What framework should investors use to assess and price risk in a startup investment?

Frank Rotman · Aug 26, 2021

The best businesses to back are ones where risk can be systematically de-risked — broken into smaller provable theses that can be tested cheaply in market

You can turn over cards, let the market tell you where you're right or wrong, and then put money behind what's working

Scope: not every venture-backable business fits this; it's QED's sweet spot

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Will Quist · Sep 12, 2022

The job of the venture asset class, including new venture, is to bet on theories: enter where you have more conviction in a highly credible theory on at least one lever than others do, and finance the company to run the experiment

Converting hypotheses into first-party data produces the biggest change in the discount rate applied to enterprise value

17:16 20VC: Why 95% of Venture Capital is Not Really "Venture Capital" | The Five Core Levers Needed To Assess Risk and Price a Startup | The Future of Venture; Who Wins, Who Loses, What Happens to the Crossover Funds with Will Quist, Partner @ Slow Ventures

Nigel Morris · May 24, 2021

Startup outcomes are capriciously hard to predict because each individual risk is solvable on its own but they must all line up as conditional probabilities — the 'point nine to the power six' problem

Team building, TAM, compliance, regulation, funding, getting the A round done are each individually solvable, but the compounded conditional probability of all of them going right is low

Scope: applies particularly at the early stage

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Will Quist · Sep 12, 2022

Any single one of the five levers can produce an answer that kills the whole investment, which is why venture is simple but complex and has no pithy one-word answer

18:16 20VC: Why 95% of Venture Capital is Not Really "Venture Capital" | The Five Core Levers Needed To Assess Risk and Price a Startup | The Future of Venture; Who Wins, Who Loses, What Happens to the Crossover Funds with Will Quist, Partner @ Slow Ventures

Will Quist · Sep 12, 2022

Venture is simple but hard: the mental formula for pricing risk is simple, but weighing the contextual levers of any given experiment is the hard part

You must trade off factors like a strong value prop against a small market against a founder with no background in the space, and the weighting is almost always contextual, requiring art, science and discipline

8:56 Pricing formula is simple but weighing contextual levers is the hard part

Will Quist · Sep 12, 2022

Pricing any company comes down to five levers: whether the arc of history bends the company's way, the product's absolute and relative value proposition, the market, defensibility, and the business model's equity efficiency

These are the questions at the root of what value investors, buyout firms and top VCs implicitly or explicitly ask when deciding how to price a deal

14:52 Price via five core levers history product value market defensibility and business model efficiency

Will Quist · Sep 12, 2022 · hedged

A root cause of many startup failures is a product whose value proposition is merely qualitative rather than easily identifiable and quantifiably worth more than its price, which makes it hard to sell — though black swans and macro/political shocks can also cause failure

The point of investing is backing someone who manufactures value in excess of what it costs to produce; when that gap is quantifiable and causal, the product sells itself with no sales and marketing cost

18:32 Qualitative non quantifiable value propositions explain many startup failures though black swans also cause failure

Harry Stebbings · Sep 12, 2022

Product value proposition is not all that matters — macro and political factors like a LatAm currency devaluation or a change in political regime can make a business completely unsustainable

18:48 Macro and political shocks not just product value proposition can doom a business

Will Quist · Sep 12, 2022

Risk should be assessed by counting how many of the five levers rest on theory rather than first- or third-party data; beyond a certain number of stacked theoreticals the deal cannot be priced and is NPV negative

Having one correct novel theory in your life is already a low-odds proposition, and the odds of being right on two vectors are exponentially rather than linearly lower

20:22 Count theoretical vs data backed levers too many stacked theoreticals makes a deal unpriceable