You could make a mistake of being overly predictive. Like, you go look at Uber and you say, well, it's only like the black cars, like the current limo fleet. When you look at Airbnb, you could either say, it's tiny because you're comparing it to people using classified to rent rooms, or it's huge because it's the entire stay in the travel industry.

Like, you go look at Uber and you say, well, it's only, like, the black cars, like, the current limo fleet.

3:30 / 4:12

And, you know, but, you know, like, you could both make a mistake of being overly predictive. Like, you go look at Uber and you say, well, it's only, like, the black cars, like, the current limo fleet. And you're like, like, the most often one is current TAMs or what what dedicates stuff. And, like, for example, when you look at Airbnb, you could either say, it's tiny because you're comparing it to, like, people using classified to rent rooms, or it's huge because it's the entire, like, stay in the travel industry, you know, industry. And you gotta kinda have a theory of the game of what that is and how you'll be doing it. And it kinda combines to what some of your current things is, what competition looks like, what it occludes. So it isn't really a,

Why this clip

Uses concrete examples of Uber and Airbnb to illustrate TAM analysis mistakes. The specific company references make this relatable and the framework around market sizing is evergreen advice for founders.

3:30 - 4:1242sPractical Framework

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What they said next

In this AI moment in Silicon Valley today, the revenue ramp of some of these companies is truly extraordinary. Like, their company is going from 0 to $100 million of revenue in record times. And of course, there are also a lot of companies that are scaling revenue and then losing revenue or churning customers.

1:58 - 31s · Consequences

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