Why It’s So Hard to Scale a Great Idea
Why do some products, companies, and social programs thrive as they grow while others peter out? According to the author, there are five causes: 1) False positives, or inaccurately interpreting a piece of evidence or data; 2) Biased representativeness of data; 3) Overrepresentation of evidence; 4) Bias in data; 5) Over-optimism
False Positives
This occurs when you interpret a piece of evidence or data as proof that something is true, when in fact it isn’t
- Sometimes a false positive occurs because of a statistical error, e.g. the D.A.R.E. program for drug abuse prevention in the United States.
- In other cases, false positives result from intentional lying
Negative Spillovers
As you scale, the likelihood of spillovers increases dramatically
- When designing your idea early on, you must anticipate negative spillovers and look for opportunities to engineer and benefit from positive ones
- Positive spillovers exist too, like network effects that make a social-media platform more valuable as more people join it
The Cost Trap
To scale successfully, you need to determine not only how many people like your idea, but also what they’re willing to pay for it and, crucially, how much it will cost to provide.
- One strategy to escape the cost trap is to make sure you benefit from economies of scale
- Elon Musk excels at this
- Another strategy is to create models that don’t rely on top-tier talent – create products that can give their full value to customers even with average performers delivering it
Biased Representativeness of Population
Make sure your test samples in the small scale reflect the larger population at scale.
- To weed out such biases, make sure your early adopters are a random sample.
- You should also make sure that your survey respondents have appropriate incentives to tell the truth.
Non-Negotiables That Can’t Grow or Be Replicated
To hold strong at scale, you need to know whether your “non-negotiables” can be replicated at scale.
- As you scale, regulatory constraints, resource constraints, fidelity concerns, and a host of other issues might arise.
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