Accelerate or Brake?

The current early-stage market feels like driving on a racetrack: constantly alternating between braking and acceleration. To avoid driving full-speed into a wall, I believe it’s wise to put on the brakes when approaching “hot” sectors with high valuations, a red ocean of competition, products that have yet to be validated with prospective customers, and unclear eventual market sizes. It may be wise to invest in particular companies in these sectors, though I believe it only makes sense to do so moving at a cautious speed. In contrast, when you’re on a straightaway with companies that solve huge, overlooked problems founded by teams that have product-market fit, validated pricing and early signs of a repeatable and scalable go-to-market motion where one can invest at a fair risk-adjusted price: I believe it makes sense to aggressively put the pedal to the proverbial metal.

Of course, this is all easier said than done—rare is the investor who will publicly declare they only invest in hot markets and untenably high valuations.

How then, if venture investing focuses on early-stage companies without trailing financials to speak of, can one underwrite wise investment decisions? The most helpful model I’ve found is to look at each investment as a probability distribution across outcomes that changes as we acquire more evidence.¹ To start, we might believe an investment in a promising startup has a:

• 50% chance of returning < 0.9x

• 25% chance of returning between 0.9x and 2x

• 10% chance of returning between 2x and 10x

• 15% chance of returning greater than 10x.

If, for example, we gain evidence that the startup has figured out how to make their sales process repeatable (e.g. their first two account executives achieved their quota last quarter with an 80% win rate, consistent collateral, etc) we will adjust our probabilities “to the right.” Hooray!

Though this is only one data point and there are many things that could kill a promising young company. How, then, can we systematically gather evidence to “update” our probability distributions during an investment process? I like it when founders and investors work collaboratively to accumulate evidence for and against risk in each major category (e.g. product, pricing, team, go-to-market, competitive, technical, market) so that everyone has a good sense for what is likely to kill the company and what is unlikely to do so. It’s said that “more startups die from suicide than homicide”² and I believe it makes more sense to focus on the company and its relationship to prospective customers (less so on its relationship to current competitors).

Sometimes it’s obvious when one should invest. Other times, there’s just too much risk. Unlike other asset classes: startups are not merely museum-pieces to be studied, so we shouldn’t stop there. 

Investors get to roll up their sleeves and work with founders to see if they can turn over a few more cards before investing. For example, something as trivial as carefully asking 5-10 prospective customers “Will you pay $126,237 for this?”³ and listening carefully to the ensuing discussion can meaningfully de-risk a company (i.e. shift our probabilities right) without changing the entry valuation.

Another thought experiment I’ve found helpful is to ask What happens when the market conditions are much poorer than today? 

For some companies (particularly those with high prospective margins and revenue), we’ll shift our probabilities “to the left” and say things like “multiple compression” or “higher cost of capital.” While undesirable, it’s not terminal. 

For other companies (particularly where the word “revenue” is accompanied by waving of hands and “cash flow” cannot be said with a straight face by those with above average honesty), we put a whole bunch of probability mass at zero because in a less-optimistic environment, they’re unlikely to either generate revenue or raise capital. Uh oh.

While there are a number of ways to build a venture portfolio, the one I believe leads to sleeping well at night focuses on companies that build solid underlying “all-weather” businesses, not only aiming for the next round of capital (which may or may not be there)⁴. Of course, life is especially wonderful when portfolio companies ALSO benefit from exceptional market conditions.

Another important aspect of early-stage underwriting is future dilution given most companies will eventually raise follow-on capital. These days, growth-stage companies are raising massive rounds at eye-popping valuations. This means that early stage investors are benefitting from low long-term cost of capital. In practice, this means that disciplined founders (as well as the early-stage investors who back them) should have far less dilution over time relative to historic norms (i.e. another factor shifting our probabilities right). Given the billions more being raised into growth funds, I don’t see this changing any time soon. 

Finally, the topic of inflation is on many a founder and investor’s mind: is this good or bad for early stage venture as an asset class? On one hand, labor is typically the single greatest segment of a startup’s expenses and salaries are increasing far faster than CPI in most relevant roles.⁵ On the other hand, most startups aren’t very “capex-ey” and have relatively short-term pricing commitments with the ability to reprice in contemporary dollars on a regular basis. 

In 1981, Warren Buffet observed that businesses who successfully withstand high-inflation environments “must have two characteristics: 

1. an ability to increase prices rather easily (even when product demand is flat and capacity is not fully utilized) without fear of significant loss of either market share or unit volume, and 

2. an ability to accommodate large dollar volume increases in business (often produced more by inflation than by real growth) with only minor additional investment of capital.” 

Buffett seems to aptly describe many technology startups as inflation-resistant bets, especially those that are so tightly-integrated into their customers lives/workflows they have become “forever-ware.” Further, some founders are beginning to add escalator clauses in long-term contracts that peg automatic price increases to CPI (or another relevant metric), thereby directly hedging inflation risk. 

Overall, the nice thing about thinking about the world in probability distributions (“shapes”) instead of artificially precise numbers is that one is not pretending to know exactly what the future holds.⁶  As I look at the evidence in front of us, investing in high-quality businesses at their earliest stages of growth with a disciplined entry price seems like a pretty good place to be right now even with the specter of high inflation and increased interest rates looming.

1. The nerds amongst us will recognize this as Bayesian inference.

2. This is a Y Combinator-ism that I find accurate more often than not.

3. My wife, Jeni, can attest that I share this this particular method of proving out b2b pricing and product scope risk at least several times per week. Hopefully she’s not yet sick of hearing about it because it’s effective enough that I’ll probably be recommending it for years to come.

4.  More generally, relying on capital flows as the principal component of asset appreciation should be terrifying.

5. Related: it’s unclear whether software engineers or engineering recruiters are in higher demand right now

6. It’s a well known fact that only CNBC, and increasingly Twitter, personalities can do this.