we continue looking at other really interesting deeper tech areas like quantum computing, where we've made one investment so far, more on the project management side of things than through hardware, but we're constantly looking into new deep tech solutions that are coming on the market.
VN: What's the big macro trend you're betting on?
NA: The macro trend is that we're still in the early days of actual deployable, scalable, machine learning adoption, and there have been a number of catalysts that, frankly, have come on faster than we even anticipated to accelerate the adoption of these tools at scale in various parts of society. In terms of truly being able to build algorithms that produce outcomes that are reproducible and explainable and reliable, we're still in the early days of having that full infrastructure stack implemented and the resources available to data scientists to implement this at scale without running into all the problems we talked about around cybersecurity and privacy and explainability.
What's happened is we've had a massive corporate tax break passed in the US a few years ago that, despite what was being said in the political world at the time, is less about building new plants and so forth and has really accelerated technology adoption by large companies. Certainly COVID, and the shock to the economy and a system of remote work, and facilitating that quickly, securely, and in a productive way, has accelerated the adoption of technology beyond what we ever could have probably even planned for in our investment thesis. And, lastly, that has accelerated even further the move to the modernization of the consumerization of enterprise tools to meet what we're used to in our day to day life. So, those are the biggest things that we're looking at that have moved the trend from that last iteration of what's possible to what's possible today with machine learning.
VN: What is the size of your current fund and how many investments do you typically make in a year?
NA: We wrapped up our Fund I, which is a $20 million fund, back in March of this year; we’ve launched Fund II, and we’re targeting a $60 million fund. Our normal investment size is somewhere between $250,000 to $2 million for our first check in. We lead and we follow, depending on the situation for any given round. So, generally we're investing in rounds of $2 to $5 million, in most cases; I used to say $2 to $3 million, or $2 to $4 million, but certainly round sizes have grown in the last year or so. In Fund I, we lead exactly half of the rounds of the 16 companies that we invested in, and generally take a board or board observer seat for most of our most of our investments.
VN: What stage/series do you invest in?
NA: Broadly, I’ll say we’re seed investors. I tend to think of seed as having three seed rounds to it: there's a pre-seed, a seed, and sometimes a late seed these days. We invest in all three, and we calibrate our expectations a little bit around which stage we think we're investing at. Generally speaking, a pre-seed investment for us is not necessarily pre-product, but usually it's a working MVP, maybe some early pilots, and ongoing discussions for initial deployments, but probably not necessarily revenue at that point. Usually we'll invest a slightly smaller check at the pre-seed round and be in a position to lead the seed round in 12 to 15 months if we want to and need to. The seed stage is generally where we see that the team is a bit more fleshed out, we have some indication of product market fit, maybe the initial MVP is built, initial deployments are happening, or have happened, and there's some path to a repeatable sales motion in place. That's generally pure seed in our mind.
Last is that late seed round. So, we will occasionally invest in a company that maybe has six figures of recurring revenue, if that's the right measurement for that particular sector, and some diversification of customers who will pay a little bit more for pricing in that round because we think there's some catalyst that another $4 or $5 million can get them to that Series A. Generally, what we're looking for is to return 2.5 to 3x our capital in about 18 to 24 months when we make a late seed investment. We've made one of those out of Fund II already and invested in a company that we think could be at $2 million recurring revenue in the next 12 months or so.
VN: It does sound like you do have some numbers that you want to see, at least in the seed and late seed rounds in terms of revenue and users.
NA: It depends on the sector, and what the go-to-market strategy is. We don't do a whole lot of consumer investing, so users aren't always as relevant for us. That said, years ago, we were pretty firm on only wanting to invest in direct sales, go-to-market channels, and strategies. The reality is there's just such a bottoms up and what I would call a B2C2B sales motion now in the economy, where companies are selling to individuals either freemium solutions or very low cost solutions that can start out with one person or one team in the organization, and you build in enterprise features over time. Everybody's trying to replicate what Slack did when they hit their really big growth curve. We are definitely investing more into those types of solutions now. So, user metrics are becoming more important but, the reality is, one of the scariest things I hear in a pitch deck, or in a pitch meeting, is that a company is going to use that Slack direct-to-consumer sales approach from day one. The reality is that that just doesn't happen that much; even Slack themselves begged and borrowed for dozens and dozens of teams to adopt their platform and get feedback from their friends and former colleagues before they really were able to build in those features that led to the unbelievable organic product-led growth that they experienced. I still even think of begging one or two users in a company as direct sales at the seed stage.
VN: Slack was actually an internal tool for a gaming company, and that's how they started.
NA: That’s exactly right, you’re spot on. They were an internal tool for a gaming company and then they spun out and the product they built wasn't really flying and then they pivoted and I think they had something like 50 teams on their platform that they just begged and asked to use it and got feedback from before they really started to build in more, let's say, predictable pricing and scaling model.
VN: When I ask VCs, “What's the most important thing to you at the early stages?” and the answer I get from everybody is, “the team.” So, when the team sits across from you, what do you want to see from those founders and those entrepreneurs to make you want to invest?
NA: I don't actually think the team is the most important thing. In my experience, you can have the greatest founder in the world sitting there but if you're too early or too late or in a market that just doesn't exist because it shouldn't exist; it doesn't matter who the founder is, for the most part, you're still probably not going to do great as an investor, which, at the end of the day, we still are. So, I actually think the market and the market timing is most important to making an investment decision. Unfortunately, it's the hardest thing to dilengence; you're going on some science and quantitative metrics, but it's also a lot of art and digging in the weeds and looking for indications in the market that there could be a general tailwind but also, potentially, some other triggers that accelerate that adoption. So, I usually say the market is first, no disrespect to founders, and then the founders are right behind that, then last is product.
At the pre-seed, we tend to be a little bit more inclined to invest in technical founders who really have a subject matter expertise in the space that they're going after and the problem they're solving. We want to see either a co-founder who can really dig in and lead the go-to-market strategy when that is appropriate or, at a minimum, technical founders who also appreciate and embrace sales into their culture, which isn't always the case. A lot of times you have founders who think you'll build the best product and it'll just fly off the shelf and I rarely ever find that to be true, so I really like technical founders who know they might need some help on sales and marketing and aren't afraid to go out and bring those people in. At the seed stage and late seed stage, we definitely look for a better, more well rounded team that has the appropriate strategy for founder-led sales but also thinking about the right model for who's going to come in and sell the product and how. If those things are in place, then it definitely makes our life a lot easier as investors and the value that we need to add to a company.
VN: The third thing on your list was the product. What are you looking for, specifically, that makes you want to invest?
NA: We probably do put some more emphasis on product given what we think our differentiator is in terms of identifying real, scalable, AI solutions versus every deck that has AI slapped in there as a marketing mechanism. So, we do spend a little more time actually thinking about this.
I'd say these are two things we look for: one is the types of founders that are just genius AI and machine learning technical and experience in their sector, which we love. We're not necessarily looking for the next great AI algorithm that's going to be out there; that’s just really hard to invest in. Big companies put a lot of time into this, universities put a lot of time into this, and odds are that somebody Andreessen Horowitz or NEA or one of the bigger funds is nurturing that founder out of Stanford or MIT today. So, what we do look for are people who have experience of building these other tools and have hands on experience and have lived the life of a data scientist, of a data engineer, of somebody responsible for AI and machine learning deployments. We want someone who can say, “When we were implementing XYZ algorithm or solution at whatever company I worked at before, here's the problems that we face and here's a solution we went out and built for that.” So, we look for that type of expertise. Short of that is just really good, solid engineering knowledge and processes. My partner David, my much more deep tech co-founder, calls it “SMOP,” or “simply a matter of programming.” We love investing in SMOP companies, but we like to bet that the team knows how to actually do the engineering well, document it properly, and implement these tools appropriately. That differentiator is really critical when it comes to actual machine learning solutions because there is so much that can go wrong from your data sets to your data labeling to how you create and test your algorithms to how you deploy them and then get feedback and change them over time. The classic statement that I used as a product manager is “crap in, crap out,” and we look for teams and solutions that know how to avoid that exact issue.
VN: You had mentioned about fund rounds getting large over the last year so I really want to do a little bit of a dive into that and what's been happening. When COVID hit there was some trepidation in the VC world about deploying capital; obviously, that didn't happen as last year was a record breaking year and 2021 is going to be another record breaking year. Round sizes are actually getting bigger, and so are valuations. So, talk to me about what that means for founders and companies.
NA: They've grown a lot. Last year, at this time, I was preparing for my annual LP meeting and doing some research on what our initial pre-money valuation was in all of our investments, particularly in the AI and machine learning space, across our Fund I portfolio. I was looking at what the industry benchmarks were and I could pretty reliably say that the true seed stage valuation for an AI or machine learning company at that point was around $7 million. We were pretty close to that for our entry point, even a little bit lower. This year, there's a funny disparity: I was looking at the PitchBook data the other day and, if you look at an angel or seed round right now, they say the pre-money valuation is little over $6 million, but the Series A valuation today pre-money is $25 million. So, there's a huge gap. What I would expect a year ago was a Series A closer to $20 million and a true pre-seed round or angel round was more like $3 or $4 million valuation.
Everything's just accelerated and there's a few factors that have forced this: part of it is reporting. I'm on the record often as not being a big fan of convertible instruments. I get the value of them in a bunch of instances but I'm not a fan. It’s caused a lot of noise in the market, and pain that hasn't quite been realized yet, in terms of dilution. But, a lot of times, these safes and notes don't get reported to the SEC because they don't need to, like doing a proper priced round, so what you see advertised as a seed round or an A round is actually a series of notes and safes that all got rolled up into, ultimately, a priced round and reported as one number. So, sometimes your Series A at $25 or $27 million also has a bunch of other notes and things like that rolled up into it, meaning the data is just skewed. We're going through a bit of a transition in terms of how we report it and how we tag what’s a seed deal or pre-seed deal or a Series A, because the numbers are just all over the place. The other factor is that there is a ton of money out there; there’s $400 billion of dry powder sitting in venture capitals still. For the first time this year we've seen the really big funds coming into actual seed rounds and investing $6 million on $18 million or $20 million pre-money valuation in a pre-product and pre-revenue company, and I don't think that's sustainable in the long run. For these huge funds that have so much capital to deploy, putting out $2, $3, $4 or $5 million dollars into a seed round is just a placeholder for them to ultimately go write the $20 or $30 million check that they want to at the Series A or Series B. So, there's a lot of money out there, but there's a lot of other factors playing into it as well.
VN: What does that actually do for those companies and those entrepreneurs who get those big checks? Does that make it harder for them to then raise their Series A because the expectations are bigger and the companies have to justify those bigger rounds and valuations?
NA: The answer right now is we'll see. My feeling is that it puts an awful lot of pressure on the next round of funding. We're seeing Series A valuations sometimes upwards of $100 million dollars right now, and for companies that have limited to no revenue, and I'm not quite sure how you grow into that as a business. I will say that the M&A and the IPO market are equally hot right now and there's a burgeoning secondary market for early shareholders to get liquidity like we haven't seen previously, so there are a lot of dynamics changing in the market for investors and founders, but I have a hard time believing that companies will continue to pay the multiples they are right now for acquiring Series A, Series B, Series C stage companies and having productive outcomes.
So, there’s going to be a lot of pressure on every stage of startups going forward, and the ability to grow into the next round of funding. Right now, it's still manageable at Series A at most companies but, after that, there's some big numbers there. I tend to think that the principles of accounting tend to return at some point, even when we have these wild, overheated markets that we've never quite experienced before. At some point, principled accounting will return and rear its ugly head and there'll be a bunch of cuts in valuation and probably down rounds and things like we used to see in the market 10 or 15 years ago.
VN: There are many venture funds out there today, how do you differentiate yourself to limited partners?
NA: There's a couple things: one is that most people still believe that we're in the early days of AI and machine learning. As LPs get more involved in direct investing, their immediate question is always, “how can you tell?” Every pitch deck, every company, says they're doing AI and machine learning, and the really fortunate thing that we have as seed investors is that David and I have both lived in this world, me from the product management and sales and marketing side of things. I've sold a lot of fake AI and I know how painful it is to try and manage that on the back end, and not have things work. I always say that most of the AI machine learning I sold in the past required an awful lot of band aids and bubble gum on the back end to make it work in the way we said it was going to but it was always a ticking time bomb before we needed to really re-architect everything. And, again, from a technical perspective, I’ll put David's abilities up against anybody in this world; it's really unusual, actually, for a VC fund or a seed fund to have somebody with his AI practitioner experience as a general partner. So, when I started Differential, without him initially, I was begging, borrowing, and stealing resources like him to try and diligence companies in an appropriate way, and it was hard. I had sales level, product manager level, technical experience, and I would get fooled sometimes too. When we put our heads together, I found we were really good at making informed and comprehensive decisions around what would check all the boxes that we need to look for in an investment.
Nearly as importantly, once we do deploy capital for a seed stage fund we scale really well, both horizontally and vertically in terms of our portfolio support. I mean that, not in the sense of creating platforms and running events and happy hours and stuff like that for our founders, necessarily, but across our team, including our other partner Mitchell Kleinhandler, and across our operating partner Jeremy Kirsch, who was my boss at Opower; we took them from zero to IPO and very few people have ever done in the software world what Jeremy’s done in terms of sales. We can plug ourselves in in a lot of different areas across an organization and, as such, we operate very flat. I can tell you right now, there are exactly zero CTOs in our portfolio that want to talk to me about AI and machine learning, best practices and infrastructure scalability; they go straight to David for all that. And, likewise, David doesn't have a whole lot of hands-on experience in helping a company get its first few customers, manage churn, manage delivery expectation, getting references, and so forth, and a lot of times me or Jeremy or Mitchell will work directly with the CEO or the chief revenue officer of our startups. So, that's what I mean when I say from a horizontal perspective we tend to divide, just like when we run our diligence process. And vertically, rather uniquely for a seed fund, it doesn't always make sense for us to stay on a board or stay hands on involved in a company through Series C, D, E, M&A, or IPO. We can, and we've managed M&A transactions and IPOs, and we’ve managed bankruptcies and other hard issues that pop up when things don't go as planned. So, when it makes sense we do and we can stay involved vertically in a company for a really long time. There’s value and some comfort in that we're investors that can be with you from beginning to end, potentially, both from a capital perspective and from a support perspective.
That’s sort of a long winded answer but that resonates. Our LPs increasingly are confused by the AI and machine learning market, but understand there's a lot of opportunity there and see us as experts in sorting through the noise on their behalf. And then, because of that, and because of our intentionally small fund sizes, we create a lot of direct investment opportunities for our LPs at the later stages of the companies, which just helps with their access, reduces their overall costs of being an LP in our fund, and they like knowing that we've been there and have had front row seats to each of these companies from an early stage and can make an honest recommendation or whether we think it's a worthwhile follow-on investment for them to make directly into a company.
VN: Venture is a two-way street, where investors also have to pitch themselves. How do you differentiate your fund to entrepreneurs?
NA: The truth is, our answer is pretty consistent across both sides of being a general partner, which is rather unique. A lot of times I feel like GPs have to be bipolar: you present one founder friendly, outward facing persona, and then you have another LP fundraising persona, but ours is pretty consistent. I can't say we’re exclusively founder friendly; there are times where we give really hard news, and we say things carefully and with a lot of thought but sometimes you have to have hard conversations with your founders. If you don't do that I don't think you're doing your job as an investor or board member. We're on their team and we always say, when we're going to invest in a company, before we issue a term sheet, “hey, we've bought into your vision up to this point, we're on board.” We have to go through this awkward phase of a couple of weeks or so where we negotiate term sheets and get the round done and protect our interests as CEO of a company and fund manager of our LPs but, once our money's in your bank account, we're on the same team. We're solving problems together and that candor resonates really well with founders. That's the only difference I would add to that spiel.
VN: What are some of the investments you’ve made that you're super excited about? Why did you want to invest in those companies?
NA: Our first investment was a company here in New York called Ocrolus. They always say the best way to be good at venture capital is to get lucky in your first five years, and fortunately we met Sam and Vic back in 2017; they were our first investment out of Fund I in the beginning of 2018 and I've been on the board there since then. They just recently announced their Series C, led by Fin VC, and we've had other other great investors join the company like Paul Martino from Bullpen Capital, Dan Petrozzo from Oak, Amias Gerety from QED, Laconia Capital here in New York, FinTech Collective. So, we just had a really good group of investors support this company since we did the seed round three years ago.
In this landscape, I look at every fundraising announcement with a little bit of a crooked eye to see if it's truly a company that's growing like crazy and executing well or just fundraising exuberant exuberance and Ocrolus has really executed and performed at an extremely high level. In 2020, they were named the fastest growing FinTech company in the US and have just really clicked on all cylinders. I’m super excited about what the future holds for them because we're getting to that true growth point. It’s very much in the FinTech world, it very much fits our thesis of adding value on a transaction by transaction basis while collecting a really useful, valuable, proprietary data set. And they've done that in the underwriting world, so really doing data extraction normalization, validation of underwriting documents for fintechs and alternative lenders. So, that's where they got their initial product market fit and they are starting to scale that into the mortgage space as well. So, Ocrolus is definitely exciting and our furthest along company.
I talked about hiring a bit earlier, and we invested in a company up in Toronto called Knockri and they're a video and transcript assessment platform for video interviews. So, large companies use them as a first pass at interviewing candidates remotely. As a company, we create a video welcoming candidates asking them some questions that fit the job and the position they're applying for and then candidates can respond to those. Knockri goes in and analyzes the transcripts from those responses and makes a really simple, and I mean simple in a good way, assessment of, let's say, 15 different characteristics that you'd be looking for in this type of a hire. What we really liked about them was all their customers had reported a really high NPS score amongst their applicants, which is important, especially the younger generation of folks coming out of school apply a lot of value to companies that are looking to a) integrate more technology into their hiring processes, and b) are consciously aware of reducing bias and increasing diversity inclusion in their workforce. So, they get really high scores for just being there, and the overall experience.
What we loved about them was the way they built their algorithms from the ground up to be unbiased. As we were doing our diligence and understanding the hiring space, the immediate knee jerk VC reaction was, “hey, this is a crowded market, there's companies like HireVue out there who've been in the video assessment space forever. Why would we want to get involved in this world now? How would we make a seed investment in this market at this point in time?” As we dug deeper we found out that, really, there wasn't much of a scoring algorithm being used in tools like HireVue because it's purely about a black box; it was impossible to explain why HireVue is making a recommendation that it was. Secondly, there was regulation in multiple states, and has since been passed in Illinois and Maryland, and I think in New York and California, that made it so companies companies had to go beyond just assessing certain characteristics and responses of a candidate, not just saying “good fit, bad fit for this position” without some real explainable data behind that recommendation. So, HireVue has largely pulled the "AI portion" off the solution market for that reason.
What we liked about Knockri was the way they labeled data to the way they built their algorithms and deployed them was all done in a very unbiased way. And now, in deployment, through their early customers, we have seen that play out where their recommendations truly are unbiased and are producing a more diverse set of candidates for important roles. So, I really like that space. It's still going to take some evangelizing to get people comfortable with the concept of scoring candidates with the technology, even if it's not making an ultimate decision and then also just deploying at scale and making sure that it remains unbiased, but it's heading in the right direction.
VN: What are some lessons you learned?
NA: A lot. So many, actually, it's probably hard to even count them all. I grew up differently than a lot of VCs, in some ways. I grew up in a pretty blue collar family and didn't have a whole lot of money at certain points, and at other points had very little money. I was a pretty good athlete, so I fortunately had a decent brain and could hit a baseball and got into a good school in the Boston area, went to Brandeis, and wasn't really sure what I was going to do after playing baseball. I had one uncle who was a lawyer, so I went down that path and worked in a law firm during college during my breaks and ended up being a history and legal studies major. I thought I'd work in the law firm for a couple of years and study and go to law school and spend my life as an attorney but after a few months doing that full time after graduation I realized I really could not envision myself as a lawyer long term; I probably couldn't have hated anything more. So, I just started networking around and was introduced to the concept of venture capital and startups at that point. So, at 23, I had my “oh shit” moment and was hired into a startup, a Series A company in Boston called 170 Systems, and just took off from there. I was there for a couple of years, learned a lot about sales, marketing, pre sales, support, and got hired away by a European company and probably got trusted with a little more responsibility than I should have at 24 or 25 years old; I think was employee number six or seven in North America for a company that's publicly traded over in Finland. We grew that North American business from next to nothing, just a little consulting revenue from some European clients, into about a $15 million business over five years. I got very involved in product management and the transition from on-premise software to cloud software, and learned a lot about that process. I went Opower in DC for Jeremy; they're now our operating partner. I was with them before their IPO, and was able to work with a ton of really brilliant, motivated colleagues that are still an important part of my network today. And then I launched a natural language processing solution for a company called RAGE Frameworks up in Boston. That was later sold to Genpact. I went back to school a couple of times; I did my MBA in Boston at Northeastern and did a Master's in global finance in Hong Kong, and decided to make the transition to venture capital at that point when I was about 35 or 36. I joined Flatiron Investors in New York with Laurel Touby, and then put the wheels in motion to start Differential, which I did pretty naively and over confidently, to be totally honest.
I've learned a million lessons along the way. I think where I've made the biggest mistakes, and had the most painful and frustrating failures, is when I overvalued myself. I did it as an employee; I once took a job that was great on the surface, it was more money, a VP of sales role, but a lot of other things about the business just weren't right to me. I thought I could fix all of that but pretty quickly after getting in there I knew it wasn't going to be possible, and had to adjust. So, one blip on my resume was spending six months in a place and one day looking at the CEO and saying, “I made a bad decision, this isn't for me.” So, I learned that lesson pretty hard and I still apply it today to how we invest and how we look at companies. There are times where I see a business and I try hard not to do product management during a pitch meeting. It means I probably like the business, and I'm probably seeing how I would want to run it, but if I apply that lens to an investment, it usually lands us in more trouble than it does good. So, I try very hard to not overvalue myself and what I can realistically do. I applied that previously, as an operator, and certainly it's exponentially more true as an investor when we are only involved a small percentage of the time with the company. So, that's my number one lesson learned.
VN: What excites you the most about your position as VC?
NA: I always say if I couldn't play shortstop for the Red Sox, I'd want to be doing what I'm doing, and both of those things were very true: I definitely could not play shortstop for the Red Sox, which became clear at 22, at the latest, if I'm being generous.
What I enjoy most about our venture capital is two things: one is, every minute of my day is different. And I mean truly, truly different and I need to be extremely organized to be ready for the day and give everybody the attention they deserve, without being distracted, without being confused or late, and it does take some doing and preparation. That part is interesting, it keeps me on my toes. The second part is, I really enjoy being an investor, much more so than being an operator or a founder in a company, because I like being in the background. I really like being the person, the psychologist in a lot of ways, that our founders turn to when they're looking for help on how to deal with certain situations. Most of the problems we face and help with are really around communication and personnel, and how to handle tricky human interactions, rather than business questions. I love the planning and strategy and then all the business stuff that comes up and helping to hire and find the right profile, but it's really mostly that psychology and being the person in the background that supports our founders. And my goal is for everyone to become wildly successful and we're a small, quiet part of that journey. It's fun for me; it was always fun for me as an employee and as a manager of sales teams and marketing teams to make other people successful, and it's true of our own team. I like putting each of our team members into a position where they can succeed and also do our best to put our founders in a position where they can be wildly successful because when they are it's an unbelievable outcome for them, and obviously for us too.
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