Borrowing Scale

The adage "software startups are cheaper than ever to start, but as expensive as ever to scale" is firmly embedding itself as part of canonical tech wisdom, as what's accepted to be true. I even tweeted about it myself the other day. APIs like AWS that offset initial capex & higher-level programming languages make getting up and running over a weekend easier and cheaper for a small group of hackers. On the other hand, high-priced engineering talent, operational costs, and distribution efforts drive costs up as the product and company scale.

The question I'd ask — will the latter half of this statement continue to be true in 5 years? 10 years? 20? What could lead to a substantial cost reduction in scaling and growing an organization?

Over the past few years, more vendors are emerging as default options for a company when it raises a seed or A that help with the Day 2 problems. Skip the roundabout with payroll providers and banks — just use Deel to build your overseas team. Keep finance and accounting in check from the start with Ramp. Need to sell into enterprise? Streamline a SOC-2 approval with Vanta.

Before, manual effort and labor fixed and massaged these problems, contributing to the costly business of scaling. Now, startups can effectively can not only borrow scale for launching a product, but also building their organizations. These tools won't replace the staff needed to manage these activities altogether, but they do aspire to give employees more leverage.

Furthermore, startups are also building abstractions on the complex, distributed systems required for software to serve millions of users. Okteto provides developers a slick interface on top of Kubernetes, a lower-level primitive for orchestrating applications. Fauna seeks to make a distributed & reliable operational database available through a single API. All of this is an effort to accelerate development velocity and timelines. While this trope probably won't realize for a long time, tailwinds seem to be pushing us towards Chris Dixon's "unicorn of one."

Some additional thoughts:

  1. What are the second order effects of the cost of scale coming dramatically down? An entire investment and employee ecosystem has been built on this premise alone.

  2. I sense security is an opportunity area to add to this list.

  3. Distribution is one area where costs do not seem to be coming down as channels and buyers are inundated. If this is the primary cost driver, perhaps this whole argument is moot.

Looking Back, Looking Ahead II

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] 

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.

Signing Up

“This made venture capital a good match for companies in which there was a medium-sized upfront investment with unpredictable but potentially very large returns. For predictable but small capital investments (a barber shop), for predictable but large capital investments (a bridge), and for unpredictable but large capital investments(the internet itself) venture capital was not the right financing mechanism.” — Sam Gerstenzang, Venture Capital’s Goldilocks problem

“The minute you started giving stock to your employees — you’re in the game.” — Bill Gurley, recent Bloomberg interview

Venture capital has a specific design — portfolios of small bets with asymmetric payoffs that benefits from dislocations in markets or technology. Large asset allocators (endowments, pension funds, sovereign wealth funds) invest capital into the asset class not only because they’re expecting a certain return, but also because they are looking for a certain type of risk (borrowed from Harry Markowitz and modern portfolio theory). Given this structure, assets (startups) must grow and scale fast to test the distribution of possible returns. This is all to say: it’s the incentives, stupid.

In a post-Social Network era, it’s seemingly cool to start a company and raise lots of venture capital. I know why. There’s an allure to the pure form — a small, close-knit team writing code, building products, and saying fuck you to the man.

I see my peers and friends (many under 25 years old) who are starting or want to start venture-backed businesses often subscribe to false beliefs about what it means to do so. There needs to be more transparency on the cover — what are founders signing up for?

Some folks like Fred Wilson and Ali Rowghani have discussed this previously, but this point is worth hammering home, especially for younger entrepreneurs. Starting a startup is more often an exercise in organization building, rather than product building. This means spending an inordinate amount of time hiring executives, managing investors, and ultimately taking care of employees who expect a substantial prize at the finish line. It still takes roughly $50M - $100M+ of capital to generate the returns venture capitalists are looking for, and this doesn’t just appear out of thin air. The money has to be raised and resourced. There are rare exceptions (WhatsApp) where the pure form works out, but scaling into a large enterprise involves untangling people, not technology, problems.

And when it’s not working out, founders are questioned, but may have not known what they signed up for in the first place. Or, they had different expectations of what the outlay of their personal time and energy would mean. Venture, in its best construction, is value additive for all parties, but these investments require significant alignment and understanding.

If you have strong opinions on this topic, I’d love to hear from you. Feel free to reach out at aashay[at]

Scuttlebutt & Seed

One of the best tools I’ve found to understand a market or company opportunity is the cross-sectional view, what Phil Fisher calls “scuttlebutt.” How do competitors, customers, investors, current & former employees all perceive a company and its products? As one spends more time building these cross-sectional perspectives, the understanding of a space compounds on itself.

Seed investing can often be a mode of scuttlebutt research for markets in their formation stage. Markets tend to bubble up with companies arriving in clumps and clusters — this year, it was permutations of Airtable and Plaid — and going through the very motion of early stage investing over a few months or a year can land you in front of competitors vying for a similar prize, building on a dimensional view. The tradeoff made with receiving this lens into new markets, technologies, and products is potentially a lack of ability to make the most of it. The window of opportunity for seed investments is tight with limited bites at the apple.

I joke with my friends in traditional venture or growth that they have the luxury of time to let a market play out a bit, but I’ll concede the competition they face is stiff. I may have taken a view towards investing that hinges on consistently picking category-defining or market-dominant companies, when there’s less pressure to do so as AUM decreases. Yet, the possibility of being involved with those businesses that grow for years and define generations is the allure of the venture business — there’s always an opportunity cost with missing, regardless of stage.


“Only slight knowledge and simple machinery went into the manufacture of the first Ford. No trained engineers were used. The frame of the car was moved manually; it helped that it could be lifted by two men. The modern auto factory, hi contrast, is itself a complex and closely articulated machine. Nothing is done by muscular effort. Computers control the flow of parts and components. Only the hideousness of the product, on occasion, reminds us that human beings are involved. We see here one reason for the increase in capitalization of Ford from £30,000 to £2,200,000,000.” - JK Galbraith, Reith Lectures 1966: The New Industrial State

The maturation of the automobile, both as a technology and an industry, introduced increased complexity, planning, and organizational bureaucracy into the production of individual cars. As it became a more legible technology, layers were added in, and the process of development and assembly became highly mechanical. Any sense of craftsmanship and dynamism was all but lost. 

In a piece from the beginning of this year, Ben Thompson points to the full bloom of the software paradigm, writing, “In other words, today’s cloud and mobile companies — Amazon, Microsoft, Apple, and Google — may very well be the GM, Ford, and Chrysler of the 21st century.” (The whole post is worth reading). While we are far from seeing the totality of impact of software on our lives, I have been thinking quite a bit about the state of software business models recently. While my lens does not stretch back many years, there are a few things worth noting that could support this claim:

  1. Beyond Equity-Based Funding Models -- In Public to Private Equity in the United States, Michael Maboussin and his team note, “buyouts of software firms, a sector with above-average multiples, rose from 6 to 17 percent of all deals over the past decade.” Buyout arrangements are reserved for industries with hard assets, and financing software with debt demonstrates a sophisticated understanding of the recurring nature of enterprise software. 

  2. Coalescence of Capital -- Venture investors seem to have collectively agreed on funding a few categories -- collaborative (and horizontal) applications, infrastructure software and developer tools, fintech rails, and vertical SaaS + marketplaces. I kid slightly, but the level of pre-emptive investment in these categories signals to me that there many assumptions baked in about market size, runway, and margin structure at steady state. 

  3. The “Markets Driven” Investor -- This hypothesis is far from fleshed out, but I tend to see more specialist investors winning competitive deals (again, not a translatable signal to eventual return) vs. generalist investors. One can point to the prevalence of platform funds (which host a cadre of specialist GPs), founder sensitivity to the cap table (picking an investor who shares POV and understands the space), and network effects stemming from being in a portfolio cluster. In some eyes, the prevalence of top-down thinking signals the “end of cycle.”

The last argument needs a bit more development, and I’m not exactly sure I’m right yet, but it’s something I’m watching. Another discussion worth having is if venture is “less risky” than previously anticipated and returns are compressed due to capital in the asset class, how should large allocators treat it given their portfolio models?

I highlight all these points to say that it feels as if startups have become more mechanical in the past few years. Some categories even seem formulaic i.e. “Building an OSS business? Here are steps 1, 2, and 3 to world domination.” I’m guilty of propagating this myself. There’s certainly merit to guiding heuristics and frameworks, as founders should not have to make easily avoidable mistakes. But when the steps for idea generation, reaching product-market fit, and company building become too concrete and almost algorithmic, its worth questioning. 

The beauty of startups is in their dynamism. They benefit from uncertainty, while big companies falter from it. A startup benefits and captures values from small dislocations and changes that lead to massive overhauls in adoption and behavior. The inspiration for this post came from a re-reading of Elad Gil’s “10X Your Business.” He writes, “ Do x, then y, then z, each of which has a linear increase in the value of the product or company.  I think it is good to periodically get out of that way of thinking and ask what sorts of deals, adoptions, or customers would completely change the game for the company. These things should be at least borderline realistic.”

Our mental model fantasies have relegated us to the fact that Google’s structural, product-driven network effects always contributed to its dominance with limited to no hiccups. History gets re-written. We rarely talk about some of the initial distribution deals that jump started the engine. While software continues to mature and that force feels insurmountable, I would love to see more of this thinking Elad pointed to in our ecosystem now. One recent example was Kustomer willing to buy out its competitor Zendesk’s contracts as a means of customer acquisition. It was brash, bold, counterintuitive, and unexpected. It’s too early to tell how that exact chess move will pan out, but I applaud the strategy.

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