Aggregate critical mass of end demand
Aggregators own the end user relationship by providing differentiated discovery & curation, aggregate demand, and thereby modularize suppliers. They monetize via advertising and transaction fees.
Google, Amazon, Netflix, Uber, Airbnb, YouTube, Zillow, Booking, Instacart, DoorDash
Aggregation is fundamentally about owning the user relationship and being able to scale that relationship.
All Aggregators have the following three characteristics:
Users reap value through discovery and curation, and most aggregators get started by delivering superior discovery.
They don’t supply the goods themselves, just the connection between buyer and supplier.
“Once an aggregator has gained some number of end users, suppliers will come onto the aggregator’s platform on the aggregator’s terms, effectively commoditizing and modularizing themselves. Those additional suppliers then make the aggregator more attractive to more users, which in turn draws more suppliers, in a virtuous cycle. This means that for aggregators, customer acquisition costs decrease over time; marginal customers are attracted to the platform by virtue of the increasing number of suppliers. This further means that aggregators enjoy winner-take-all effects: since the value of an aggregator to end users is continually increasing it is exceedingly difficult for competitors to take away users or win new ones. This is in contrast to non-aggregator and non-platform companies that face increasing customer acquisition costs as their user base grows. That is because initial customers are often a perfect product-market fit; however, as that fit decreases, the surplus value from the product decreases as well and quickly turns negative. Generally speaking, any business that creates its customer value in-house is not an aggregator because eventually its customer acquisition costs will limit its growth potential.”
There are different levels of aggregation based on the aggregator’s relationship to suppliers:
Level 1 Aggregators, like Netflix, acquire their supply
Level 2 Aggregators, like Uber, do not own supply but incur transaction costs in bringing suppliers onto the platform. They “typically operate in industries with significant regulatory concerns that apply to the quality and safety of suppliers.”
Level 3 Aggregators, like Google, do not own supply and do not incur transaction costs in acquiring it.
Super-Aggregators: operate multi-sided markets with at least three sides (users, suppliers, and advertisers) and have zero marginal costs on all of them. Google and Facebook are the only two such examples.
https://stratechery.com/2017/defining-aggregators/
https://stratechery.com/2015/aggregation-theory/
https://stratechery.com/2017/the-super-aggregators-and-the-russians/
https://stratechery.com/2018/the-moat-map/
https://stratechery.com/2015/beyond-disruption/
https://stratechery.com/2018/zillow-aggregation-and-integration/
https://stratechery.com/2025/the-youtube-tip-of-the-google-spear/
https://stratechery.com/2015/netflix-and-the-conservation-of-attractive-profits/
https://www.notboring.co/p/thatch
https://www.acquired.fm/episodes/google
https://www.platformaeronaut.com/p/monetizing-meals-advertising-ecosystems?triedRedirect=true
https://www.platformaeronaut.com/p/amazons-latest-grocery-push-mapping
https://www.michaeldempsey.me/blog/2025/10/03/sequencing-vs-equal-odds-applied-research/
https://www.danhock.co/p/llms-vs-marketplaces
The more internalized the network effect is (ie the more the network is the product itself), the more the platform tends to commoditize its suppliers. Whereas, for externalized platforms, the network is the ecosystem of developers built on top of the technology and so it’s key to the business model to support and help differentiate those suppliers.
https://stratechery.com/2018/the-moat-map/
The value chain for any given consumer market is divided into three parts: suppliers, distributors, and consumers/users. The best way to make outsize profits in any of these markets is to either gain a horizontal monopoly in one of the three parts or to integrate two of the parts such that you have a competitive advantage in delivering a vertical solution. In the pre-Internet era the latter depended on controlling distribution.
For example, printed newspapers were the primary means of delivering content to consumers in a given geographic region, so newspapers integrated backwards into content creation (i.e. supplier) and earned outsized profits through the delivery of advertising. A similar dynamic existed in all kinds of industries, such as book publishers (distribution capabilities integrated with control of authors), video (broadcast availability integrated with purchasing content), taxis (dispatch capabilities integrated with medallions and car ownership), hotels (brand trust integrated with vacant rooms), and more. Note how the distributors in all of these industries integrated backwards into supply: there have always been far more users/consumers than suppliers, which means that in a world where transactions are costly owning the supplier relationship provides significantly more leverage.
The fundamental disruption of the Internet has been to turn this dynamic on its head. First, the Internet has made distribution (of digital goods) free, neutralizing the advantage that pre-Internet distributors leveraged to integrate with suppliers. Secondly, the Internet has made transaction costs zero, making it viable for a distributor to integrate forward with end users/consumers at scale.
This has fundamentally changed the plane of competition: no longer do distributors compete based upon exclusive supplier relationships, with consumers/users an afterthought. Instead, suppliers can be commoditized leaving consumers/users as a first order priority. By extension, this means that the most important factor determining success is the user experience: the best distributors/aggregators/market-makers win by providing the best experience, which earns them the most consumers/users, which attracts the most suppliers, which enhances the user experience in a virtuous cycle.
The result is the shift in value predicted by the Conservation of Attractive Profits. Previous incumbents, such as newspapers, book publishers, networks, taxi companies, and hoteliers, all of whom integrated backwards, lose value in favor of aggregators who aggregate modularized suppliers — which they often don’t pay for — to consumers/users with whom they have an exclusive relationship at scale. For example:
Amazon
Netflix
Uber
Airbnb
What is important to note is that in all of these examples there are strong winner-take-all effects. All of the examples I listed are not only capable of serving all consumers/users, but they also become better services the more consumers/users they serve — and they are all capable of serving every consumer/user on earth.
https://stratechery.com/2015/aggregation-theory/
value has shifted away from companies that control the distribution of scarce resources to those that control demand for abundant ones
### The Characteristics of Aggregators
Aggregators have all three of the following characteristics; the absence of any one of them can result in a very successful business (in the case of Apple, arguably the most successful business in history), but it means said company is not an aggregator.
Because aggregators deal with digital goods, there is an abundance of supply; that means users reap value through discovery and curation, and most aggregators get started by delivering superior discovery.
Then, once an aggregator has gained some number of end users, suppliers will come onto the aggregator’s platform on the aggregator’s terms, effectively commoditizing and modularizing themselves. Those additional suppliers then make the aggregator more attractive to more users, which in turn draws more suppliers, in a virtuous cycle.
This means that for aggregators, customer acquisition costs decrease over time; marginal customers are attracted to the platform by virtue of the increasing number of suppliers. This further means that aggregators enjoy winner-take-all effects: since the value of an aggregator to end users is continually increasing it is exceedingly difficult for competitors to take away users or win new ones.
This is in contrast to non-aggregator and non-platform companies that face increasing customer acquisition costs as their user base grows. That is because initial customers are often a perfect product-market fit; however, as that fit decreases, the surplus value from the product decreases as well and quickly turns negative. Generally speaking, any business that creates its customer value in-house is not an aggregator because eventually its customer acquisition costs will limit its growth potential.
Classifying Aggregators
Aggregation is fundamentally about owning the user relationship and being able to scale that relationship; that said, there are different levels of aggregation based on the aggregator’s relationship to suppliers:
Level 1 Aggregators: Supply Acquisition
Level 1 Aggregators acquire their supply; their market power springs from their relationship with users, but is primarily manifested through superior buying power. That means these aggregators take longer to build and are more precarious in the short-term.
The best example of a Level 1 Aggregator is Netflix. Netflix owns the user relationship and bears no marginal costs in terms of COGS, distribution costs,
or transaction costs.
Moreover, Netflix does not create shows, but it does acquire them (increasingly exclusively to Netflix); the more content Netflix acquires, the more its value grows to potential users. And, the more users Netflix gains, the more it can spend on acquiring content in a virtuous cycle.
Level 1 aggregators typically operate in industries where supply is highly differentiated, and are susceptible to competitors with deeper pockets or orthogonal business models.
Level 2 Aggregators: Supply Transaction Costs
Level 2 Aggregators do not own their supply; however, they do incur transaction costs in bringing suppliers onto their platform. That limits the growth rate of Level 2 aggregators absent the incursion of significant supplier acquisition costs.
Uber is a Level 2 Aggregator (and Airbnb in some jurisdictions due to local regulations). Uber owns the user relationship and bears no marginal costs in terms of COGS, distribution costs, or transaction costs. Moreover, Uber does not own cars; those are supplied by drivers who sign up for the platform directly. At that point, though Uber needs to undertake steps like background checks, vehicle verification, etc. that incur transaction costs both in terms of money as well as time. This limits supply growth which ultimately limits demand growth.
Level 2 aggregators typically operate in industries with significant regulatory concerns that apply to the quality and safety of suppliers.
Level 3 Aggregators: Zero Supply Costs
Level 3 Aggregators do not own their supply and incur no supplier acquisition costs (either in terms of attracting suppliers or on-boarding them).
Google is the prototypical Level 3 Aggregator: suppliers (that is, websites) are not only accessible by Google by default, but in fact actively make themselves more easily searchable and discoverable (indeed, there is an entire industry — search engine optimization (SEO) — that is predicated on suppliers paying to get themselves onto Google more effectively).
Social networks are also Level 3 Aggregators: initial supply is provided by users (who are both users and suppliers); over time, as more and more attention is given to the social networks, professional content creators add their content to the social network for free.
Level 3 aggregators are predicated on massive numbers of users, which means they are usually advertising-based (which means they are free to users). An interesting exception is the aforementioned App Stores: in this case the limited market size (relatively speaking) is made up by the significantly increased revenue-per-customer available to app developers with suitable business models (primarily consumable in-app purchases).
The Super-Aggregators
Super-Aggregators operate multi-sided markets with at least three sides — users, suppliers, and advertisers — and have zero marginal costs on all of them. The only two examples are Facebook and Google, which in addition to attracting users and suppliers for free, also have self-serve advertising models that generate revenue without corresponding variable costs (other social networks like Twitter and Snapchat rely to a much greater degree on sales-force driven ad sales).
For more about Super-Aggregators see this article.
https://stratechery.com/2017/defining-aggregators/
The name “platform” is a descriptive one: it is the foundation on which entire ecosystems are built. The most famous example of a platform — one with which regulators are intimately familiar — is Microsoft Windows. Windows provided an operating system for personal computers, a set of APIs for developers, and a user interface for end users, to the benefit of all three groups: developers could write applications that made personal computers useful to end users, thanks to the Windows platform tying everything together.
What is critical to note about Windows, though — and this extends to newer platforms like iOS and Android — is that it was essential for the ecosystem to function. Developers could not write applications for another operating system if they wanted to reach users, and users could not use a different operating system if they wanted to use popular applications.
Aggregators are different. This is how I illustrate them:
“Aggregator” is also descriptive: Aggregators collect a critical mass of users and leverage access to those users to extract value from suppliers. The best example of an Aggregator is Google. Google offered a genuine technological breakthrough with Google Search that made the abundance of the Internet accessible to users; as more and more users began their Internet sessions with Google, suppliers — in this case websites — competed to make their results more attractive to and better suited to Google, the better to acquire end users from Google, which made Google that much better and more attractive to end users.
Notably, unlike platforms, Google is not essential for either end users or 3rd party websites. There is no “Google API” that makes 3rd party websites functional, and there are alternative search engines or simply the URL bar for users to go directly to 3rd party websites. That Google is so influential and profitable is, first and foremost, because end users continue to prefer it.
Here is a way to visualize the difference:
To be clear, both roles can be beneficial — platforms make the relationship between users and 3rd-parties possible, and Aggregators helps users find 3rd-parties in the first place — and both roles can also be abused.
https://stratechery.com/2019/a-framework-for-regulating-competition-on-the-internet/
Super-Aggregators
What makes Facebook and Google unique is that not only do they have zero transaction costs when it comes to serving end users, they also have zero transaction costs when it comes to both suppliers and advertisers.
Start with supply: not only is the vast majority of online content accessible to Google’s search engine (unsurprisingly, the biggest exception is Facebook), but in fact that content wants to be discovered by Google. Nearly every site on the web has a sitemap that is intended not for humans but for web crawlers, Google’s in particularly, and there is an entire industry dedicated to search engine optimization (SEO). Netflix is on the opposite side of the spectrum here (unlike YouTube, it should be noted): the company has to actively source content and pay for it. Uber and Airbnb and Amazon are in the middle: theoretically there is an open platform for suppliers but there are costs involved in bringing them online.
Facebook takes this to another level: its users are its most important content providers, and they do it for free. Professional content providers aren’t far behind, not only linking to all of their content but increasingly putting said content on Facebook directly (to the extent Facebook is paying for content it is to juice this cycle of self-interested content production on Facebook).
That said, there are a few more companies that have a similar content model: Twitter, Snapchat, LinkedIn, Yelp, etc. All run on user-generated content augmented by professional content placing links or original material on their services. However, there is still one more thing that separates Facebook and Google from the rest: advertisers.
Super-aggregators not only have zero transaction costs when it comes to users and content, but also when it comes to making money. This is at the very core of why Google and Facebook are so much more powerful than any of the other purely information-centric networks. The vast majority of advertisers on both networks never deal with a human (and if they do, it’s in customer support functionality, not sales and account management): they simply use the self-serve ad products like the one pictured above (or a more comprehensive tool built on the companies’ self-serve API).
This is the level that the other social networks have not reached: Twitter grew revenue, but primarily through its sales team, which meant that costs increased inline with revenue; the company never gained the leverage that comes from having a self-serve ad platform (specifically, the self-serve platform costs are fixed but the revenue is marginal).
Snap is following in Twitter’s footsteps: to date the vast majority of the company’s revenue has come from its sales team; the company has a perfunctory API for self-serve ads, but most of the volume springs from the aforementioned deals made by its sales team. Similar stories can be told about LinkedIn, Yelp, and other advertising-based businesses.
https://stratechery.com/2017/the-super-aggregators-and-the-russians/
The Internet has completely transformed business by making both distribution and transaction costs effectively free. In turn, this has completely changed the calculus when it comes to adding new customers: specifically, it is now possible to build businesses where every incremental customer has both zero marginal costs and zero opportunity costs.
Internet’s impact on Innovator’s Dilemma:
This has profound implications: instead of some companies serving the high end of a market with a superior experience while others serve the low-end with a “good-enough” offering, one company can serve everyone. And, given the choice between a superior experience and one that is “good-enough,” of course the superior experience will win.
To be sure, it takes time to scale such a company, but given the end game of owning the entire market, the rational approach is not to start on the low-end, but rather the exact opposite. After all, while marginal costs may be zero, providing a superior experience in the age of the Internet entails significant upfront (fixed) costs, and while those fixed costs are minimized on a per-customer basis at scale, they can have a significant impact with a small customer base. Therefore, it makes sense to start at the high-end with customers who have a greater willingness-to-pay, and from there scale downwards, decreasing your price along with the decrease in your per-customer cost base (because of scale) as you go (and again, without accruing material marginal costs).
This is exactly what Uber has done: the company spent its early years building its core technology and delivering a high-end experience with significantly higher prices than incumbent taxi companies. Eventually, though, the exact same technology was deployed to deliver an lower-priced experience to a significantly broader customer base; said customer base was brought on board at zero marginal cost
https://stratechery.com/2015/beyond-disruption/
It also had the benefit of being true (until last week). The real estate business in North America has long been an expensive quagmire, for reasons I laid out when Zillow bought Trulia:
While real estate transactions in the aggregate are very frequent, for individual buyers and sellers they are very rare. Thus there is little incentive to push for a simpler solution.A real estate transaction is usually the largest transaction most buyers and sellers will undertake, which makes them very risk averse and unwilling to try an unconventional service.There is a lot of regulation and paperwork associated with a real estate transaction, where assistance is very valuable. And, as just noted, transactions are rare, which means there is little incentive to learn how to deal with said regulations and paperwork on your own.
Combine the reticence of consumers to push for change with the local realtor association-controlled MLSs, and a willingness by realtors to punish anyone changing the status quo (by not showing a house, or pointing out flaws that would kill a sale), and the best outcome for Zillow was to be an aggregator but not an integrator: the company was completely removed from the purchase process.
This gets at why Zillow, for all of its success, seems so underwhelming compared to other Aggregators. One of the key theories underpinning Aggregation Theory is Clayton Christensen’s Conservation of Attractive Profits, which I explored in the context of Netflix while developing the theory:
The Law of Conservation of Attractive Profits
This is the original piece of Aggregation Theory that was missing from last year’s Defining Aggregators: it is one thing to sit on top of an existing industry and, well, be a media company/lead generation tool. There have been a whole host of businesses that did exactly that, and while there is plenty of money to be made, without some sort of integration into the value chain of the industry itself they simply aren’t transformative. To put it another way, aggregation doesn’t transform value chains; integration does.
Why aggregation matters is that it is the means by which new integrations are achieved:
More broadly — and this really gets at why Zillow is different — Aggregators that change industries (including Aggregator-like Amazon and Apple that deal with physical goods) integrate the customer relationship with however it is their industry generates revenue; Zillow, on the other hand, was completely divorced from the home selling-and-buying process.
https://stratechery.com/2018/zillow-aggregation-and-integration/
You might have missed the announcement, because YouTube underplayed it; from their event blog post:
We’re adding updates to brand deals and Shopping to make brand collaborations easier than ever. We’re accelerating these deals through a new initiative and new product features to make sure those partnerships succeed – like the ability to add a link to a brand’s site in Shorts. And YouTube Shopping is expanding to more markets and merchants and getting help from AI to make tagging easier.
It’s just half a sentence — “getting help from AI to make tagging easier” — but the implications of those eight words are profound; here’s how YouTube explained the feature:
We know tagging products can be time-consuming, so to make the experience better for creators, we’re leaning on an AI-powered system to identify the optimal moment a product is mentioned and automatically display the product tag at that time, capturing viewer interest when it’s highest. We’ll also begin testing the ability to automatically identify and tag all eligible products mentioned in your video later this year.
This leads to a third medium-term AI-derived benefit that Meta will enjoy: at some point ads will be indistinguishable from content. You can already see the outlines of that given I’ve discussed both generative ads and generative content; they’re the same thing! That image that is personalized to you just might happen to include a sweater or a belt that Meta knows you probably want; simply click-to-buy.
It’s not just generative content, though: AI can figure out what is in other content, including authentic photos and videos. Suddenly every item in that influencer photo can be labeled and linked — provided the supplier bought into the black box, of course — making not just every piece of generative AI a potential ad, but every piece of content period.
The market implications of this are profound. One of the oddities of analyzing digital ad platforms is that some of the most important indicators are counterintuitive; I wrote this spring:
When I wrote that I was, as I noted in the introduction, feeling more cautious about Meta’s business, given that Reels is built out and the inventory opportunities of Meta AI were not immediately obvious. I realize now, though, that I was distracted by Meta AI: the real impact of AI is to make everything inventory, which is to say that the price-per-ad on Meta will approach $0 for basically forever. Would-be competitors are finding it difficult enough to compete with Meta’s userbase and resources in a probabilisitic world; to do so with basically zero price umbrella seems all-but-impossible.
This analysis was spot-on; I just pointed it at the wrong company. This opportunity to leverage AI to make basically every pixel monetizable absolutely exists for Meta; Meta, however, has to actually develop the models and infrastructure to do it at scale. Google is already there; it was the company universally decried for being slow-moving that announced the first version of this feature last week.
I can’t overstate what a massive opportunity this is: every item in every YouTube video is well on its way to being a monetizable surface. Yes, that may sound dystopian when I put it so baldly, but if you think about it you can see the benefits; I’ve been watching a lot of home improvement videos lately, and it sure would be useful to be able to not just identify but helpfully have a link to buy a lot of the equipment I see, much of which is basically in the background because it’s not the point of the video. It won’t be long until YouTube has that inventory, which it could surface with an affiliate fee link, or make biddable for companies who want to reach primed customers.
https://stratechery.com/2025/the-youtube-tip-of-the-google-spear/
The appeal of the marketplace model is its asset-light scalability. Aggregators don’t need to own inventory or stores because they tap into existing grocers’ shelves as virtual warehouses. This allowed Instacart and others to rapidly scale across thousands of stores and cities, essentially piggybacking on retailers’ infrastructure. It’s a win for consumers in terms of convenience and selection (multiple grocer options in one app) and for smaller retailers who instantly get an online channel without heavy investment.
However, the trade-offs are significant. Because the aggregator is a middleman taking a slice of the transaction, margins are slim and unit economics are challenging until scale is achieved. Instacart’s commission (roughly a 6% take-rate, about $7 on a $110 order) is only about half the percentage that food delivery apps like DoorDash take for restaurant orders. Grocery baskets are larger, so that 6% still yields comparable dollars per order, but the operations cost is high. The retailer keeps the majority (~85% of the sale) to cover the product cost and store costs, meaning both the store and the platform are splitting an already thin retail margin. Aggregators like Instacart have found that massive order volume and density are required to turn a profit in this model – CEO Fidji Simo noted they “used to lose money on every order until we got to 100 million orders” delivered, underscoring that scale is everything.
Over time, the major aggregators have started to improve economics by layering on high-margin revenue streams like advertising:
https://www.platformaeronaut.com/p/monetizing-meals-advertising-ecosystems?triedRedirect=true
https://x.com/modestproposal1/status/1072216004548313089
Unit Economics by Fulfillment Model
Store-Pick Marketplace
The cheapest to set up (uses existing stores, no new capex), but the most expensive per order in labor. A gig shopper or store employee walking aisles might cost $7–10 per order in picking time, plus delivery costs of $6–10. It works at low volumes and enables rapid market entry, but margins are thin unless orders are batched. Grocery orders picked this way often leave the retailer breakeven at best, especially once fees to platforms like Instacart are included.
Centralized Warehouse / CFC
High upfront cost (tens to hundreds of millions per facility), but lower variable cost per order once scaled. Robots pick items more cheaply than humans, and delivery routes can be batched with dozens of orders per truck. A fully utilized CFC might bring fulfillment+delivery cost down to ~$8–12 per order vs. $15–20 for in-store pick, but if volume is low the overhead crushes economics. Better margin potential (target 3–5%) but only viable at large scale.
Micro-Fulfillment / Dark Stores (MFCs, DashMart, GoPuff)
A middle ground: small dedicated warehouses near customers with a curated assortment. Lower labor cost than store-pick (pickers don’t navigate crowded aisles), but higher overhead than using existing stores. Delivery costs remain high because baskets are smaller (often <$40) and couriers run orders one by one. Unless order density is very high, the unit economics are negative. When density is achieved, fulfillment cost per order can fall to ~$5–7, but delivery cost still dominates.
https://www.platformaeronaut.com/p/amazons-latest-grocery-push-mapping
The Law of Conservation of Attractive Profits
When you think about it that way — that Netflix isn’t so much a network as they are a type of marketplace in which consumers can give their attention to creators — it becomes apparent that Netflix isn’t that far off from Uber or Airbnb or any of the other market-makers that are transforming industry-after-industry. Netflix:
I made this an ordered list on purpose: these three characteristics work in concert to create value due to something called the Law of Conservation of Attractive Profits,
first explained by Clayton Christensen in his 2003 book
:
Formally, the law of conservation of attractive profits states that in the value chain there is a requisite juxtaposition of modular and interdependent architectures, and of reciprocal processes of commoditization and de-commoditization, that exists in order to optimize the performance of what is not good enough. The law states that when modularity and commoditization cause attractive profits to disappear at one stage in the value chain, the opportunity to earn attractive profits with proprietary products will usually emerge at an adjacent stage.
That’s a bit of a mouthful, but the example that follows in the book shows how powerful this observation is:
If you think about it in a hardware context, because historically the microprocessor had not been good enough, then its architecture inside was proprietary and optimized and that meant that the computer’s architecture had to be modular and conformable to allow the microprocessor to be optimized. But in a little hand held device like the RIM BlackBerry, it’s the device itself that’s not good enough, and you therefore cannot have a one-size-fits-all Intel processor inside of a BlackBerry, but instead, the processor itself has to be modular and conformable so that it has on it only the functionality that the BlackBerry needs and none of the functionality that it doesn’t need. So again, one side or the other needs to be modular and conformable to optimize what’s not good enough.
Did you catch that? That was Christensen, a full four years before the iPhone, explaining why it was that Intel was doomed in mobile even as ARM would become ascendent.
When the basis of competition changed away from pure processor performance to a low-power
system
the chip architecture needed to switch from being integrated (Intel) to being modular (ARM), the latter enabling an integrated BlackBerry then, and an integrated iPhone four years later.
The PC is a modular system whose integrated parts earn all the profit. Blackberry (and later iPhones) on the other hand was an integrated system that used modular pieces. Do note that this is a drastically simplified illustration.
More broadly, breaking up a formerly integrated system — commoditizing and modularizing it — destroys incumbent value while simultaneously allowing a new entrant to integrate a different part of the value chain and thus capture new value.
Commoditizing an incumbent’s integration allows a new entrant to create new integrations — and profit — elsewhere in the value chain.
This is exactly what is happening with Airbnb, Uber, and Netflix too.
As noted above, I discussed Airbnb last week: the service commoditized trust, divorcing it from the underlying physical property. That freed Airbnb to integrate trust into a worldwide network of hosts and guests:
Airbnb modularizes property allowing it to integrate trust and reservations.
Uber has a trust element as well (as do nearly all the sharing companies), but even more important was how the service commoditized dispatch and modularized cars:
Uber modularizes cars allowing it to integrate dispatch with hailing and payments.
Netflix has pulled a similar maneuver: by commoditizing time and distribution the company has integrated production and customer management:
Netflix modularizes content production, allowing it to integrate productions with subscriptions and distribution.
(See this follow-up about how I would re-label this chart)
Note the common element to all three of these companies: all have managed to modularize the production/delivery of their service which has allowed them to move closer to the customer. To put it another way, all of this new value is being created by specialized CRM companies: Airbnb for travelers, Uber for commuters, and Netflix for the bored.
https://stratechery.com/2015/netflix-and-the-conservation-of-attractive-profits/