Startup Risk

Underwriting upside

To paraphrase Sam Zell from the early ‘90s, I think the best advice for late-stage companies right now is: "stay alive until 2025." At series B and beyond, few rounds are clearing—unsurprising given how much exuberance we have yet to unwind. At some point, founders and investors must negotiate in that uncomfortable DMZ within the spread and make a deal. 

Series As, after their own sabbatical, have begun to return with renewed sobriety. Companies with product-market fit and early signs of repeatability¹ are raising at reasonable valuations, particularly those that have products that are “non-optional and don’t suck”, are equity-efficient hard tech, or use automation and AI to improve cost structures.

Now that fundamentals are at the forefront of the venture market, I was particularly excited—and honestly a little bit apprehensive—to dig into the main topic of this letter: risk. It’s something we all feel like we understand yet struggle to articulate clearly.

There’s a particular flavor of risk that startup founders and investors use to make good decisions under uncertainty. Risk that won’t make us sound smart with talk of volatility, Gaussian distributions², or Sharpe ratios. Startup risk has very little math and a lot of mucking about at the messy intersection of people, technology, and products.

Startup risk is existential future risk in the sense of “we just ran off a cliff with (what we hope are) the requisite parts for an airplane and are hurriedly assembling it before… *SPLAT*.” Put another way, startup risk is more concerned about future possibilities than present reality. Unlike public equities where there’s (hopefully) a real business to analyze, startups begin as “default-dead” with little to their name at present. Tomorrow is the prospect of a flying machine while today we have nothing more than a jumble of heavier-than-air parts in rapid descent.

Startup risk is also upside risk. Unlike aviation safety, startup risk is more concerned with “what could go right” than “what could go wrong.” It’s not just because founders and venture investors are inherently optimistic people: they just have a lot more to gain by succeeding than they have to lose by failing. Debt investors, in contrast, generally think of risk as the likelihood of losing money because they’re in the business of avoiding failure, rather than maximizing success. 

I don’t believe a single approach—present vs. future, downside vs. upside—is universally superior, but everyone should know what game they’re playing and act accordingly.³

This letter is focused on the risk startup founders and investors face in achieving—or failing to achieve—future upside. Though there are many different ways to underwrite risk, I’ll attempt to articulate what I believe are the principles behind successful models. Frequently, these principles justify conventional wisdom; however, important opportunities occur when principles tell us to stray from the herd—to call bullshit and refrain from acting or move aggressively while others pause.

“The single most powerful pattern I have noticed is that successful people find value in unexpected places, and they do this by thinking about business from first principles instead of formulas.”
- Peter Thiel

First principles are the “laws of physics” governing startups, hard-to-argue-with premises such as:

• Businesses exist to make money and are eventually valued on cash flows.

• Risk is uncertainty around the ability to generate large, growing, defensible cash flows.

• Startup valuations increase as they reduce risk, all else equal.

• A business with little competition is worth more than a business with lots, all else equal.

• Predicting the macro environment 10 years in the future is really hard.

Starting from such simple concepts, we can explain the structure of the startup ecosystem. These emergent properties are also how the wondrous diversity of biology—from single-celled organisms to a species capable of understanding its own genetics—follows from the relatively simple laws of physics. Similarly, I believe startup first principles allow us to underwrite everything from low-risk, incremental vertical software to high-risk, world-changing moonshots. 

To explain the emergent properties of the startup ecosystem from first principles, I’d like to cover:

1. Why startups need stage-by-stage financing, upside focus, and underwriting the future.

2. Underwriting risk with team, market, business model, and traction.

3. Pricing risk with valuations.

4. Why inefficient and illegible are exciting.

5. Doing hard things: difficult is defensible.

By articulating risk clearly and pricing it appropriately, I believe founders and investors can improve their chances of avoiding costly failures and identifying overlooked opportunities. 

Finally, I’d like to acknowledge the many prolific writers and jam sessions with founder and investor friends that fueled this letter, alongside some of my own battle scars. In each section I’ve tried to pair concepts with examples to strike a balance between rigor and practicality.⁴

Despite my best efforts, I’m sure there will be some things that smart, sophisticated, and successful (SSS) people disagree with and I’d love to make this the start of a discussion. Having multiple ideal points makes startups fun :)

Why startups need stage-by-stage financing, upside focus, and underwriting the future.

In the last market cycle, it seems like venture capital has been an org structure and a portfolio construction model masquerading as an asset class. The venture industry often reduced itself to asking “Will it go up?” and “How much capital can I deploy?” instead of what it should have been asking: “What makes a great startup investment?”

Visionary founders with bold proclamations of the future? Swashbuckling adventures of a small team in a garage in their heroic quest to defeat incumbents? Dramatic near death experiences? “Unique” personalities of many founders and VCs? These are all frequent occurrences that make the ecosystem fun and interesting, but not sufficient to define an asset class. 

I believe that the defining characteristics of startup investing are: 

• valuations that depend on “underwriting” the future more than “analyzing” the present,

• business plans that aim to maximize upside rather than minimize downside, and

• stage-by-stage financing

Underwriting vs. analysis

I use the word underwriting because the dominant factor in returns is the risk of some uncertain positive events occurring in the future—can the team assemble the plane before they hit the ground? In this sense, startup investing is a sort of like buying inverse-fire-insurance: disaster is assumed but the the payout is large if a company can somehow avoid catastrophe. 

Analysis, by contrast, is the ability to understand the past and present clearly with diligent effort. Perfectly valid with respect to established companies, but not helpful when the company is just an idea and was incorporated yesterday.

Once existential risk is no longer the central consideration for returns—when the company becomes “analyzable”⁵—investing in the company ceases to be venture capital in my view. Such an investment might be a perfectly wonderful “growth capital” or plain old equity investment, but no longer “venture capital”.

Underwriting is the art and science of assigning probabilities and prices to future events—good or bad. “Great” startup investing is not unlike “great” insurance underwriting in that the actual risk must be lower than how the market prices that risk to profit. We’ll dig into the concept of non-consensus underwriting—how people can disagree in their perception of risk—later.

Upside vs. downside focus

Unlike insurance which is fragile—small and capped upside with large but capped downside—startup investing is anti-fragile—small and capped downside with large and unlimited upside. Given the spectacular risks involved with underwriting an uncertain future, business plans that focus on small but positive outcomes likely lead to underperforming venture portfolios: failures wash out gains from winners. That’s why startup underwriting becomes attractive only when paired with business plans that aim to achieve massive success where even a 90% failure rate is acceptable relative to the returns from the winners.

Stage-by-stage vs. all-at-once financing

Because valuations start low given the risk at outset, raising capital for the company’s entire journey in the beginning would be crushing to founders’ equity ownership—raising $50M at a $10M pre-money valuation would result in greater than 80% dilution. Instead, raising “just-in-time” capital via a consecutive series of rounds solves the problem neatly: a company can aim to prove out a subset of its risks at a time and raise enough capital to do so. Then, we can repeat this process multiple times until we either fail, sell, or have a massively successful business on our hands. 

In some cases, a company is so efficient that they can achieve escape velocity with a single round of funding. More commonly, though, prospective investors at each round underwrite the residual risk in a company to justify a new (and hopefully higher) valuation.

How do we come up with valuations?

First, It’s worth talking about what should not go into startup valuations.⁶ Unlike valuing mature companies with predictable cash flows, I do not believe it’s possible to reach a reasonable valuation for a startup with a small number of variables divined from financial statements or the market as a whole. What is a startup worth that has $0 in revenue? One might be an infrastructure company with massive adoption of their open source product and another might be a yet-to-be-built social network for cats. When a company has revenue, should we take the “average” seed revenue multiple? That’s probably not right either, because a company with extremely experienced founders is probably worth more than one with unproven founders. 

Startup risk is subjective and nuanced, properties that resist being reduced down to simple models. At the beginning of a startup’s journey, a set of smart and dedicated, yet imperfect, humans aims to will something into existence against the forces of entropy and active destruction. The company exists in an elaborate yet un-choreographed dance with founders, customers, partners, suppliers, and employees—all with their own constantly changing psychologies—amidst the backdrop of rapidly-evolving technology and fickle capital markets.

What input to the Black-Scholes model describes the likelihood of a founder convincing their critical first enterprise customer to bet on unproven software? What part of the capital asset pricing model (CAPM) covers the risk of founder breakup? Where in a discounted cash flow (DCF) do we represent the skill of the founding technical team’s ability to successfully scope and build a product that delights that first customer? It doesn’t take long asking such questions before a sane founder or investor sets such valuation models ablaze.

Because the dominant impact on returns from startup investing is existential upside risk, I believe that getting to a reasonable valuation must depend on underwriting the residual risk in a startup’s ability to achieve its goals.

Underwriting startup risk is idiosyncratic—and I think that’s a good thing. If you talk to 20 successful early-stage investors you’ll probably get 20 different underwriting approaches, indicating that the market is either illegible (i.e. evidence is not clearly available to all participants) or inefficient (i.e. valuations do not consistently incorporate available evidence) or both. This is probably not surprising given the tremendous complexity in building a startup from scratch: each observer has their own set of heuristics for assessing value, making it hard for everyone to agree. The wide variety of underwriting models is also particularly exciting because it suggests that—unlike more legible and efficient markets—superior evidence gathering ability or superior insight are likely to generate superior returns (i.e. alpha) for both founders and investors, a topic we’ll dive into later.

That’s not to say underwriting startups is just rolling dice, though some approach it that way. Some founders and investors routinely produce superior returns and I believe a significant part of that is attributable to underwriting skill⁷.

To reiterate the whole point of all of this effort: by articulating risk clearly and pricing it appropriately, I believe founders and investors can improve their chances of avoiding costly failures and identifying overlooked opportunities.

Most approaches to startup underwriting I’ve seen include some combination of team, market, business model and traction⁸. Skill in underwriting these factors often comes from having seen many companies succeed and fail so as to be able to answer questions such as: Which founder attributes are helpful (or hurtful) in particular situations? How do we correctly anticipate changes in markets? Is a business model plausible? and What evidence leads us to believe the company has “nailed it” or “scaled it”? Not unlike artificial neural networks, the organic neural networks in the heads of founders and investors tend to benefit from lots of training data.⁹

Let’s cover each area of underwriting—team, market, model, traction—individually and discuss how one might evaluate them at the outset, how that underwriting changes throughout the course of a company, and common underwriting traps.

Underwriting teams: Can this team can execute this business?

Every early-stage investor I’m aware of believes that team is the most important aspect of underwriting because great founders have the uncanny ability to figure out just about any risk while those who are merely looking to get rich quickly tend to opt out at the first sign of difficulty. I wholeheartedly agree with this belief, having seen the magic of teams who can reliably conquer seemingly-insurmountable challenges.

“Great” teams are tricky to define precisely because great in one market might be ineffective in another market and traditional credentials—prestigious prior jobs or having more degrees than a thermometer from top universities—are unreliable predictors of success.

Some punt on a precise definition of “great” and opt for the “I know it when I see it” approach; however, I believe that great teams have universally important personality characteristics accompanied by skills and experience that depend on their particular market and business model.

Divining personality characteristics

Some investors have highly developed opinions regarding founder attributes they prefer. Y Combinator (YC), for example a particular viewpoint¹⁰ and Jessica Livingston, a founding partner of YC, is widely respected as having the ability peer into the souls of founders to identify these characteristics after even a very short conversation. While I was working with YC a few years back, I had the chance to see Jessica at work and honestly have no idea how she does it so quickly, but I think the strong values-alignment within the YC community has more than a little to do with her.

Without being blessed with Jessica’s level of intuition, mere mortals can try and understand behavior patterns by speaking with references who have longstanding relationships to understand what makes someone tick. Front-door references—those provided by the team—are usually so biased as to be useless; however, I’ve found back-door references amongst people I know and trust to paint a much more realistic picture. Because the technology industry is tightly networked, there are almost always co-workers, classmates, friends, or prior investors who you already know or can get to within one degree of connection in one or two messages. Given the expectation of working together repeatedly over the years, there’s a strong incentive to be truthful—it always comes out eventually—and I’ve found that people are usually remarkably candid.

When trying to understand someone, I think it’s important to answer some key questions:

• Have they walked through walls to succeed… or did they give up at the first sign of failure?

• Did they communicate problems immediately… or did they deflect and delay?

• Did they do the right thing by their stakeholders… or just the minimum legal thing?

• If they ran a company, did they carefully conserve resources… or spend like a drunken sailor?

• Were they earnest in making commitments… or did things frequently fall through the cracks?

In essence, I want to find evidence confirming (or disconfirming) that someone is moral and ethical at their core with a burning desire to succeed and the discipline to make their dreams into reality.

Market-appropriate skills and experience

When it comes to skills and experience, I believe there are multiple ideal points—there’s no one best founder archetype. Founder-market fit is the idea that a strong team in one market environment might be a weak team in another environment. Because it’s so emotionally draining to build a startup, founders in markets that feel unnatural often can’t push through the inevitable setbacks and give up. Similarly, founding teams that don’t have the experience to be credible to important customers or partners may not be able to get off the ground at all.

Here are some patterns in founder-market fit I’ve observed:

• Obsessed younger founders sometimes paradoxically have more experience than any other age group using new technologies. In the post-dot-com period, for example, early-twenties founders who grew up with the internet having spent all of their waking hours glued to their computer may have had more hours online than any other age group. Even slightly older founders may have had the handicap of “a job” preventing them from spending so much time online during the birth of early internet communities. To that end, betting on a young Harvard student named Mark to build Facebook was one of the better venture capital investments in the last few decades.¹¹

• In enterprise sales driven markets, founders with experience tend to know how to navigate political buying processes and can overcome the dreaded “vendor viability” questions procurement departments use to harpoon early stage startups. Founders with less experience tend not to understand how to navigate complex organizations and have fewer trusted relationships to draw on when convincing large customers to bet on a small, risky startup. Dave Duffield, for example, was 65 when co-founding Workday, a HR software vendor now an ~$80B market cap public company at the time of writing.

• In vertical markets with a lot of domain-specific knowledge, founding teams with experience in a rare intersection of fields are powerful. I’ve found most commonly this to take the form of “software engineering + x”. For example, I’ve seen teams succeed where x = biology, x = healthcare, x = energy, and x = cybersecurity because of their unique ability to build software that takes advantage of their domain knowledge. Scarcity of founder insight can be a powerful force in becoming an “n of 1” company without much competition.

Traction impacts team risk

As the execution skills of a team become apparent, perception of team risk can change:

• For young or first-time founders, perception of risk often decreases rapidly with even a little evidence indicating execution ability and speed of learning. This is another reason I believe YC is so successful—it is a crucible that consistently helps teams have the most productive few months of their lives. Unproven founders who can demonstrate early signs of rapid growth even with small absolute traction substantially increase the valuation investors are willing to pay relative to the same team without those early indicators. 

• For experienced founders, investors are often willing to “give credit for forward execution”, to the high water mark of their past experience. In situations where experience really de-risks the business, this can result in valuations that seem eye-popping if one were to ignore the team and look only at traction. At some point, team risk converges to actual execution which can be a rude awakening if teams expect to be given credit far beyond their past experience.

Traps in underwriting teams

As with any form of underwriting, I’ve observed two common things that can lead to overconfidence in a team’s ability: 

• Projecting: Investors with operating experience imagine themselves in the founding role and ask the question: “Could I execute in this market?” instead of the more important question: “Can THIS TEAM execute in this market?” I’ve found that projection most commonly happens with extremely exciting market opportunities and teams that are not clearly great but also not clearly doomed. Optimism accompanying incredible market opportunities can cloud even a skeptical investor’s assessment of a founding team.¹²

• Credentialism: Backing teams who come from marquee companies or with degrees from top universities seems like a low risk proposition. Indeed, many founders with strong credentials who have “seen greatness” are exceptional; however, fancy logos are correlated, not causal, to success. There are many people who are extremely smart but took the safe road through life—going to top schools and top companies—because they are inherently risk averse and deathly afraid of failure. Inability to take risk or inability to emotionally recover from the inevitable setback is fundamentally incompatible with running a startup. 

• Adversity bias: Some investors take the opposite tack by only investing in founders who have had tremendous adversity of some sort. Given that there are great founders with top credentials and great founders who have absolutely zero credentials, I believe the key is to figure out the underlying personality characteristics and experience that predict success in a given market, not relying on credentials or past adversity to do that work for you.

Underwriting markets: How many customers can we serve and how much will they pay?

The trite way to describe a good market is “big”. Of course, if a market is too small, even a low risk proposition can’t generate a meaningful return. While it’s tempting for founders to go after a small but less-competitive market, there’s wisdom in the belief that “building a startup in any market is hard, so you might as well pick a big one.” Some small markets are not competitive for a reason: they’re graveyards. Such markets might be hard to operate in, the customers don’t really care about solving the problem, or the customer psychology in the market is generally averse to change. Some examples that come to mind include: fintech tools to split the check at dinner, location-based social networks to find what friends are doing right now, and healthcare solutions where there’s no incentive for the ecosystem to adopt.

How do we identify which markets are big? One common method is “top-down” using analyst reports that come up with figures for total annual spend and the compound annual growth rate (CAGR) of that spend. A startup will call the total spend their Total Addressable Market (TAM) then choose several segments of that market to get a Serviceable Addressable Market (SAM) and finally build a business model to figure out what portion is the Serviceable Obtainable Market (SOM) given some assumptions on ability to execute. 

On its surface, the top-down approach seems reasonable; however, I see two major flaws in using it for startups:

• Startups often segment markets differently than analysts do, and

• Startups are most interested when markets undergo unanticipated change.

Startup segmentation vs. analyst segmentation

In B2B markets, each type of customer behaves differently: large customers want different things than medium sized customers who want different things than small customers. Top-down market analyses tend to handle segmentation by customer size well; however, what if the customer behavior also depends on other subtleties such as the unique psychology and needs of individuals or small teams within an organization? 

Consider Airtable, a company whose product would have been thrown out of any head-to-head feature comparison with Excel or vertical enterprise software; however, for many use cases, Airtable was superior. First, it didn’t suck for end users—it was easy to use and well designed. The product had more structure than spreadsheets but was more malleable than typical vertical enterprise software. Because of the breadth of use cases Airtable supported, the company focusing on individuals and small teams who started using the product on their own rather than going after large enterprise contracts in a single vertical. Over the years, the product has become extremely popular and teams have adopted it for a wide gamut of business workflows. Only when the product became mature with a robust feature set and strong security/compliance pedigree could the company win large enterprise-wide contracts.

Had Airtable tried to own the entire spreadsheet market or various vertical software markets from the start, they would likely have lost the battle to incumbents. Had they chosen just a single vertical, they would be legitimately in a small market. Instead, they chose to re-segment multiple markets and attack incumbents from below. If early investors were short-sighted and relegated Airtable to a small segment of the spreadsheet market without anticipating their ability to convert non-consumption—selling to customers who bought nothing before—and replace various categories of vertical software within larger organizations, it would be easy to reject the company out of hand with a curt “market is too small”.

Consumer markets often work the same way: great new products unlock “latent” demand. Consider how Uber massively increased the size of the ride-for-hire market; however, trusting any market analysis that merely showed Uber taking share of the existing taxi TAM would lead you to toss the company in the “market is too small” bucket as well.

Overestimating market sizes also happens, but with less dramatic results. Nearly weekly I see a cybersecurity startup claiming to have a $10B+ market because they’re trusting analysts who amalgamate salaries, consulting fees, and products from multiple distinct subcategories. Trusting this sort of market sizing might make you feel good about an investment on paper, but when it comes to delivering revenue, customers will pay just for the software that the company can build—a much smaller figure than the analyst’s total.

Top down market reports often do not have the nuance startups need to re-segment markets, resulting in unhelpful TAM, SAM, and SOM estimates. I don’t believe analysts intend to deceive unwitting startups; instead, the primary customers of these reports are large companies, investors in large companies, and customers of large companies—simply a different audience. To startup founders and investors, incorrect market sizes aren’t a trivial matter: they’re dangerous because they lure us into investing our lives and our fortunes into a market segment that just isn’t that big—a juice that isn’t worth the squeeze—or dissuade us from pursuing a lucrative opportunity that doesn't line up neatly with how segments have been described historically.

Unanticipated market changes

What happens if a market’s segmentation is straightforward, but the market is undergoing rapid growth? Most market analyses have some way of accounting for changes to market size over time. Typically analysts will observe past changes, speak with market participants and come up with a CAGR for that market as a whole as well as individual segments within it. These sorts of growth models work well when it’s “business as usual.” 

Because of scarce resources relative to incumbents, startups can rarely operate in the land of business as usual. They more often operate in the opportunistic land of “Why now?” Why is now the time where businesses are willing to move from their old and expensive on-premises software to the cloud? Why is now the right time when employers will make their existing workforce more efficient with AI vs. opening more headcount? Why is now the time when there will be higher demand for datacenter GPUs? Startups are pirate ships finding the perfect opportunity to attack, not massive navies steaming directly into head-on conflict.

For example, consider the market’s surprise at NVIDIA’s rapid growth in data center revenue. Applying a CAGR to trailing revenue shows the following prediction: 

However, because of the explosion in demand for AI training and inference, NVIDIA’s datacenter business exploded, resulting in the following actual revenue—growth that would be completely missed by a simple CAGR model. 

I believe that CAGRs are too coarse grained to work in a world of unique insights. Successful founders often have heterodox beliefs that aren’t appreciated by the rest of the market for one reason or another. If an insight is truly unique, it won’t show up in an analyst report (otherwise, it’s not so unique, is it?). It’s this belief that founders and investors share that outsiders do not that often gives the startup just enough of an edge to overcome scale advantages of incumbents.

Bottom-up market sizing: an alternative to top-down

Instead of relying on analyst-derived segmentation and growth rates, “bottom-up” market sizing aims to create a market hypothesis that a startup can validate with evidence. The inputs to a bottom-up market sizing model are concrete, have ground truth to back them up, and generally take the deceptively simple form for any company with a “build it + sell it” revenue model:

market size = # customers * $/customer

The most important aspect of that equation is $/customer (i.e. the amount of money each customer will pay us per year) because founders must first spend a lot of time with customers to figure out which ones will actually pay them and how much they’re willing to pay.¹³ Sometimes this answer is segmented (e.g. mid-market enterprises will pay a different amount than large enterprises) and our overall market size model becomes the sum of multiple subsegment market sizes. At the end of this exercise, though, the startup will have a high-confidence estimate of what customers in the real world will pay for their product—validated price points.

To determine # customers, startups usually have a pretty good idea how the market segments out after asking customers to pay for a product—it’s the type of customers who at least don’t say “no”.  There are often common characteristics behind the customers who say “yes” where we can rustle up publicly available data to estimate how many customers share those characteristics. 

There are many other indirect revenue models, such as advertising, lending, or market making where the same principles apply: validate how much money you make each time you turn the crank then figure out how many times you can turn it per year.

Ideally, this exercise shows that the most immediately serviceable customer segment could result in billions in gross profit. If that’s not the case, we can ask concrete questions about how we might expand our initial market:

• Do we believe the number of customers in the segment will change (e.g. growing market)?

• Do we expand to other segments?

• Can we increase pricing or can we sell more stuff to the same segment?

Questions like these neatly connect market size with business model so founders can estimate the impact of narrowing or expanding scope. Generally speaking, the more different products we sell or the more customer segments we serve, the greater the business model risk. Ideally we can focus on doing one or a very small number of things very well and can get to quite a bit of revenue before we are forced to do more.

A startup that succeeds will eventually tap out its market and aim to expand in some way. Granular bottom-up market sizing gives us concrete data on when we might have to do that. At $10M, $100M, $10B, or $100B in revenue? Knowing the texture of the market allows us to propose a business model, informed by evidence, that has the possibility of getting to $200M+ gross profit with a path towards $1B+, which is approximately when I believe a business is ready to go public.

Underwriting business models

A business model is a startup’s “critical path to paradise”: the key hypotheses that the startup must validate to become a large and sustainable business—a fully assembled and working flying machine. Critical path hypotheses typically include some combination of:

• product—What will our product do and what will it not do? How will that change over time? How long will it take to build? Are there hard technical or scientific challenges we must overcome?

• customer psychology—Who will buy it and why will they buy it? How will that change over time?

• distribution strategy—How will we reach customers and get them to buy our product? 

• unit economics—How much will customers pay? How much will it cost us each time we sell our product? How does that change with scale?

As with hypotheses in other fields, business models can be consistent with reality, inconsistent with reality, or somewhere in between. If a business model is inconsistent with reality we have a 0% probability of proving it out. Any eventual outcome multiplied by 0% equals zero—clearly not a great investment strategy—so we want to know as quickly and as cheaply as possible.¹⁴

At the very earliest stages of a startup with very little evidence to work with, how do we pose hypotheses that have a reasonable chance of being true? Luckily there are a few techniques:

• Finding reference classes: Even without evidence, we can look at norms in the industry, e.g., typical earnings for a salesperson who sells similar products to the target customer base, amount of effort necessary for other engineering teams to build similar products.

• Early experimentation: We can get outside the building and talk to prospective customers. For example, we can determine the price a set of customers is willing to pay for a product. We may find that for that price, there’s no way we can afford the sales team required to sell the product to them (common in selling software to small businesses or individuals). In this circumstance, a startup must go back to the drawing board to identify a modified set of hypotheses that have a higher chance of success, for example by selling an expanded product to the same customers, choosing bottom-up distribution, or moving up-market to larger customers.

One flawed approach to formulating hypotheses that can never be proven wrong: be vague. A correctly formed hypothesis is a falsifiable belief—there is possible evidence that could prove it wrong.¹⁵ A vague unfalsifiable hypothesis is not specific enough be proven wrong and is therefore not very helpful. Of course, no well-meaning investor or founder would knowingly build a business model around unfalsifiable hypotheses; however, it’s easy to accidentally do so using big phrases that sound true, but aren’t.

“Don't use a five-dollar word when a fifty-cent word will do.”
- Mark Twain

Big words and phrases often serve as a “cognitive stop sign”: people believe they understand what such phrases mean so they don’t think through the details.¹⁶ Consider common startup strategies for distribution such as “product-led growth” (PLG) or long term defensibility such as a “data network effect”. What is the falsifiable hypothesis behind each? Not obvious.

Simple language makes it clear what the falsifiable hypothesis is and what evidence we expect to observe.¹⁷

For example, with respect to the hypothesis that a company can attract customers via PLG, instead of “sounds right”, we can restate the hypothesis as: “Security engineers working at large enterprises will go onto a website, install some software, and connect it to their production cloud infrastructure.” The response to anyone who has operated in this environment is clearly "no @#$%ing way” because anything touching production infrastructure is tightly controlled and must follow a stringent approval process in most organizations. In this case, the whole business model becomes invalid—the company needs to rethink its distribution strategy.

In another example, while the concept of a “network effect” is intellectually satisfying, we can restate the hypothesis as: “Customers will be happier with our product if we had more customers and less happy if we had fewer customers.” In some cases it might be true (e.g. communication tools or proprietary AI models that depend on lots of training data), but in many cases a product’s value is more or less the same regardless of the number of customers. In this latter case, there’s clearly no network effect. 

A false belief in a network effect probably won’t directly kill the company; however, investing in a data mote where it doesn’t matter to customers consumes valuable resources (i.e. it carries opportunity cost vs. investing in other forms of defensibility). Each hypothesis we add to our business model decreases our probability of success¹⁸, so startups need to figure out what actually matters to their business: the critical path to paradise, not just a path to paradise. 

Underwriting traction: Using evidence to understand residual risk

After articulating the hypotheses behind the market (a big one) and business model (the critical path to paradise) alongside the implicit hypothesis that the team can operate at increasing levels of scale, the quest of a startup is then to produce evidence confirming each hypothesis, retiring it as an area of risk.

At any time before we’ve fully retired all areas of risk, we can look at the strength of evidence we do have to determine residual risk—how much risk is still left in the business. For example, looking at a company’s product, customers, a sales team, marketing channels, and so forth, we can evaluate each hypothesis to reach one of several conclusions:

• Insufficient evidence: all hypotheses start like this at the outset, e.g. we haven’t tried to acquire many customers so we have an unknown customer acquisition cost (CAC).

• Disconfirming evidence: we believe some hypotheses are under no circumstances achievable because we’ve tried but can’t figure it out, e.g. we’ve experimented with many marketing channels and they’re all unprofitable, there are integration choke points that won’t let us pass, or our sales team is unable to succeed against competitors.¹⁹

• Confirming evidence: we’ve tried and proven it, e.g. we have customers that are paying while reliably renewing and expanding their contracts with positive net revenue retention.

Hypotheses with insufficient evidence are startup risk. There is some probability we won’t be able to validate them—we may not be able to assemble the flying machine before *SPLAT*. 

When faced with substantial disconfirming evidence, a startup is forced to do some form of pivot. This can be minor, such as moving up-market from small business customers to mid-market customers, or major such as focusing on a different product altogether.

Meanwhile, hypotheses with substantial confirming evidence reduce risk—that part of the flying machine looks like it’s working.

To estimate residual risk, we can evaluate the strength of evidence for each individual hypothesis in the business model alongside the hypotheses that the team can execute and that the market is large. Combining all of these together, we gain an understanding of the overall likelihood of getting to paradise. We’ll go into more detail below on using risk to underwrite valuations, but the most important point is: the less risk for a given company the higher the valuation.²⁰

How do founders choose which risks to focus on first?

If valuations are low when there’s a lot of residual risk and get higher as the risk is proven out, then of course founders should try to spend their time reducing risk. This requires capital to pay employees, buy equipment, and run marketing programs, amongst other expenses. 

At the outset, raising enough money to prove out all areas of risk is typically either impossible—no set of investors may be willing to write a check that large—or wildly dilutive to founder equity. Instead, most startups pursue stage-by-stage financing, raising a little money at a time to retire the largest areas of risk then raising more to retire additional areas of risk, and so on. Stage-by-stage financing is a series of “just-in-time” bets instead of a single massively risky bet.

There’s a lot of nuance in choosing how much evidence to collect and when to collect it:

• Risk is not binary—one or zero—but a sliding scale based on how much evidence exists. For example, small scale profitable customer acquisition via a particular channel provides some evidence that a go-to-market hypothesis is valid; however, there still remains risk that acquisition costs will increase as the channel becomes saturated.

• Each level and type of evidence has a different cost— For example, we can gain a little confidence on pricing by getting verbal agreement from prospective customers or we can find comparable companies to benchmark against.²¹ Stronger evidence to validate pricing requires us to actually build the product, an activity that may be 10,000x the cost!

• Some evidence has pre-requisites—Some evidence can be acquired at any time while other pieces of evidence must be serialized. For example, we can’t tell if customers love a product before it’s built and it’s hard to sell ads on a social network before anyone is using it.

• Multiple paths—Sometimes multiple approaches to producing evidence for a hypothesis can be worked on in parallel. For example, we may have several options for customer acquisition where only one needs to scale for the company to succeed.²²

Combining these effects together, we get some very difficult decisions to make. For example, verbal pricing validation is better than no validation, one customer buying is better than no customers, and five customers is better than one customer. Evidence of customers in multiple segments (e.g. customer sizes or industries) is often better than having all customers in a single segment because it provides evidence that the addressable market is larger; however if each segment has only a small number of customers as evidence of validity, that may actually result in higher risk than if all customers were in a single segment. 

One succinct way to describe startup strategy is: finding the cheapest level of risk reduction per marginal dollar. As you might imagine, I believe it’s important to carefully craft the right risk reduction plan for a business to maximize the amount risk we can prove out with a small amount of money.

Despite this complexity, I believe there’s a risk reduction roadmap that works well for startups who have considered buying processes—where customers think a lot before buying²³:

1. Verbal validation—founders have spoken to many customers and we have reasonable confidence that all elements of the business plan are plausible.

2. Product-market fit—we have clear evidence that customers love our product.

3. Repeatability—our sales and marketing engine can reliably get customers to buy with good unit economics (i.e. fast payback period of sales and marketing investment)

4. Scalability—we can get lots of customers to buy while maintaining good unit economics.

Framing the problem this way, it’s obvious why a company should focus on one stage before moving onto the next: it’s expensive to hire a large sales and marketing team if we can’t make the unit economics work on a small one (the quest for repeatability). It’s expensive to hire any sales and marketing team before we have a product (the quest for product-market fit) and it’s expensive to start building a product if we don’t know if people want it at a price we can profit from (the quest for validation).

It’s therefore important for founders to know what quest for evidence they’re on at any given time. Going on a future quest—such as prematurely trying to scale a sales team—before completing the a pre-requisite quest—such as building a product customers love—usually leaves behind a very expensive tombstone.²⁴  The model above starts with cheap evidence (verbal), moves on to moderately priced evidence (building a minimum viable product), before building an expensive go-to-market motion and undergoing a colossally expensive scaling process.

Another common deathtrap is to pursue multiple divergent paths simultaneously. For example, B2B companies must often decide whether to serve small, mid-sized, or large enterprise customers. Trying to sell to both mid-sized and enterprise at the same seems like gathering parallel evidence for a profitable sales motion; however, these customers often have different product requirements. This divergence leads to the very expensive strategy of either building two products or trying to sell a product to customers who will be dissatisfied with it. I believe it’s better to serialize customer types so you can delight one before moving onto another.

Pricing risk: from underwriting to valuations

Assuming it’s at least plausible that a startup can succeed, how should founders and investors fairly value the company? It depends on both the residual risk and potential rewards. A company that has proven beyond a reasonable doubt that it will be a large and sustainable business will command a higher valuation than one with more uncertainty. Similarly a company with the same amount of risk but in a large market should be valued more than one in a small market.

To add some quantitative rigor, I like to think of valuations as a probability distribution across outcomes. To do this we can create representative scenarios then assign probabilities, terminal values and additional dilution amounts to each.

While a multi-billion dollar business is always the goal, underwriting multiple scenarios allows us to model various off-ramps. A company with a great product but without distribution might be able to sell to an acquirer at an attractive price—an especially common outcome in cybersecurity, an industry that has very high sales and marketing costs given the complexity of the technology.

For example, consider a hypothetical company that is generating some revenue in a large market but success is far from guaranteed. An investor may underwrite the following scenarios (we’ll discuss how to come up with terminal values and probabilities in detail below):

To calculate the value of each scenario, we can multiply the terminal value by (1 - dilution) to determine what the effective return for investors equity might be. For example, in the base case, $500M * (1-40%) = $300M.

From there, we want to weight each return by the probability of the scenario occurring to determine how much each scenario contributes to the eventual outcome. In the base case above, $300M * 20% = $60M.

Summing each scenario contribution, we can produce an “expected value” or the average return equity holders can expect. Of course each specific result will be different from this average—craters will be lower and bull case outcomes will be much higher.

If we believe that the distribution above accurately represents reality, the expected value of the company is $152M. It would be unprofitable for investors to invest above the expected value of a company: by definition, that would be expecting to profit less than $0 on average which does not compensate them for the potential failure, effort, and cost of capital to invest in the business—a negative net present value (NPV).

Instead, investors will aim for a return target consistent with their venture model. Because there’s a lot more uncertainty the earlier in a company we are, investors should apply a larger margin of safety in the form of a higher return target. For example, if one were to focus on bold and very early stage bets, I think 10x is an appropriate target. In that circumstance, investing in the company at a $15M valuation or less would be attractive relative to the 10x return target.

Because ~90% of the expected outcome is contained in the base and bull scenarios, I believe many experienced investors simplify their thinking and focus only on the likelihood, dilution and terminal value of those.

In the table above, all of the complexity of underwriting we discuss in prior sections is reduced to a small set of numbers. While such simplification may seem like a blunt instrument—and it is—I believe it strikes a happy balance between under-modeling and over-modeling. Because of the level of uncertainty we’re dealing with, adding more bells and whistles to the model won’t make it more effective.

Traps in building outcome distributions

Though crafting scenarios that best reflect reality is a subjective process where SSS investors can disagree (we’ll cover how that happens below), let’s start with common traps to avoid:

• Too much credit for forward execution underestimates risk when investors believe a risk is minimal when it’s actually much harder to solve. Avoiding this one comes from the experience of knowing which aspects of building a particular company in a particular market are hard, and which are easy. 

• Benchmarking to high acquisition prices may be tempting particularly in the wake of high-dollar outcomes in a particular market; however I’ve found acquirers are fickle and there’s wisdom to the phrase “companies are bought, not sold” implying that a startup has very little control over such outcomes. It’s much more reliable to build a large and sustainable business and perhaps the company will get an attractive offer along the way.

• Anticipating large terminal values in bull markets can justify just about any entry price; however, at some point terminal values will be based on fundamentals and timing the market to exit only when things are frothy is very hard.

How do we sanity check terminal values? 

Craters are self explanatory and small acquisitions right around the company’s liquidation preference are easy to account for. For success cases, though, we have to make some big assumptions. If we believe that a company’s valuation will eventually be based on its cash flows, the terminal value is really just the output of a valuation model whose major input is cash flow.

While there are valuation frameworks of varying complexity, for our purposes we need something approximately reasonable, not precisely accurate. How much will a company be worth at exit 10 years from now? Nobody knows, but if it grows from $0 to $200M in revenue, our stock should be worth a heck of a lot more than we paid for it.

Ideally, we choose a slightly conservative terminal value so we can be surprised to the upside if growth or market sentiment is better than we think while avoiding being surprised to the downside if the opposite occurs. Here are a couple of approaches:

Equity Risk Premium: We can take the current interest rate (4.11%, 10Y treasury as of 1/22/24) and add an equity risk premium or the amount the market pays us for accepting the risks of stocks vs. US government debt. I believe a reasonably safe long-term average is 6%, resulting in a total “yield” of ~10% as of the time of writing.²⁵ To normalize growth rates, one approach is to use 3 year forward figures.²⁶ In this case, a company doing ~$198M in gross profit today that is growing at 15% annually would be doing ~$300M gross profit in 3 years. A 10% gross profit yield (10x gross profit multiple) produces a ~$3B valuation under this model. Because many startups aim achieve this level of gross profit and growth when building their initial business plans, I believe $3B is an acceptable bull scenario terminal value. 

If we want to estimate the effect of lower or higher interest rates, we can easily calculate them. At 6% interest rates (8.3x multiple), that company above would be valued at ~$2.5B and at 2% rates (12.5x multiple) ~$3.75B. Note that these valuations are different, but they’re not that different. Under any rate environment, it takes a LOT of gross profit to substantiate some of the lofty valuations that were being underwritten in the last few years. 

Gross Profit Multiple: We can also use public market comps. Similarly let’s use the ratio of valuation (EV) to gross profit to normalize gross margin profiles across companies. To justify a terminal value of $3B, assuming the same 15% growth rate, based on the chart below we expect a ~15x multiple or ~$200M in gross profit—it’s wonderful when two valuation models align!²⁷

Of course, when underwriting terminal values, there are a number of traps to be wary of:

• Large operating expenses destroy value—in these valuation models, we assume that gross profits are “allocatable cash flow”—cash that the company can invest in growth. However, if a company has large costs that are required to run the business which are not captured as part of their gross margin or its growth investments are inefficient, they will likely underachieve the benchmark multiples above. For example, if a company’s sales process is extremely complex relative to deal sizes, allocating cash to the sales team will result in little revenue growth. Similar value destruction can happen with excessive stock based compensation (SBC) or if an engineering team is not very good and needs to spend a lot of money to accomplish even minor tasks.²⁸

• Public market comps change over time: Because we’re aiming to make an investment that will pay off years in the future, we cannot assume today’s valuation environment will persist. If using comps from a frothy environment, you can get tricked into wild terminal values. Using long-term normal valuation multiples while adding in a margin of safety, you may seem overly conservative, but you’ll avoid being rudely surprised when valuations revert to the mean.

• Eventual market size really matters—it may be tempting to invest in a company whose eventual market size is only in the hundreds of millions if they are in a non-competitive market—if we can underwrite a great outcome with only $200-300M in gross profit, why do we need a larger market size? That’s because “terminal” is a misleading term. An IPO is just another funding round. A company that executes into a big market can substantiate high growth rates for some time to come; however, if a company saturates its market, growth will atrophy and its valuation will suffer.

When the stars align, we can build a legendary company that persists for decades—a company that can exist in the pantheon of “20-year compounders”. Businesses that can maintain significant growth for decades eventually make $200-300M gross profit look tiny and can far exceed our $3B terminal value. Consider the long-term success of Google, Palo Alto Networks, Meta, and other durable winners. In Amazon’s case, over 99.9% of valuation growth occurred after their IPO.

Such durability is the substance legendary founders and venture funds are made of.

Risk is in the eye of the beholder: How do we assign probabilities to each scenario?

I do not believe there is a single formula to convert specific risks a company faces into precise probabilities at the early stage; however, I believe that the more clearly we can articulate risks, the more accurately we can estimate the company’s likelihood of achieving each scenario.

Returning to the example company described above, we can use our simple valuation model²⁹ to transform each scenario into a more precise question: what is the probability of the company getting to a particular level of gross profit?³⁰

Just as insurance underwriting starts with an actuarial model, I like to do the same with startup underwriting to make sure we’re anchored in reality. Specifically, I like to use historical base rates of dilution and scenario probability for companies at a given stage³¹ as a starting point for underwriting models.

While fires are largely unpredictable, we can expect rates for fire insurance to be much higher in homes with wood shingled roofs adjacent to dry brush than those with tile roofs in dense urban environments. Similarly, with startup underwriting, we can adjust our base rates to fit a given company’s particular risks. For example, for a seed stage company that is reliably signing $50k gross profit annual contracts with customers today, we may ask a more precise question about the bull case scenario: what’s the likelihood that they can grow to $250k annual contracts and sign 800 of them to get to $200M in gross profit with a clear path towards $1B after that? 

Each investor who approaches this question will bring their own principles and beliefs as they look at the team, market, business model, and traction. They may seek to understand customer psychology, spend a lot of time with the founders, or have their own thesis on the evolution of the market. I believe that there are many ways to do venture right—given the complexity of underwriting startups there are multiple ideal points, not a single optimal approach. There is no magic formula.

The approach that can let you sleep at night is to approach underwriting with a lot of intellectual humility and assume wide margins of safety: reasonable terminal values, low bull case probabilities, and high return targets.

Even if they are all looking at the same team, market, business model and traction, investors can substantially diverge in their underwriting. Here are a few places I’ve seen that happen:

• Re-segmentation (e.g. by size, vertical, requirement)—startups often attempt to redefine a pre-existing market either by pulling a subset of customers away from their current solutions or by converting non-consumption. Sometimes this is the result of a heterodox insight not widely appreciated by the rest of the market, for example, that a subset of customers are so fed up with how they do things that they’re willing to adopt an entirely different approach.

• Slow vs. never—Some industries adopt change slowly or unpredictably, leaving behind a litany of dead startups that justify a belief that the industry will never change. There’s an oft-cited phrase, “being early is indistinguishable from being wrong” that captures this perfectly. For example, enterprise cloud adoption has been well underway; however, hospital cloud adoption is still in its infancy. If an investor assumes “never” they will underwrite a high risk for any company selling cloud-based software to hospitals; however, if an investor believes hospitals were just slow but now deeply care about reducing IT spend post-COVID, then they may assign a much lower risk.

• Market trajectory—Not everyone shares the same beliefs about rapid changes in markets. For example, one of the most spirited debates right now is “Do you believe that generative AI is a megatrend or is it overhyped?” Based on that answer, you’ll underwrite wildly different demand for AI chips, infrastructure software, and the like.

• Founder background—as we discussed in the team section above, when founders aren’t from central casting, investors tend to form divergent opinions based on their own idiosyncratic opinions of what a “great” team looks like.

• Esoteric markets—when investors don’t understand the dynamics of a market, they assign a high risk to it by default. Many markets are large but, for one reason or another, investors have not had much exposure. I believe there are many great opportunities in markets that a). have a lot of cash sloshing around, b). have not seen much venture investment, and c). are facing some sort of technological regime change.

When investors underwrite different levels of risk, they can justify different valuations.

Because each investor uses a different qualitative model and underwrites to a different return target, there’s a wide variation in valuations investors are willing to assign to a given company at a given time. There are so many subtleties in how people underwrite risk that I believe we’ll be in an “inefficient” market for a long time to come.

Consider the company from above: if another investor underwrote less risk (i.e. higher probabilities of achieving success as shown in the table below) they might be willing to invest at a $40M valuation assuming the same 10x return target, given the same terminal values and dilution estimates.

After all of this, you may be tempted to believe that venture underwriting is bullshit when multiple SSS investors can look at the same company and come up with wildly different valuations. If we were playing dice, that would be true; however, given some investors ability to repeatedly invest in outlier companies at valuations that produce healthy returns, I believe there is a reasonable case to be made that tremendous skill is involved.

Inefficient + illegible = exciting. 

As we discussed above, startup underwriting is often illegible³² (i.e. evidence is not clearly available to all participants), inefficient (i.e. valuations do not consistently incorporate available evidence) or both. This means that different abilities of gathering or incorporating evidence will lead to different perceptions of risk. As we’ve just shown, differences in perception of risk can substantially impact what you think is a reasonable valuation.

“There are two requirements for success on Wall Street: the first is to think correctly and the second is to think independently.”
- Benjamin Graham

I believe that the best startup outcomes come from a non-consensus, and right, understanding of risk. If an investor overestimates risk, they will either opt-out entirely or underwrite a low valuation. Consider how many dismissed SpaceX in its early days as nothing more than a flight of fancy. In these circumstances, someone who correctly understands risk can invest at a “market” valuation, but will be able to underwrite far higher risk-adjusted returns. When your understanding of risk is correct, finding opportunities that “are good but look bad” is great venture capital investing. 

As a founder, similar things occur: if prospective competitors overestimate risk in your market, they may avoid competing altogether (good!). It’s then your job to convince investors of a correct understanding of risk so you can avoid having your valuation be penalized by false perceptions. Great startup pitches clearly articulate the case for the company being a relatively low-risk proposition in the form of a compelling, psychologically convincing narrative.³³

What happens if other investors underestimate risk? They will underwrite higher valuations than you think are reasonable (danger!). Because it’s not feasible to short early stage startups³⁴, the only safe response is to step back while others proceed. Sometimes you may be wrong because you were the one overestimating risk, but that’s OK—call it a learning experience. You’re only penalized by the investments you do make, not the ones you don’t. In fact, Bessemer Venture Partners, a well regarded venture firm, jokingly memorializes their “anti-portfolio”—the now-successful companies where they passed that includes Apple, Google, AirBnB, and Zoom. More evidence supporting the claim that venture capital is inefficient and illegible: even very successful investors have embarrassing anti-portfolios.

Realistically speaking, because of the competitive dynamics of venture capital, investors are partially price takers—they are one of many participants in a sort of auction where they can choose to participate at a given price but not set the price itself. I say “partially” because, as founders, we want the best valuation for our company, but we also know that not all investors are created equal. Some are wonderful to work with and can meaningfully help a company’s chance of success while some are actively destructive. Personally, I’d rather compromise a little on valuation to have a great investor than just auction to the highest bidder. 

Because of these dynamics, I believe that the best investors only invest when:

• they have some sort of edge in underwriting risk, 

• where they can help meaningfully improve the likelihood of success, 

• or where founders are willing to eschew taking the highest valuation bid to work with them.

Easier said than done: in the heat of a hot deal or a frothy market, it’s tempting to buy into hype.

Emotions are untrustworthy investing partners

Underwriting is not a cold, rational activity—given the complexity of factors, it inherently takes the properties of human psychology. For example, if you like a founder, chances are that their customers and future employees will also. 

Emotions can also be decidedly unhelpful: little does more to impact human psychology than hot markets—concentrated startup founding and investing activity in a particular category. 

Hot markets create a double-headwind for returns: high entry prices and lots of competition. Because hot markets tend to spawn a lot of competitive companies and fund them aggressively, we should adjust our valuation model to decrease expected values relative to normal markets (competition drives up sales and marketing costs, customers have more leverage in negotiating down gross margin, and strategic acquirers have more options to pick from). At the same time, frenzied venture investors drive up entry valuations. Both factors compress the risk-adjusted return we can underwrite (not good!).

“If you follow the mainstream, usually the margins are very small.”
- Francis Greenberger

Founders and venture capitalists aren’t dumb, so why does herd behavior in hot markets persist? Follow the incentives.

Hot markets have momentum. If one investor leads a Seed round today, there’s likely to be another investor leading a Series A at a higher valuation not too long after. On paper, this is good—you got a markup on your investment in a very short period of time. The gods of IRR (internal rate of return) and TVPI (total value to paid in ratio) are appeased. As an early career VC, you get promoted at your current firm or get a better job at another firm. Life is good.

Similarly for founders on the sidelines, seeing others get lofty valuations with little traction is the siren-song for starting a new company.

Investors and founders don’t make money on unrealized gains; they make money on outcomes.

“No matter how good a year you’re having, someone out there made more money than you specifically because they made a lucky bet with an irresponsible amount of leverage.
But they won’t do well forever.”
- Byrne Hobart

Of course, sometimes founding or investing in hot sectors pays off: a company succeeds despite competition by gaining an unassailable advantage or they get lucky by exiting at the right time to a strategic acquirer willing to pay up. Much of the time, however, competition makes it hard to succeed or the market turns out to be not quite so large as everyone hoped. Companies who raise a lot of money but are unable to show cash flows typically become walking wounded, unable to reach the orbit of the public markets or to command high acquisition prices. These companies either go out of business or sell at not-so-attractive valuations.

To reliably achieve great outcomes—liquid stock for founders, employees, and investors—what we really care about is for our investment to have large, growing, and defensible cash flows. Hotness is not causal to cash flow.³⁵

When trying to invest in peculiar companies that have bold, but under-appreciated visions—“is good, looks bad”—we must be prepared for a tepid funding environment until greatness is obvious³⁶ or perhaps the company may not need to fundraise because it can grow extremely efficiently. In either case, investors must be prepared to not see a markup for years. Unfortunately, this is suicide for an early career VC hoping for fast markups.³⁷

If you have the long-term orientation and emotional stamina for non-consensus startups, sometimes leaning into obscurity is a good strategy because it takes time to build an unassailable advantage. Staying off the radar avoids attracting the ire of large incumbents or spawning copycat competition. Such companies may avoid announcing their funding at all, keeping a low profile amongst investors while focusing on their customers.³⁸

How does one reliably find non-consensus companies? By definition, they can’t be the ones everyone else is talking about. One pattern is to find sectors that seem really boring, but where a lot of money sloshes around. Attractive sectors tend to be more picked over but boring is beautiful! You can’t focus on one sector for too long, however, because once a few successful companies emerge, it may become hot. Consider the relative obscurity of software for synthetic biology, healthcare, and logistics a decade ago compared with the aggressive venture investments in the last market cycle.

A second, and perhaps more durable non-consensus strategy is to focus on companies that do things that seem really really hard.

Doing hard things: Difficult is defensible.

It’s very tempting to get excited about opportunities where a small team can execute and generate so much high-margin revenue that growth fund analysts begin to salivate. Who wouldn’t want that? However, that 90% gross margin software business that’s not distribution-hard, technology-hard, product-hard, or some other form of “-hard” will likely face contribution margin erosion after accounting for sales and marketing costs when competitors see such a juicy business and aim to duplicate it.³⁹ Consider Hopin, the high flying COVID-era virtual events company that was once valued at $7.8B but sold for $15M after customers began transitioning back to real-world events and intense competition from Zoom and Microsoft.

Hard things are rare. Rare things accrue long-term value. Here’s a tautology: if something is hard, it’s not easy. Competitors can duplicate easy things but they can’t easily duplicate hard things. A simple rule, but easy to forget.

Some types of hard things need tremendous amounts of capital. I’m glad, as a species, we pursue these sort of hard things but they’re difficult for early-stage venture underwriting. 

I believe the best type of “hard things” for early stage investors are those sorts of problems that exceptionally talented and dedicated teams can solve. A consumer product that is hard to duplicate because it has an exceptional brand or design. An enterprise infrastructure or hardware product that requires highly specialized technical expertise. A marketplace, communications tool, or social network that has a strong network effect.

Consider one of the greatest “hard thing” entrepreneurs currently operating:

“I was at a lunch with Munger in 2009 where he told the whole table all the ways Tesla would fail. Made me quite sad, but I told him I agreed with all those reasons & that we would probably die, but it was worth trying anyway.”
- Elon Musk

This is not just a flippant comment. Elon’s persistence in the face of Charlie’s correct illustration of risk captures underwriting vs. analysis brilliantly. Underwriting tells us that it doesn’t matter if something might fail so long as the outcome compensates us for the risk. Few are as willing as Elon to invest their life and fortunes behind highly risky futures with massive potential outcomes. 

Revisiting our underwriting model, distributions across outcomes work for both mundane and wild bets—you just have to underwrite appropriate scenarios. For example, a moonshot bull case may assign a low probability to a much higher terminal value. If it’s capital efficient, we may underwrite a similar amount of additional dilution; however, if it’s going to take a lot of money to get there, we might underwrite much higher dilution. Given the large pot of gold at the end of the rainbow, even modeling a 95% failure rate with 70% dilution in the success case produces a $9B expectation value! 

Of course, underwriting these sorts of outcomes for every team and company would be reckless; however, Elon is one of the most dogged entrepreneurs in history. Betting on him has paid off early backers beyond this humble model’s expectations.

Four types of investors: Dreamers, Winners, Cynics, and Shorts

One way to segment investors is by two dimensions: their accuracy of understanding risk and  their level of positive outlook.

In startups, it’s not feasible to bet on failure—so cynics (blinded pessimists) and shorts (clear-eyed pessimists) sit on the sidelines, marinating in their criticisms. Above, Charlie is acting as a clear-eyed pessimist: he agrees on the specific challenges facing the company but underwrites higher risk with no participation in the market. He won’t lose money if Tesla fails and he won’t make money if it succeeds.

As founders and investors accumulate experience, we live through countless failures. Even though we understand some rate of failure is to be expected, each smoldering crater really hurts. Our emotional tendencies to loss-aversion can overtake rational underwriting—it’s easy to project painful past failures onto new startups even when it’s unreasonable to do so. Clear-eyed optimists can, over time, become late-career blinded pessimists.

Founding and investing in startups is a “long-only” game, which is to say that it’s inherently optimistic. Optimism, like pessimism, can be accurate or delusional. 

For investors, I believe being a clear-eyed optimist is the only way to reliably win over the long term: accurately acknowledging risks but stepping onto the battlefield when the situation is favorable. Investors who are dreamers (blinded optimists) tend to run after hot markets and pay high prices relative to residual risk. Dreamer investors might win in the short term, but over the long-term, they’ll likely get blown up.

For founders, sometimes being a blinded optimist is helpful. Startups are emotionally crushing and it’s unclear how many would pursue the founder’s journey if they knew the road ahead. My wife, who is currently running a Series A health-tech startup, loves a quote that resonates with every entrepreneur: “We do these things not because the are easy, but because we thought they were going to be easy.” Many seemingly-impossible businesses are started by people who claim later, once they’ve become a little more clear-eyed, that they never would have founded the company in the first place had they known how hard it would be.⁴⁰ Consider NVIDIA’s present success relative to how much pain it took to get there:

“I wouldn’t do it… building a company and building NVIDIA turned to be about a million times harder than I expected it to be… If we’d realized the pain and suffering and just how vulnerable you’re going to feel… nobody in their right mind would do it.”
- Jensen Huang

For founders, blinded optimism works because hope can spring eternally. Bank accounts do not, so it’s a less helpful disposition for investors.

There’s a YC phrase I love: “Companies fail for only two reasons: founders give up or they run out of money.” Even if you are a dreamer, so long as you can stay cheap and make enough money to be “ramen profitable” or “default alive” you can live to fight another day.⁴¹

But what about founders with clear-eyed optimism from the outset? Many experienced entrepreneurs start their second, third or fourth companies knowing exactly how painful the journey will be: you can almost hear the sorrow in Elon’s voice yet still he pushed ahead. 

Similarly, why don’t founders who start blinded but eventually become fully clear-eyed give up? Why didn’t NVIDA fail multiple times over given how hard it was? In Jensen’s words: “My will to survive exceeds anybody else’s will to kill me.”

For a strikingly large number of people, the will to progress triumphs over any personal discomfort. I’m grateful for all of those that knowingly start or continue the founder’s journey.

“Here's to the crazy ones. The misfits. The rebels. The troublemakers. The round pegs in the square holes. The ones who see things differently. They're not fond of rules. And they have no respect for the status quo. You can quote them, disagree with them, glorify or vilify them. About the only thing you can't do is ignore them. Because they change things. They push the human race forward. And while some may see them as the crazy ones, we see genius. Because the people who are crazy enough to think they can change the world, are the ones who do.”
- Steve Jobs

1. Roughly $1-2M gross profit run rate, 100-200% annualized growth, and happy customers.

2. If you’re interested, Mandelbrot has an excellent attack on the the “normal” Gaussian distribution as being inadequate to explain real-world market pricing behavior in The Misbehavior of Markets.

3. I’ve found that investors bringing habits from elsewhere to startup investing commonly have excess focus on analyzing the present and downside mitigation. Similarly startup investors who invest in other asset classes often bring their upside and future focus where it may not be optimal.

4. As with any compromise, the academically-minded will want more rigor and the practically-minded less.

5. The transition to being an analyzable company often happens around Series B but sometimes can occur much later in sufficiently complex, difficult, or capital intensive businesses.

6. In the spirit of Charlie Munger—may he rest in peace—“invert, always invert.”

7. I believe the other significant aspects to persistent outsized venture returns include sourcing and ability to help, two topics for another letter.

8. “Correlation” models aim to follow strong investors without doing their own underwriting; however, at the end of the day, someone is underwriting team, market, model, and traction.

9. The notable asterisk here is a tendency for SSS people to “overfit” their experience and miss new opportunities that come up because of changing underlying assumptions. I believe that a healthy dose of intellectual humility and willingness to repeatedly question assumptions helps avoid this trap.

10. Read Paul Graham’s essays for a deep dive on the subject of how YC selects founders to back.

11. Particularly if such a bet was made in your Roth IRA, as Peter Thiel famously did.

12. I find myself frequently being tempted to fall into this trap.

13. See prior letters for a very particular process of how to do this in sales-driven markets.

14. It’s quite painful to spend years on something provably inconsistent with reality at the outset.

15. Unfortunately, unfalsifiable claims are everywhere in business and life.

16.  This is a cognitive bias is referred to as “the illusion of explanatory depth”.

17. There are many writers who use the “simple language” technique to great (often hilarious) effect: Randal Munroe’s Thing Explainer, Tim Urban’s Wait But Why, and Nathan Pyle’s Strange Planet, to name a few.

18. This is a key finding from probability theory: If there are two events—A and B—the probability of one event occurring—p(A)—must each be greater than the probability of both events occurring—p(A & B).

19. “We’ve tried and failed” is often indistinguishable from “we’re still trying” leading to situations where accumulating even a tiny bit of disconfirming evidence (e.g. around monetization) decreases valuation vs. having no evidence. 

20. Importantly, you can’t use residual risk to compare valuations between companies if they have different long-term prospects. Consider OpenAI in its early years as a commercial entity with incredible risk and incredible potential rewards relative to a company with far less risk, but more modest potential rewards.

21. If you’ve read prior letters, you may recall that verbal validation is my favorite cheap risk reduction tool.

22. Velocity is a key predictor of startup success: when there are multiple options to solve a given problem, acting and iterating quickly maximizes the chance of finding at least one that works before *SPLAT*.

23. Examples of considered buying processes include: mid-market and enterprise where multiple individuals are involved and high-cost consumer products.

24. In fast-moving markets, it’s tempting even for experienced founders to skip ahead, particularly when the company has raised a lot of money. I like to call these situations “bonfires of cash”.

25. For growth technology companies that typically re-invest their cash (e.g. in engineering, sales and marketing, acquisitions) in the business rather than pay taxes or distribute dividends, I believe gross profit is a good proxy for “allocatable cash flow”, so we’ll use that as our yield target.

26. I’m purposefully including a lot of “rule of thumb” calculations given our goal of “roughly reasonable”, not “precisely accurate”. The dominant uncertainty in the model is the risk of the startup succeeding at all.

27. Not shown, projecting 3y forward gross profit multiples at 10% yield across growth rates is extremely close to this trend line.

28. To look at extremes, consider the healthcare.gov fiasco estimated to have cost over $1.7B vs. WhatsApp having only 50 engineers before scaling to 900 million users and being acquired for $18B.

29. 15x gross profit multiple, continuing to assume a modest 15% growth rate to give us a margin of safety.

30. Sometimes acquirers value the team, technology, product, or impact on strategy above what gross profit multiples would predict. I prefer not to model these potential welcome surprises.

31. For example, at the seed stage: approximately 1 in 20 companies will end up in their bull case and approximately 1 in 3 will fail completely.

32. There’s a whole additional dimension of illegibility: not every investor sees every investment opportunity. Sourcing is a big topic for another letter.

33. I’ve found that the best startup pitches, much like any great story, follow a narrative arc that would be just as engaging around the campfire as it is on a zoom.

34. It’s also much more fulfilling to be on the side of building the future than betting against it.

35. Hotness can be correlated to cash flow if hype is cheap marketing for an otherwise sound business.

36. The upshot of this is that prospective competitors face the same headwinds.

37. The best answer I’ve seen is incentive alignment: making sure investors expect a higher return from eventual carried interest than they do from getting a promotion or raising a bigger fund.

38. Cue the Glomar response: I can neither confirm nor deny the existence or non-existence of this startup.

39. There’s a momentum trade in this circumstance for early stage investors and founders to sell secondary at peak hype, but it’s difficult to get the timing right, not to mention destructive to one’s reputation.

40. Though that’s easy to say in retrospect when you’re rich, having made it through the startup gauntlet.

41. Managing burnout and mental health are some of the hardest problems for founders to deal with.