1/ Do you know how big companies make decisions? They build scenarios and models on spreadsheets.
They do this because often the decision maker’s job is at stake, so all substantial decisions by that person require justification which is often to be had from numbers.
2/ It’s a myth that big companies don’t take risks. Introducing new products is risky and so is expanding into new geographies. In fact, all decisions are risky in a way. (If they weren’t, no decision is required as it’s simply obvious to all).
3/ Given that big companies take risks regularly, it’s a miracle that startups exist at all. After all, big companies are better resourced, have established brands, and are better capitalized than startups.
4/ So why do startups regularly keep popping up onto the scene and some of them even end up becoming big companies? That should be impossible, yet it’s a regular occurrence.
5/ The reason for this is that while big companies take risks, they hate uncertainty.
The difference between the two? Risk can be modeled and quantified on a spreadsheet, while uncertainty can’t.
6/ Uncertainty is when something is beyond quantification.
It’s what you feel when thinking about whether people will be ready to rent out their beds to strangers (AirBnB) or if anyone will ever buy a book without physically flipping its pages (Amazon).
7/ Big companies cannot model the market size of people buying books online because it’s completely uncertain (until a startup actually figures this out by actually doing it).
8/ And because key assumptions cannot be modeled, big companies won’t fund internal competitive projects until the uncertainty is fully resolved.
9/ Often by that time, the startup has become too big to kill as it would have developed its own moats.
10/ In this sense, uncertainty provides a cloud of fog that big companies systematically avoid.
11/ What if the uncertainty that a startup is pursuing is resolved before it develops moats?
This often happens with inventors or discoverers who show the world that something hard is possible, and then someone better resourced ends up eating their lunch.
12/ So, uncertainty about a key dimension of the business is good for a startup.
If everything is obvious and known about your startup, you need to ask why is no one else eating this obvious source of value? Is it possible that you’re deluding yourself?
13/ An important caveat about uncertainty is that since startups are poorly resourced, having too many uncertainties is also not a good thing.
14/ Having one uncertainty is as good a deterrent as many, so a wise choice for an entrepreneur is to keep unknowns to a minimum (ideally just one).
15/ These unknowns can range from technology viability to market acceptance to revenue models or go to market approach, but which when resolved, works in favor of the startup.
16/ The idea of uncertainty as the fuel for startup success has all sorts of unintuitive implications.
17/ First, it explains why successful startups look like toys when they start.
It’s because successful startups have to feel unconventional. If they’re too obvious early on, competition will eat them up.
18/ Second, it explains why successful startups start by appealing to niche markets.
This is because, for the mainstream market, solutions to obvious problems are already being pursued by big companies.
19/ For example, there’s very little uncertainty in knowing that a higher mileage car will sell more and that’s why big companies continue to push fuel efficiency forward.
20/ Tesla, a startup, survived because it wasn’t certain how big a market was there for low-range, expensive cars that require a charger installation at home.
21/ Third, it also explains why startups often commercialize existing technology and don’t make fundamentally new inventions or discoveries themselves.
22/ That’s because if a new technology, once invented, is known to obviously be useful (like better fuel efficiency), it’s being pursued by big companies’ R&D departments.
23/ But if it’s not certain if a new technology will be useful, then no investor is willing to fund the double whammy of market and technology uncertainty.
24/ Sidenote: this is also why deep-tech startups have a lower odds of success than conventional tech startups. It’s because if a research area is well known to be important, deep-tech startups compete directly with thousands of researchers at better-resourced academic and industrial R&D labs.
25/ To sum up, the following aspects seem to make up the recipe for a successful startup:
- Large uncertainy about a key business dimension
- Uses existing technology
- Pursues a niche market
- Unconventional, “strange” offering
- Inferior current alternatives
- Builds strong moats while uncertainity is still unresolved
26/ If there are multiple uncertainties, how should an entrepreneur resolve them?
Prioritize the most significant ones early on, when the (opportunity and funding) cost to failure is the lowest.
28/ In fact, you can say that my essay is really a compilation of notes from Jerry’s blog.
I highly recommend you read it.
This essay is part of my book on mental models for startup founders.
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