Inflection Points: The Dangers of Premature Scaling


This opening edition of Inflection Points highlights a chart found in the newsletter of former Uber head of growth and current a16z partner Andrew Chen. But before we get to that, if you haven’t read it already, check out our own Fernando Dal Re’s excellent article on Crunchbase on advising early-stage founders not to accept big VC checks too early. Instead, they should raise smaller checks from smart, operational investors who will help them beyond merely providing cash (sound like anyone you know? Hey! Over here! ✋). The danger, he says, is that “with more money to play with, startups aren’t encouraged to adopt a lean mentality and have a higher risk of scaling prematurely." 

Mr. Chen may have seen this and decided that he needed to be on the “Premature Scaling” corner with Mr. Dal Re. Not long after Fernando's article was published, he sent out a newsletter with the subject “Why premature scaling fails: The Traction Treadmill.” 

Chen makes the same basic point as Dal Re that trying to scale before getting product-market fit can kill your company, and then proceeds to describe how too much user churn means large outlays of money to continue growing. (Is he talking about Uber?🤔) Chen asserts that the larger your user base, the harder it becomes to iterate on your product to make it stickier. Your team is bigger and the product’s complexity is higher, making it hard to scale and fix at the same time while also locking you into the product that you have. "Scale is the enemy of iteration", he says.

He complements his discussion of what good retention and acquisition look like with C3 charts. My favorites! (For more on C3 charts, check this article from my favorite customer lifetime value thought leaders, Fader and McCarthy.) They illustrate cohort user or revenue retention over the long term, combined with the growth of the overall metric over time.

Source: Why premature scaling fails: The Traction Treadmill

Note that strong retention on the top row shows up as horizontal (or, in some cases, upward sloping) cohort revenues over time for each cohort, while the lousy retention on the bottom row shows a downward slope. The “meh” acquisition in the left-hand column looks like linear or plateauing revenue over time, while the “on fire” acquisition is super-linear or exponential. 

I think Dal Re and Chen would agree with me that startups with horizontal or upward-sloping C3 charts have strong quantitative signals of product-market fit and may be ready to scale. Such a startup has arrived at an Inflection Point!