Let's start with a simple question: why do early-stage startups want revenue? We all know why big companies want revenue -- it's one of two critical halves of the formula for profit. And big companies exist to maximize profit. Don't startups exist for the same reason? Such reasoning is an example of the "startup dollhouse fallacy" -- that startups are just shrunken-down big companies. In fact, revenue is in and of itself should not be the goal for startups, and neither is profit.
What matters is proving the viability of the company's business model, what investors call "traction." Demonstrating traction is the true purpose of revenue in an early growth company. Further, traction is not just important for investors. It should be even more important to the founders themselves, because it demonstrates that their business hypothesis is grounded in reality; that they are not wasting their time.
Consider a fictitious company practicing validated learning. This company has very little revenue today but has a large long-term vision. Their current product is only a fraction of what they hope to build, but they are selling it. They are operating at micro-scale.
They are not selling their product by hand. Instead, each potential customer has to go through a self-serve process of signing up and paying money. Because they have no presence in the market, they have to find distribution channels to bring in customers. They can only afford those (like Google AdWords) that support buying in small volume.
Compensating for these limitations is the fact that they know each of their customers extremely well. They are constantly experimenting with new product features and product marketing to increase the yield on each new crop of customers they bring in. They have learned what works and what doesn't. Over time, they have found a formula for acquiring, qualifying, and selling customers in the market segments they have targeted. Most importantly, they have lots of data about the unit economics of their business. They know how much it costs to bring in a customer and they know how much money they can expect to make on each one.
In other words, they have learned to grow renewable audiences. Given the data they've collected about these early customers, they are also able to estimate with modest precision how big the market is for their product in its current form. They may be at micro-scale now, but they are in a very good position to raise venture money and engage in extremely rapid growth.
Validated learning as a unit of progress is remarkable in several ways. First of all, it means that most aggregate measures of success, like total revenue, are not very useful. They don't tell us the key things we need to know about the business: how profitable is it on a per-customer basis? What's the total available market? What's the ROI on acquiring new customers? And how do existing customers respond to our product over time?