Underutilized Fixed Assets

Every marketplace is unique. But every successful marketplace is unique in the same ways. Historically it’s very hard to find a successful marketplace that wasn’t built on an underutilized fixed asset.

Airbnb is a canonical example of this. Someone has a guest bedroom. It’s always there, a fixed asset. But it’s unused and they make no money from it. Until Airbnb, they may not even have considered that they could make money from it. And then suddenly, with Airbnb it generates hundreds of dollars for them. It’s like an ATM they didn’t know existed in their guest room.

What Exactly are Underutilized Fixed Assets

To understand underutilized fixed assets (UFA) is to understand each component of the term.

Fixed vs Variable Assets

Fixed assets are those where the cost of them is constant, and independent of usage. If you buy a pan, it is a fixed asset. You have already paid for the pan. Whether you use it once or a hundred times, the total cost does not change. Having gasoline for your car on the other hand is a variable asset. If you don’t drive at all, you will not spend any money on gasoline. But the more you drive, the more you will have to refill your tank and by extension pay for gas.

This difference between fixed and variable assets is flipped when you look at their costs on a per usage basis. Variable assets are relatively constant in their costs per usage. While the more fixed assets are used, the cheaper their cost per usage falls. Usage of fixed assets is amortized across all usage of them, so they have a natural economy of scale.

There’s a third and more important way to view fixed assets. They can be viewed as assets that are already paid off. Any incremental uses of them are functionally free. Unlike variable assets, where incremental usage always still carries a cost.

Underutilized vs Fully utilized assets

Assets have a maximum amount they can be used. Both in terms of the frequency with which they can be used as well as the total number of times they can be used. There can be significant variance between two similar assets on their maximum utilization or even disagreement about what is a reasonable benchmark to use for max utilization, but those are in-the-weeds details. Underutilized assets are those that are not used very much, while fully utilized assets are those whose usage is close to the maximum possible.

Why are Underutilized Fixed Assets important?

Underutilized fixed assets are things with fixed costs that are not being used as much as they could be. They are important because they *can* be used more, and from their owner’s perspective all additional usage is free.

Unlocking early markets

The cross-side network effect of marketplaces is incredibly strong, but equally difficult to create. Convincing both sides of a market to to join on the promise of the other side being there is a constant struggle. And simultaneously building both sides is significantly more difficult than being able to focus on just one side of the market.

In the early days, many marketplaces have found an underutilized fixed asset to be an incredible boost to expedite building the supply side of their market.

The best way to think of underutilized fixed assets is as pure potential energy sitting in people’s homes, cars, and random tchotkes. Marketplaces take a tremendous amount of energy to get their flywheel spinning. But it’s easier when there is an external supply of potential energy that can be put to work.

Preferred Pricing

Unlike businesses, most people with underutilized fixed assets baseline their value at zero. Any money these assets turn out to be worth is money they found lying on the floor that they’re happy to receive.

This lets a marketplace bring on supply at a much lower cost of acquisition than expected. And these savings can be passed along to consumers as well.

Latent supply

Once a new underutilized fixed asset is identified, a startup can grow rapidly because there is so much latent supply of the asset initially sitting unused.

This supply tends to be mostly retail. It’s more fragmented sources of supply, with people who are less price sensitive since it’s not a business to them.

One downside with more fragmented supply is the complexity in bringing them online, making them legible, and handling logistics. This tends to work in marketplaces’ favor, however, as it’s the exact type of complexity that software is particularly good at, and allows them to unlock the underutilized fixed asset.

One way to look at marketplaces is a series of supply and demand acquisition elasticity curves. Finding a good underutilized fixed asset is a surefire way to bend the acquisition elasticity curve for a while.

Burn the Bridges

One under-appreciated advantage of underutilized fixed assets is that because they are a finite and arbitragable source of supply, it’s hard for new competitors to replicate once they’ve been discovered and tapped. It’s hard for a new Airbnb to emerge because people with empty bedrooms no longer are unsure what to do with their empty room–they use Airbnb. So a new competitor can try to go after these same potential listers, but they have to compete against Airbnb, rather than on the hosts doing nothing with the bedroom.

Case Studies

For many marketplaces the early patterns are the same.


Before Airbnb, hotels were the primary option available to consumers. Yes, there were some short term house rentals or communities like Couchsurfing, but these alternatives had low liquidity and trust.

This wasn’t due to lack of empty bedrooms available in cities. Every day thousands upon thousands of empty bedrooms and homes are unused by their owners. But these travelers and home owners never used to connect. There was no way for them to find each other, and even if they did, they would not trust each other.

Airbnb bridged this divide. They brought both sides online. They built trust into the platform by handling payment, customer service, and discovery, as well as by building up reviews and photos for each listing.

All of this made it possible to list people’s empty bedrooms and other underutilized fixed assets like this. These listings were often cheaper than hotel rooms since listers had already assumed the costs of their empty bedroom. They are also better positioned, neighborhood-wise, compared to hotel rooms.


Before Uber and Lyft, taxis existed to varying degrees in each city. Mobile ordering and dispatch made UberBlack originally possible. But it wasn’t until Lyft that driving was opened up to anyone with a car. Lyft’s main insight was that mapping and routing apps like Google Maps and Waze commoditized local knowledge in drivers. This made it possible for anyone to become a driver, massively expanding the pool of drivers to anyone with a car.


Ebay was the first of the online marketplaces, and perhaps the most successful. They raised a total of $7M before going public as an already profitable company. Before Ebay, if you had random stuff in your house you either tried to sell it locally, perhaps at a garage sale, or just let it pile up in your house. By making it easy to list, review and discover items online, Ebay brought liquidity to their marketplace and made it possible for people to sell all the random unused things in their house.

New Startups

For every marketplace, in their early days they should be thinking about potential underutilized fixed assets that make sense for their platform.

For example, Hipcamp helps consumers find campsites to rent. Hipcamp adds supply by getting private landowners to list their land, and for many of these people their land was literally sitting fallow before Hipcamp made it possible for them to share it with others.

The Sequencing of Marketplace Supply

There’s something beautiful about retail marketplaces: two sided markets where both sides are just random average users.

They never last. So there’s a wistfulness to watching them, frozen for just a moment.

Look at all marketplaces as they scale, and you eventually find that the percentage of their revenue that comes from retail users on the supply side falls. Power sellers dominate Ebay. Uber is increasingly full time drivers doing it as their full-time job. Airbnb is increasingly full of individuals and now companies that buy properties and convert them into ideal Airbnb listings.

I’ve learned to draw area charts. This changes everything.

A constant debate persists around whether to embrace the professionalization of one’s platform or to stave it off as long as possible. There are deliberate product choices that can tilt the platform in either direction. These decisions are a subset of a burgeoning understanding of the life cycles of a platform, which I’ll cover more in a future essay. But few marketplaces are able to avoid this transition.

Things can only be underutilized for so long

Empty houses, random stuff in your house, and idle cars. 

There is a natural invisible asymptote to these. Eventually, the growth of marketplaces built on top of these underutilized assets slows. They are a great playbook to enter a market, but there is a finite amount of unused assets–and eventually there are no more idling assets to utilize.

Profit drives new behavior

At the beginning, people with underutilized fixed assets dominate the supply side of a marketplace. These people will always have their place on a platform, because they have such a cost advantage. But eventually, others, or even some of the same people, realize that using the marketplace is so profitable that it’s worth it even if they have to bring new supply online.

There are natural economies of scale to this. Someone creating supply on a platform full-time will be better at many things like:

  • Understanding the intricacies of how the platform works
  • Understanding how to best make money on the platform
  • How to best get listed, seen, and discovered on the platform
  • The most attractive ways to invest capital for a return on the platform

Increasingly the best hosts, sellers, and drivers on the platform professionalize. They are already making the majority of their income on the platform. And whether or not they are the majority of sellers on the platform, given their professional usage they are very likely to be the majority of transactions on the platform.

Scaling is a business

To scale beyond a certain rate is difficult using underutilized fixed assets. Besides a finite amount of them, there is a natural limit to the rate at which they grow on your platform.

Professionalized sellers are very different. As long as it’s profitable for them, they’ll do everything they can to expand to meet demand. Underutilized fixed assets are nice because they have a low effective cost basis.

Professionalized sellers are nice because they are variable and will scale as far as can be supported. Large marketplaces gradually shift to professional sellers as they try to maintain their pace of growth.

The consequences of sequencing

Companies consciously or unconsciously make decisions that embrace or reject this professionalization of sellers to differing degrees and speeds. These choices have large impacts on the growth rate and unit economics of the business.

They also have large impacts on the relationship with supply on the platform and regulation by the government. For example, if you think of Airbnb’s regulatory hurdle at a local level, local landowners renting out their bedroom is far more attractive politically than fast growing companies buying up for sale properties to convert them into short term hotel rooms.

Appendix A: Crypto

Crypto is no different. When Satoshi created Bitcoin, they identified an underutilized fixed asset that could be used to bootstrap the cryptocurrency’s security. Bitcoin was set up so that individuals could have their CPUs, which were often left idle, mine Bitcoin when not being used. For early adopters, this was great. They hadn’t realized their unused CPU cycles were valuable–but now it was like they found free money they hadn’t even known about. And in today’s dollars, those who had their CPUs mining made substantial fortunes. And for Bitcoin, the deal was even better. Security requires something computationally hard. The Proof of Work system got others to provide the compute needed to scale security for Bitcoin without Satoshi needing to personally pay significant sums of money for their own compute. These underutilized CPUs were distributed among a very fragmented and large set of early adopters. Diminishing fears of miner centralization and having the very users be the source of security as well.

However, Bitcoin is also a good example of the limits of underutilized fixed assets. Though underutilized CPUs may be the cheapest source of compute (free). Bitcoin mining is a zero sum game. One’s return is roughly proportional to what percentage of mining you are. This is ideal for Bitcoin, because it makes a competitive red queen equilibrium, where miners must keep working to bring more compute to bear at as low costs as they can in order to keep pace, much less gain ground on their competitors. What matters is not having the cheapest compute, but having the most scalable compute that is still profitable. Underutilized CPUs were cheap, but they weren’t scalable.

Enterprising miners soon realized that they could utilize their GPUs for Bitcoin mining. And machine learning startups renting GPUs from AWS soon learned about this the hard way, as miners realized there was a perfect arbitrage by renting GPUs from AWS to mine Bitcoin–and quickly tied up all of Amazon’s GPUs until their prices rose to make it not profitable anymore. Though Satoshi had intended for a distributed user base mining with their personal computers’ CPUs, there were better places along the SLA-Price curve.

Since then mining has become even more specialized and economy of scale, with mining hashrate centralized around a small number of large companies that specialize on mining. These companies create their own ASICs, which are purpose built for mining Bitcoin, and allow for significantly more mining power at lower costs than even GPUs. This has made CPU and GPU mining prohibitively expensive and, even for those with spare CPU cycles, not very profitable.

Appendix B: Food Delivery

Underutilized fixed assets are the topic of this essay. But all the other variants, such as underutilized variable assets, are important to understand as well. Food delivery is a great example of this. Many people often express disbelief that food delivery startups, have been able to get as many restaurants to sign up for them while charging large take rates (sometimes north of 30% now) from the merchants. “How do these restaurants afford it?” these skeptics ask. What these skeptics fail to understand, is that restaurants do not view deliveries the same way they view customers dining in. There are many factors that restaurants are constrained on including, ingredients, labor, kitchen capacity, and dining space. 

For walk-in diners, the primary constraint is dining space. There is an immovable cap on how many tables a restaurant has, and thus how many turns they can do a night [1]. This real estate space is a fully utilized fixed asset. So a startup bringing new diners to a restaurant is entering a zero sum game, especially during peak hours when a restaurant knows they can likely fill all their tables. If a restaurant accepts a diner from a startup and pays them a take rate, this replaces a diner that would have walked in for free. This is the reason why restaurants often don’t list their prime hours on sites like OpenTable. They know they can fill their limited number of tables, so why pay OpenTable a fee for it?

But delivery is different. Real estate space is not relevant to delivery orders. Instead the two main constraints for restaurant delivery are labor and kitchen capacity. Kitchen capacity is an underutilized fixed asset. Most kitchens can handle more orders than they handle each day, but never need to because there’s not enough space in the restaurant for more diners. Like all underutilized fixed assets, restaurant owners are very happy to have their kitchens handle more orders if makes sense.

The other constraint is labor. Restaurants may have some underutilized labor, depending on how busy they are. However, if they have any significant number of delivery orders, they likely would need to have their workers do more shifts, or hire new workers. So labor is an underutilized fixed asset up to some point, but then primarily a variable asset for restaurants.

So when a startup brings new delivery orders to a restaurant, their main question is whether the delivery will be profitable net of the variable costs like ingredients and labor of the restaurant. Other factors like real estate costs are already fixed and so not factored in by restaurants. If these delivery orders are profitable, restaurants are happy to do any and all incremental orders–and will happily pay a higher take rate in return for bringing them the customer. And if the startup brings more customers than they have workers to handle–they’re overjoyed to hire new workers, as long as the economics make sense [2].

Variable assets are great because they can scale well. However, they are far less preferable to underutilized fixed assets for a number of reasons. Their primary weakness is that they can be copied by competitors. Underutilized fixed assets when discovered are have a huge amount of stored value. The first company to properly use them can increase their value significantly. However, after they’ve burned through this arbitrage, future competitors must find a new way to get advantaged distribution fast. This isn’t true for variable assets as we can see in food delivery. The field is increasingly competitive with Grubhub, Uber Eats, and Doordash all competing in increasingly costly battles.


[1] In the US, most restaurants seem to view the number of turns possible per night as being fixed. Many Chinese restaurants, however, do not. Instead they view it as one part of the restaurant trilemma. Food quality, price, and service/speed are all in tradeoff with each other. Chinese restaurants often optimize for great food at low prices, opting to make it up by turning over tables as fast as possible. Often that’s why these places will get your orders before you’re seated, give you your check before you’ve finished eating, and rush you out the door the second you finish the last bite of food. While Westerners often complain about this bad service, it’s the ultimate service: all to maintain quality food at low prices.

It’s surprising that this is not common in the US. Perhaps it’s due to social norms around dining. However, perhaps this is beginning to change. One of the few reliably profitable areas of the restaurant industry, Quick Service Restaurants (QSRs) are all about improving how much throughput each location can handle.

This chart is the only reason I published this entire essay

[2] This is why delivery companies are not winner-take-all on supply. Restaurants are rarely exclusive with delivery companies, because they want any and all incremental demand they can get. In fact, it’s not enough to charge a lower take rate than your competitors, because restaurants will still accept all incremental orders they’ll make money off of–since they’re almost never over capacity. This is why delivery companies must compete to corner the market on demand if they want to be winner take all.


Thanks to Keila Fong for making sure that Kwokchain: Year 2 is not an entirely empty book.

Understanding how Uber & Lyft Grow in Markets

Bill Gurley popularized the idea of expanding the market in his 2012 blog post “All Markets Are Not Created Equal: 10 Factors To Consider When Evaluating Digital Marketplaces”. Number seven on his list is the idea that marketplaces can actually expand the Total Addressable Market size of the industries they operate in (known popularly as TAM Expansion). The idea is that by changing the price point, making it more convenient, or changing other parts of the value proposition of a product, companies could actually grow the industries they were part of. All marketplaces aspire to TAM expansion; few achieve it.

Gurley’s 2014 essay “How to Miss By a Mile: An Alternative Look at Uber’s Potential Market Size” focused on the idea of TAM expansion as a core part of understanding the potential of marketplaces. Gurley wrote the essay to rebut a critic of Uber, who focused on the TAM of the ridesharing market to explain why Uber’s valuation was too high. Gurley brought up a number of ways Uber might expand the ridesharing market.

Was Gurley right about Uber? Has it expanded the market size for taxis? And if so–how? Over the years there have been many arguments about whether Uber was truly growing the taxi market, or just killing the existing industry. It’s hard to know what lessons to draw from Uber and other ridesharing companies—without seeing actual data about their impact.

Table of Contents

  1. Introduction to NYC Taxi and Ride Hailing Data
  2. Uber and Lyft Expand the Taxi Market
  3. TAM Expansion Driven by the Outer Boroughs
  4. Unmet Latent Demand in the Outer Boroughs
  5. Expansion of Taxi Market Shows No Sign of Slowing
  6. Uber and Lyft Both Grow and Cannibalize Markets
  7. Final Thoughts
  8. Post-scripts

Introduction to NYC Taxi and Ride Hailing Data

It’s rare that we get to quantitatively examine a case of TAM expansion as there is little publicly available data of examples of it. But thanks to the NYC Taxi and Limousine Commission and 538, we have the data to analyze the effect of ride hailing vehicles on the taxi market. And most of all, thanks to Todd Schneider, who has not only done significant work to clean and organize the data–but also graciously open sourced it all. With this data, we can examine date and pick-up location for all taxi or ride-sharing rides going back the last decade.

Market expansion is a poorly understood area. Some people think that it is rarely real, and startups mostly cannibalize existing markets. While others view it as an ephemeral property of companies that either happens to strike or not for a lucky few. Few have seen hard data on what market expansion looks like, so it’s understandable that there is little shared understanding around it.

Looking at NYC data on the taxi and ride-sharing markets we can determine that the taxi market is expanding, with the important caveat of outer-borough rides contributing to the bulk of new growth. The data helps show a concrete example of what market expansion looks like—and that it’s not a vague attribute of companies—but specific to the customer segments where the service solves the needs of customers that previously were underserved by the status quo services. Though not part of the scope of this piece, market expansion can be thought of as customer segmentation including segments that previously would not have been customers of the market given the prior levels of service and cost.

Uber and Lyft Expand the Taxi Market

Looking at how many Ubers and Lyfts blanket the streets of most major cities, many would assume that they have expanded the size of the taxi market. They’re right.

Here’s data on monthly rides in NYC of Taxi AND ride hailing trips combined. See if you can guess when Uber and Lyft started to take off.

Uber and Lyft started to have a real effect starting in 2014. Before then the NYC taxi market was relatively stable—averaging around 14 million rides per month consistently for the half decade leading up to 2014. Since 2014 the number of rides has begun to expand—and hit 25 million a month at the end of 2017. Gurley was correct that companies like Uber could expand the market, to the surprise of nobody who has walked in NYC or SF.

To understand what is driving this market expansion (in NYC at least), we need to cut the data a level deeper.

TAM Expansion driven by the Outer Boroughs

The most striking cut of this data is comparing Manhattan to the Outer Boroughs. The New York Times has written about the impact of Uber in the outer boroughs. And 538, which deserves much credit for their push to get this data released, has multiple articles analyzing the impact of Uber in Manhattan and the Outer Boroughs.

The data is striking on the different impact of ride hailing apps on Manhattan vs. the Outer Boroughs. Here’s the same chart of monthly rides, but colored by trips begin in Manhattan vs those in the Outer Boroughs.

When we talk about Uber and Lyft expanding the taxi market, it’s largely a story of the Outer Boroughs. As Uber and Lyft have grown over the last 5 years, the Outer Boroughs have been responsible for most of NYC’s increase in rides.

Unmet Latent Demand in the Outer Boroughs

The next step is to cut the same data by Taxi rides versus ride hailing rides. The chart below keeps the separation of Manhattan and the Outer Boroughs but also color codes taxis yellow and ride hailing vehicles gray.

These charts show the larger picture of Uber and Lyft’s impact on NYC. In Manhattan alone, Uber and Lyft have been taking real market share from taxis. More recently, they may be starting to expand the market.

It’s a different story in the Outer Boroughs. While Yellow taxis are supposed to serve the Outer Boroughs, historically they’ve avoided them—opting for the high density of Manhattan where it’s easier to find rides. Green taxis, which are cheaper and easier medallions to get but are not allowed to serve Manhattan, were specifically introduced to bring more supply of taxis to the outer boroughs. Looking at our data, we can see in retrospect that neither Yellow or Green taxis were able to serve anywhere near the true latent demand for ride sharing services.

Expansion of Taxi Market Shows No Sign of Slowing

Separating Manhattan and the Outer Boroughs into two charts side by side allows us to compare their relative scales and composition. I’ve done this in the chart below, while also separating out Yellow and Green taxis. Demand for rides in the Outer Boroughs is strong and approaching the daily scale of demand in Manhattan. And it’s not slowing down. It may be obvious to us in a few years that the Outer Borough has always had greater demand for ride services, given its lower density and fewer subway connections—and that taxis weren’t really meeting that demand. Supply met demand only after the ride-sharing apps came around.

Uber and Lyft Both Grow and Cannibalize Markets

The data now lets us evaluate if Uber and Lyft are expanding the market or replacing the taxi drivers.

We can begin by examining the impact ride-hailing apps have had on the taxi market over the last five years.

Below is a look at rides in 2012 and 2017 (the left is Manhattan and the right is the Outer Boroughs) colored by type of ride. Yellow are taxi rides, gray are ride-hailing rides, and orange are rides that used to be taxi rides but are now ride-hailing rides.

Though not perfect, this data gives a decent proxy for the industry. Over the last five years the Outer Boroughs have unambiguously seen an expansion of the market while the story is more complicated in Manhattan. While Uber, Lyft, and their competitors have expanded the market in Manhattan, they have also replaced many rides that would have otherwise been served by taxis.

With these charts we can look at what percentage of Uber and Lyft rides are net new rides as opposed to rides that might have been previously handled by taxis.

In Manhattan, around 70% of rides on ride-hailing platforms would likely have otherwise been served by taxis. While in the Outer Boroughs almost all rides are net additive.

Proponents and detractors of Uber and Lyft are both right. These companies both cannibalize AND expand the market. But the degree to which they do varies significantly along geography, density, and other factors. It should be noted that NYC is likely the city with the most robust pre-existing taxi market in the US. While we don’t have that data, it’s reasonable to expect data from other cities to look more like the Outer Boroughs than Manhattan. In general, ride hailing apps are likely to have cannibalized the market in a few dense cities in the US; and in the overwhelmingly non-dense parts of the US, they’ve expanded the market.

Final Thoughts

In his rebuttal to Aswath Damodaran, the top potential new use case Gurley suggested Uber had was:

1. Use in less urban areas. Because of the magical ordering system and the ability to efficiently organize a distributed set of drivers, Uber can operate effectively in markets where it simply didn’t make sense to have a dense supply of taxis. If you live in a suburban community, there is little chance you could walk out your door and hail a cab. And if you call one of the phones, it is a very spotty proposition. Today, Uber already works dramatically well in many suburban areas outside of San Francisco with pick up times in less than 10 minutes. This creates new use cases versus a historical model.

Gurley was right. The data shows that ride hailing apps have improved their improved promptness, reliability, and service-level of taxis while increasing coverage. And that has allowed the overall taxi market to significantly expand in less dense areas, like the Outer Boroughs. It’s a demonstration that tech companies can use technology to find new levels of service while coherently handling increased scale of liquidity, unlocking discontinuous improvements of the customer experience.

Uber and Lyft realized two important things:

  1. Mobile technology has made it possible to automate dispatching for all drivers more efficiently than possible before—leading to a centralization of dispatch.
  2. Mapping software like Google Maps and Waze can embed expert local knowledge into the phone, allowing anyone to become a driver.

These developments gave them the pricing power, SLA, and thick supply to meet the needs of consumers in the Outer Boroughs, which were not well served previously.

Uber and Lyft understand the importance of segmenting markets. They were among the first marketplaces to understand that they should treat different cities differently—even staffing local teams in each city to better address issues best solved locally. Many of the economies of scale and network effects in the ridesharing business exist within cities—but not between cities. Similarly, the data shows that there are important differences between urban cores and less dense areas even within the same city.

Useful segmentations in companies aren’t just limited to geography. For example, many of Pinterest’s metrics can be segmented by the different topics that pins are about. Choosing the right ways to segment a company’s business to best understand the business and which areas are related and affect each other is key to figuring out how well a company is performing—and what areas are compounding.



I want to give a serious shout out to Todd Schneider’s blog. The data used in my charts come from his painstaking work to not only pull, organize, and analyze the data—but also his gracious open sourcing of it all. I highly recommend reading his analysis, which covers a broad set of fields, and his blog in general. Todd posts infrequently, his essays are gold, and he always presents fascinating data.

Also want to thank Michael DempseySaam Motamedi, Arjun Narayan, Dennis Tang, Dan Wang, and Eugene Wei for their help with this post.


[1] More rigor around the understanding and quantification of the probability and potential of TAM expansion for different marketplaces is an important area of work. There’s interesting debates to be have on this subject–and subject for future discussions.

[2] Astute observers will point out that TAM (and TAM expansion) only exist relative to how one segments the market. There is no such thing as a free lunch. Or rather, there is a free lunch—as long as it’s someone else’s. For example, while Uber and Lyft appear to be growing the market for taxi rides in the Outer Boroughs. It is very likely that if you looked at the broader Transit market (including both taxis and public transit) that much of Uber and Lyft’s growth has replaced rides that might have otherwise been on bus or subways. MTA data suggests this is true. TAM expansion is often the cannibalization of substitute markets. Further work is needed to understand what share of Uber’s rides comes from taxis, public transit, or are net new rides.

[3] TAM expansion is key in marketplaces, beyond being a source of unexpected good fortune. Marketplaces typically improve the efficiency and liquidity of a market. However, improving the efficiency of a market naturally shrinks its size—so without an expansion of the market, marketplaces typically decrease the original market size. However, by unconstraining their markets and making possible new levels of service and cost, they may induce new use cases or market segments that were not possible or feasible before—expanding the market.

[4] Hopefully this piece is a small illustration of the benefits of looking at various cuts at one’s data to understand what are the distinct segments and core loops that are really driving aggregate performance. While practitioners at startups and some at venture firms are able to look at real data, there is little real data available to the public. Would love to see more data shared publicly—we collectively advance in our understanding of marketplaces and network effects most when learnings are distributed publicly.