Investing in a world of limitless data
The Streetlight Effect
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 success.
🧠 Mental Model #17 – The Streetlight Effect – Investing in a world of limitless data
📖 Investor reading – Computer-driven trading firms fret over risks AI poses to their profits – Ex-Ardian execs and former Coller data scientists launch AI secondaries firm – What the Pentagon thinks about AI
💬 Some tweets – Generative AI investing: a process – “Startups are bought not sold” is a misbelief – How the AI investment boom was catalyzed
Investing in a world of limitless data
Here’s a not-so-familiar (yet familiar) story:
A policeman sees a drunk man searching for something under a streetlight and asks what he has lost.
He says he lost his keys, and they both look under the streetlight.
After a few minutes, the policeman asks the drunk where he last saw the keys, and he replies that he lost them in the park.
The policeman asks why he is searching under the lampost. The drunk replies, “because the light is better here”.
This parable, widely attributed to a 13th Century Turkish philosopher by the name of Mulla Nasreddin, encapsulates an insidious problem facing founders and investors: observational bias.
We have a tendency to study things that are easy to study; to use data that’s easy to source; to seek the truth where it’s most easily found, rather than where it actually lives.
The field of cardiology offers an instructive (and very sad) example of this problem. In the early 1980s anti-arrhythmia drugs burst onto the scene. These drugs became standard issue for heart-attack patients, smoothing out heartbeats all over America.
But a decade later, cardiologists realised that the drugs were killing around 56,000 heart-attack patients a year. Doctors had been so focused on measurable arrhythmias, they had overlooked the disastrous long-term implications of the drugs used to treat them.
Observational bias is an invisible problem. Most founders simply can’t see it. And it leads them to build the wrong products, measure and optimise the wrong metrics, and waste a lot of capital.
The Streetlight Effect plagues investors, too. As data, platforms, and financial instruments have proliferated it becomes even harder to know where to look for value and growth.
Take the gold market. Analysts fixate on a tiny fraction of the overall gold supply because this is where the data is, but a large proportion of global gold supply is ignored because it cannot be located and measured. It doesn’t exist, but of course, it does.
Understanding what’s missing from the supply/demand fundamentals of any market can be a powerful tool for understanding price dynamics.
Implications for investors
When it comes to startup investing, everyone agrees that data is the future.
But if you only use data that’s easily available, you will have no edge over the market. This is precisely what most “data-driven” VCs are doing. Their outputs are only as good as their inputs, and those inputs tend to be run-of-the-mill datasets from mainstream databases.
The key to leveraging data in the startup investment process is investment in data acquisition, management, and deployment. Working with data without compromising its integrity and utility is a huge challenge, and the vast majority of VC firms are simply not set up to do this well.
That’s why we’re building Koble as a thoroughbred “Quant VC”. Data isn’t part of our investment process. It is our investment process.
Hedge funds (the successful ones) get this. In public markets, technology and systematic strategies now represent 75% of total trading volume (with the usual distribution of winners and losers, and everything in between).
These guys understand the critical importance of data, investing eye-watering sums in finding it and refining it. But most importantly, they know where to look. And they’ve started looking at private markets.
Systematising startup investing presents significant challenges when it comes to data availability, quality, and actionability. But for the first time in the history of our asset class, these challenges are surmountable.
You just have to know where to look.
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 success.
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.
📉 Computer-driven trading firms fret over risks AI poses to their profits – Executives at quantitative trading firms warn that machine-generated misinformation is a new frontier.
💰 Ex-Ardian execs, former Coller data scientists launch AI secondaries firm – London-headquartered Clipway uses artificial intelligence and machine learning to price LP portfolios.
🤖 What the Pentagon Thinks About Artificial Intelligence – Artificial intelligence may transform many aspects of the human condition, nowhere more than in the military sphere.
“Not everything that can be counted counts, and not everything that counts can be counted.”
― Albert Einstein
Regards from your [enlightened] startup investing AI,
Koble is re-engineering startup investing with AI, applying quantitative strategies that have disrupted public markets to early-stage startup investing.