Platform Economics- Why Meta made a Profit and Uber did not [Finance Fridays]
Understanding the economics of platforms and what why some platforms are unicorns and others just hype.
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In case you haven’t heard- Uber, Lyft, Doordash, AirBnb, and other poster children of the gig economy still haven’t made a profit. Despite what executives or new metrics might tell you, these companies continue to burn money.
This begs the question- why? Uber and co were inspired heavily by the Facebook business model- grow aggressively around a service, attract users onto your platform, and then you have an unchecked monopoly to do with as you please. In Uber’s case- this would have enabled them to continue to raise the prices as they pleased. With no inventory and a fleet of contractors, we were looking at a money-making machine. So why didn’t this play out? The problems with the gig apps are complex and warrant their own article. Today, we will look at one major component of this puzzle- the faulty economics of these platforms. In this edition of Tech Made Simple, we will be looking at why Meta and YouTube are profitable while their children are not by looking at the platform economics perspective. Misunderstanding the principles of platforms is what led to a lot of misplaced founded hype. To be a great engineer, manager, or investor- it is important to understand how to apply this lens to evaluate systems and ideas.
Ready to learn more yet? Let’s get into it.
Why Uber’s platform did not benefit from scale
The Platform Playbook- Before we proceed, let’s first understand the platform playbook properly. The playbook is placed on the network effect- the value of a social network grows exponentially with the number of connections in that network (we covered this in more depth here). This approach sacrifices short-term profitability for scale- in hopes that the increased utility will eventually lead to revenues. This is a relatively new phenomenon. Most traditional businesses operate on profitable unit economics- each individual sale is profitable and profits are increased by using more sales. In platforms driven by the network effect- companies run at losses for a few years until they hit a critical point: where the connections in their graphs suddenly become profitable. The platform playbook is built on this idea- offer a free/cheap service at a loss, blitzscale your way to glory (till you have no competition), and enjoy your monopoly. So why did this fail for Uber? Let’s understand this by seeing what Meta and YouTube did right?
Looking at Facebook and YouTube- Both these channels had very similar stories- they bled money, stored very expensive data for free, and held on long enough till the point where they had very comprehensive user profiles. At this point- advertisers were practically salivating to get in on this action. Both YouTube and Facebook beat back many many competitors over the years- and have remained undisputed in their space. Their large user bases gave them a huge advantage- they had more data and better profiles than their competitors. This meant that advertisers were willing to pay a premium to promote their services on these platforms. So why didn’t Uber and the rest replicate the success?
Reason 1: Ability to Clone- What do you really need to build a competing service for Uber? At it’s core Uber’s simplicity is a double-edged sword- it allows them to stay lean but the app can be copied very easily. It’s not terribly hard to build a localized ride-share with basic dynamic pricing. You might not have the riders or drivers, but they can be acquired. Features can be copied. This is true for most predominantly online businesses- including Facebook and YouTube. So why they have succeeded while Uber failed? This is where the second reason kicks in.
Reason 2: Cost of Switching- If the fees get too high- what stops a driver from switching from Uber to Lyft/another competitor? What stops the rider from using the other services? Nothing. This is one area where YouTube and Facebook are far ahead of the apps they inspired- there is in-built loyalty to the platform. The content keeps the normal users, and the profiles keep the advertisers, even if there is a strong premium to these services. This is not true for Uber. If Uber raises their prices, people will switch to Lyft. If all the major players raise their prices- a local competitor will swoop in to eat their lunch. These two are big problems on their own. On top of these, there is a third. And this third is a deep, fundamental problem with Uber and other services- one that has been largely overlooked.
Reason 3: False Expectations- Uber should not be a billion-dollar company. Food Delivery is not a Billion Dollar business (until you optimize for delivery like Pizza Stores). A physical service that is on-demand is going to be expensive because this on-demand nature is a large cost (think of how buying your tickets in advance or providing services that were scheduled ahead of time is always easier). You can’t engineer your way around this. These premiums would limit your serviceable market strongly- which would stop these companies from reaching the scale they have. You can’t be cheap, on-demand, and operating at a large scale all at once, unless you have external money flowing to stabilize this. Not understanding this has led to billions of dollars being burned in futile attempts to change the landscape of the solution. We will discuss this idea in more depth in a depth look at the Gig Economy and its actual performance.
If Uber and the like should teach you anything- it should be that you can’t engineer your way out of an economics problem. Time and time again, we have seen people build solutions to ‘disrupt’ an industry- only to realize that the problem they solved was a very small element of the overall puzzle. If you’re looking to build solutions for a particular domain/challenge, actually study it and understand the solutions. It will save you a lot of headaches- and stop you from getting swept up by meaningless hype. Not understanding the domains that they have set out to revolutionize is the bane of many Tech Folk. Hinton infamously predicted that AI would replace radiologists (not dissimilar to how Musk promised Full Self-Driving by 2020). And neither prediction worked out.
Geoff made a classic error that technologists often make, which is to observe a particular behavior (identifying some subset of radiology scans correctly) against some task (identifying hemorrhage on CT head scans correctly), and then to extrapolate based on that task alone.
LONG STORY SHORT: thinkers have a pattern where they are so divorced from implementation details that applications seem trivial, when in reality, the small details are exactly where value accrues.
If you’re into business and finance, would highly recommend this detailed video by the guru ModernMBA on Uber. His works on AirBnB and Food Delivery are great too.
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Good points, Devansh. One additional observation: there's only one Meta out there, a Big Kahuna who took its first mover advantage and not only helped to invent the new landscape, but embedded itself into the lives of billions. This was certainly a right place, right time scaling up that I don't think any company founded after 2010 could hope to attain.
Great post. The only part I disagree with is the characterization that Uber will never work economically. I take your point that on-demand ride hailing isn't going to work, but food delivery has been working for them, and I think virtually all on-demand services that can be fulfilled with a single car will work when those cars can drive themselves (this is not an original idea, but maybe the more PC way of phrasing what Travis Kalanick used to say publically...)