Almost exactly a year ago, I wrote a piece titled “Looking Back, Looking Ahead” sharing my reflections after spending ~6 months as an early stage investor. While there was positive reception, the post served as a guidepost for me to orient around and build on. This year, I won’t comment on the macro narratives of 2020, but rather, share more “notes from the road” that have come to light through the past year. The surface area here is limited to personal observations about what I know and learn about — the venture industry, early stage startup ecosystem, and software markets. Each of these vignettes could command an individual post of their own, so think of them more as jumping off points for conversation.
If you’re interested in discussing any of the below, please reach out at aashay[at]haystack.vc
Venture & Investing
Investment sizing is a function of conviction. Far from an original thought, a close friend shared this with me as I was thinking through different allocation amounts in a round. If you’re wrong about an investment in venture, you lose your principal, but the position size determines the magnitude of losses. Vice versa for gains. While this quip may seem intuitive on its head (partially derived from the Kelly criterion), it has been harder to put into practice because of the questions surrounding conviction. Where do you choose to find it as an early stage investor (given limited data)? How does one define deep vs. narrow conviction? When should one “borrow” vs. form conviction independently?
Increased investor specialization helps drive valuations. This argument may be overly simplistic, but I’ve seen game theoretic dynamics play out in certain categories (fintech, SaaS, infrastructure) partially driving up early stage valuations. If you view the venture industry through the barbell lens, platform funds are growing larger and larger on one end of the spectrum. A division of labor is the efficient way to organize a large organization, thus the increased presence of specialist investors within big firms. As one constrains the investable universe, it may be easier to rationalize the inevitability of certain outcomes within a category. With that in place, the associated opportunity cost (and career risk) of *not* doing certain deals is too high — you’re left with high prices and auction dynamics.
Seed is the competitive frontier. Now what? Over the past year, I’ve spoken with a handful of GPs who used to write $8M - $10M checks at the A now comfortable leading seed rounds with $2M - $4M investments to get their foot in the door of a promising company. Traditionally, Series As were the competitive rounds for companies who had recently hit PMF, but that game has leaked into seed. On one hand, this could just be a reshuffling of round names; on the other hand, companies could be capitalizing and structuring ahead of traction in a world where constraints matter.
Early stage venture shares little with other forms of capital allocation (private equity, hedge funds). While early stage investing generally needs a shrewder lens and sense of analytical rigor, newer approaches (in the vein of public company analyses) may also be missing the forest for the trees. Startups are inherently imperfect — one could argue they’re collections of individuals building products, not companies. Investing in them entails finding the right sources of information, unique access, along with crafting & understanding narratives.
How does liquidity follow paper gains? I’m not exactly sure how I would frame or parameterize this research, but one piece of data I’ve wanted access to is the resulting liquidity and cash returns of companies that generated 10x paper markups within certain funds or vintages. Right now, with preemptive rounds and an unwavering belief in the expansion of software markets, markups (and TVPI) may be abundant for some managers, but they’re simply proxies for ultimate success. In other words, intermediate metrics are tough.
Personal brand may continue to matter even more for investors, but I personally struggle with the implications of this. Founders now can pick individual investors over institutions, and one way to bypass the traditional gatekeepers to stand out is through a recognizable, unique, and authentic social presence. Some may dismiss this as a flash in the pan, but I’ve seen countless examples of people building real investment careers starting on the Internet, and the trend will continue to grow. My own life wouldn’t look the same if I hadn’t met people on Twitter or written publicly. Yet, I can’t help feeling as if platforms like Twitter have become increasingly noisy and performative (maybe they’ve always been and I’ve just missed something). There’s one question of continuing to stand out in that environment, but if the north stars are investing in and building great companies, what are the best avenues going forward?
Some investors are taking a “value chain” approach to portfolio construction. I see the benefits. In the 1990’s, when John Doerr was at the peak of his powers, he relied on his form of a California Keiretsu. Originally a Japanese concept that dominated their late 20th century manufacturing prowess, keiretsu means a set of companies with interlocking business relationships. Now, as different “stacks” (data infrastructure, JAM, e-commerce) become more legible, investors complement an initial investment in a category with other members in the value chain. The companies may see some product or partnership benefit from working together, but they can also collectively reinforce a narrative about their corner of the market, thus bringing their future cost of capital down in tandem.
I admire investors who have nailed their personal process because it’s easy to give lip service to, but incredibly hard to implement. In an ecosystem with a high volume of deal activity, one can get lost chasing shiny objects. The investors I’ve come to admire the most are intensely clear about what they look for an investment, and this clarity compounds on itself through the sourcing, selection, winning, and management phases. For example, read this doc from Brad Gillspie, of IA Ventures. I read and reference it often because it not only has informed my thinking on the mechanics of seed stage startups (bearing a hypothesis-driven mindset, staying capital efficient, “earning your burn”), but also helped structure my approach and personal process. Finding conviction in a process is difficult as a younger investor because you don’t have enough data or historical runway to train your algorithm. As I look forward, finding the path to trust my judgment and making my guiding principles more concrete is high on the priority list.
Markets & Sectors
What’s being underwritten in fintech infrastructure? Payment processing. Card issuing. Identity verification. Payroll APIs. Fraud detection. Tapping into ACH. Kicked off the Plaid acquisition in January, this has been a banner year for fintech infrastructure investments, especially in the venture market. I’ve asked myself what founders and investors are projecting into the future if the number of neobanks and PFMs that use these services might seem small on its head. Growth is part of the equation, along with digital financial services leaking into other categories (online marketplaces, healthcare), and a belief in the power of accumulated data. I’ve started to frame fintech infrastructure in my head by looking at global spend (software, services, human capital) supporting traditional financial institutions (banks, insurance companies, etc.). It’s a rough heuristic as financial services will look different when encoded in software (did someone say crypto?), but the magnitude is mind-blowing.
The lines between “SaaS company” and “API company” have blurred. In a previous era, there were three components of enterprise software & technology — business applications (thumbs up for system of record), middleware (yuck), and infrastructure (boxes and appliances). Many of the API-first poster children (Stripe, Twilio) initially were written off as middleware components. This pair grew to incredible heights over the last decade and paved the way for more to believe you could accrue value in that part of the stack. But, how would you characterize Stripe or Twilio today? Are they integration / infrastructure companies or applications that enable workflows? Somewhere in the middle probably. The lines demarcating an application and an API are blurring — workflow products are quickly releasing open endpoints after launch, and API companies are giving customers UIs and dashboards to deepen engagement with non-technical users.
Dev tool and open source companies are priced to perfection, but I remain optimistic. Similar to the App Annie days following the first iOS explosion in the early 2010’s, investors are tracking projects across GitHub on all levels for any semblance of traction. It’s a funny rotation, because dev tools were considered a terrible place to invest up until a few years ago. The shift is a result of both organizational and technological forces. The increasing number of developers around the world have more sway within their companies to adopt new tools, and microservices & the cloud made it easier to apply a piece of technology to part of a system without overhauling lots of code. Since these projects are public in nature, the markets for them follow the information, and it’s hard to find value (in the traditional sense) as an investor. Yet, there are still dozens of unsolved problems that have now been put in developer’s hands making me bullish — observability & monitoring, tracing & debugging, security & networking, data transformation & analysis, deployment & orchestration, and much more.
I spent more time this year learning about biology and life sciences. If I were to look out at the next several decades, it’s hard not to be excited about the potential of synthetic biology alone to reinvent therapeutics, food, materials, and more. Think about anything that requires some sort of chemical process and replace it with a biological one — the possibilities are endless. At first, it was easy for me to fall into the heuristics trap of “what’s the AWS of bio?” or “what’s the platform play here?” when organisms in the lab work nothing like bits and bytes. We can get closer to making it easily replicable through automation and machine learning, but the marginal cost of biological matter is fundamentally different from software’s. This realization has made me step back a bit from scrutinizing deeper sciences with the venture lens, but I continue to be energized about the future potential of what life sciences can bring us over the next few decades.
The next few years may give birth to consumer Internet companies we can’t even begin to fathom. Predictions are hard. Yet, if applications and infrastructure continue their song and dance, it’ll be interesting to see how current build outs support new waves. These have mostly received attention for their ability to serve the enterprise, but how will they act as building blocks for entrepreneurs to build completely new services? From a big data ecosystem centered around Snowflake to an analytics universe with Databricks at the center to edge networks with Fastly and Cloudflare to the changing nature of the web with WASM or WebGL, the future looks promising.