The Math Of Failure
On Margin of Safety
Moneyball is a fortnightly newsletter from Koble exploring the limitations of human decision-making and their implications for startup investing.
We've spent two years developing our groundbreaking algorithms, which discover early-stage startups that outperform the market and predict their probability of being successful.
🧠 Mental model #4 – Margin Of Safety – The Math Of Failure
📖 Investor reading – Venture capital’s silent crash – EQT Ventures closes €1bn fund for early-stage startups – Welcome to the Third Wave of Investing: Machine Intelligence
💬 Some tweets – You don’t need VC funding to launch your startup – VC has ceased to be the funder of the future – Consistently amazed
At Koble we’re building a deep-learning model that can predict the success of early stage startups. Here’s what’s happening…
We’ve been fishing. Well not quite…
Let’s talk about F1, a classic machine learning metric. F1 is a combination of a Precision score and a Recall score. To explain we’re going to use a great analogy from Adam Shafi.
“Explain Precision and Recall like fishing with a net. You use a wide net, and catch 80 of 100 total fish in a lake. That’s 80% recall. But you also get 80 rocks in your net. That means 50% precision, half of the net’s contents is junk.
You could use a smaller net and target one pocket of the lake where there are lots of fish and no rocks, but you might only get 20 of the fish in order to get 0 rocks. That is 20% recall and 100% precision.”
The team have been working hard to contextualise Koble’s F1 metric in relation to the F1 metric of every large Pre-Seed and Seed fund. We’ll soon make some of these public, along with a comparison to our model.
The Math Of Failure
“To a person with a hammer, the world looks like a nail.”
Charlie Munger’s famous quip neatly encapsulates the challenge of bias – the lens through which we see the world and make decisions.
The lens through which we consciously and subconsciously process information is uniquely ours, creating cognitive blind spots and formidable problems when it comes to investing.
Munger tells the story of a gallbladder surgeon who – due to economic and reputational incentives and narrow domain expertise working solely on gallbladders – came to the conclusion that gallbladder surgery was necessary for everybody:
“He thought that the gallbladder was the source of all medical evil, and if you really love your patients, you couldn’t get that organ out rapidly enough.”
This might seem comical, but it’s a widespread phenomenon. Indeed, evidence from the world of dentistry suggests that far too much surgery is done on people and that the risk/reward profile is often better suited to non-intervention. As Nassim Taleb has observed:
“The principle of intervention, like that of healers, is first do no harm.”
It’s not just dentists; it’s politicians, central bankers, celebrities, activists, parents… A great number of people are walking around with the unshakeable belief that their answer to the complexities of life is everyone’s answer. They overestimate their circle of competence and project their cognitive biases onto other people.
For incontrovertible evidence of this effect, see Twitter.
How to mitigate bias?
We can seek to mindfully recognise the limitations of our domain expertise, but the whole point is that people are bad at that math. So, we should apply a margin of safety to our calculations.
Margin of safety is an engineering concept that’s been co-opted by value investors – and it should be embraced by their estranged cousins, startup investors, too.
Henry Petroski’s brilliant book, “To Engineer is Human” explores the concept in some depth. He explains that a quantitative margin of safety is in fact a safety factor:
“Calculated by dividing the load required to cause failure by the maximum load expected to act on a structure. Thus if a rope with a capacity of 6,000 pounds is used in a hoist to lift no more than 1,000 pounds at a time, then the factor of safety is 6,000 / 1,000 = 6.”
The rope might be weaker than described, a heavier load might be lifted, or the same weight lifted in a manner that increases the force on the rope. The safety factor integrates these possibilities and enables the engineer to derisk the construction process.
Implications for investors
Investors also build things (portfolios as opposed to bridges). And we’re trying to derisk them. This is very hard. Most VCs believe deeply that they are unicorn hunters when in reality, they’re being paid by LPs to build a de-risked portfolio that generates 3x returns.
As Donald Rumsfeld has pointed out, we’re presented with many different types of risk:
“There are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns—the ones we don't know we don't know.
By incorporating a margin of safety we can more effectively discount the unknown risks of investing in startups – in all their forms – building redundancy into the investment process.
Petroski points out that there is no universal factor of safety that works in every scenario. A bridge is different to a jet plane, a crypto exchange (FTX anyone?!), a government bond, an early-stage startup. The subtitle of his book, “The Role of Failure in Successful Design”, is rich terrain for investors.
Seen in this light, the challenge (and the fun) of investing becomes understanding the unique contexts in which we deploy capital – the complex interplay between micro and macro forces that underpins company performance in public and private capital markets – and mastering the simple (but not easy) math of failure.
Work with Koble
At Koble, we've spent two years developing our groundbreaking algorithms, which discover early-stage startups that outperform the market and predict their probability of being successful.
We’re working with forward-thinking angels, VCs, family offices, and hedge funds to re-engineer startup investing with AI. If that resonates, get in touch.
💥 Venture capital’s silent crash: when the tech boom met reality – Investors of all stripes have crashed the clubby world of VC, drawn by the potential of tech start-ups. But there are signs the party is over.
💰 EQT Ventures closes €1bn fund for early-stage startups in Europe and North America – It (sometimes) leverages its internal AI tool, Motherbrain, to find companies.
🤖 Welcome to the Third Wave of Investing: Machine Intelligence – The history of investing has often been a story of incremental improvements. Things are about to get more interesting quickly.
“If you're not having fun, you're doing something wrong.”
– Groucho Marx
Regards from your [ruthlessly ambitious] startup investing AI,
Koble is re-engineering startup investing with AI, applying quantitative strategies that have disrupted public markets to early-stage startup investing.