Is Uber's Share of Ride Revenue Unfair?

It is easy enough to get an argument about the cost of using a marketplace such as the Apple App Store, Etsy or Uber. When Apple takes 15 percent to 30 percent of a sale, it can seem unfair to the seller. Uber’s take, said to be up to 50 percent in some cases, can seem usurious. 

But distribution, the roles in a value chain that move products or services from manufacturer to buyer, have a definite cost. 

And for some of us, a market maker or marketplace represents the cost of distribution. Seen that way, perhaps 30 percent is not unreasonable. 

Market makers, platforms and distributors all move goods between buyers and sellers. 

Function	Traditional Distributor	Marketplace / Market Maker (Uber, eBay, Etsy)
Takes ownership of product?	Usually yes	Usually no
Holds inventory?	Yes	No
Warehousing/logistics?	Major function	Usually little or none
Sets resale price?	Often yes	Usually seller sets price
Bears inventory risk?	Yes	No
Main asset	Physical network	Digital network
Revenue model	Gross margin on resale	Commission ("take rate")
Value provided	Physical distribution	Matching buyers and sellers
Scalability	Limited by physical assets	Highly scalable

A traditional distributor such as Sysco or McKesson buys products, stores them, transports them, and resells them.

A platform such as Uber, eBay, or Etsy generally does not own the underlying goods or services. Instead it creates a market, establishes trust, handles payments, provides discovery, and charges a fee.

The point is that platforms, market makers and distribution networks provide a similar function. 

From a value-chain perspective, both perform an intermediation function:

• "How do products get from factory to customer?"

• "How do buyers find sellers and transact safely?"

Both reduce search costs, transaction costs, and coordination costs.

The major innovation of digital marketplaces is that they perform many distribution functions without taking inventory ownership.

One could argue that Uber is a "virtual distributor" of transportation services, while eBay and Etsy are "virtual distributors" of goods.

So distribution represents a necessary part of any retail value chain. 

Research from the Reserve Bank of Australia found that for retail goods, roughly half of the final retail price reflects wholesale and retail distribution margins and costs. 

But distributor profits themselves were less than 10 percent of final sale price.

Final Retail Price = $100	Share
Manufacturing cost	$50
Wholesale distribution costs and margin	$15
Retail costs and margin	$35
Final consumer price	$100

Industry	Distributor Margin
Electronics	3%–10%
FMCG/Grocery	3%–10%
Industrial products	10%–20%
Medical products	20%–30%+
Apparel	15%–30%
source: Unanswered

Digital marketplaces usually charge commissions ("take rates") rather than earning resale margins.

Platform	Approximate Take Rate
Etsy	~6.5% transaction fee plus payment and advertising fees; effective cost often 10%-20%+ for sellers (Reddit)
eBay	Often around 10%-15% depending on category (Business Insider)
Uber	Company reports roughly 20%+ take rates, though some external studies estimate substantially higher in certain markets (Business Insider)

Looked at that way, take rates or distribution costs are quite similar. 

Role	Typical Share of Final Transaction Value
Physical distributor profit	3%-30%
Marketplace take rate	10%-30%
Combined wholesale + retail distribution system	Often 30%-50%+
Uber/eBay/Etsy platform fee	Often 10%-30% (50% in some cases for Uber)

Industrial Products	Digital Era Products
Scarcity = moving products	Scarcity = matching participants
Value = logistics	Value = network effects
Advantage = warehouses	Advantage = users and data

In economic terms, platforms replace physical intermediation with information intermediation.

So we move from:

Manufacturer → Distributor → Retailer → Consumer

to:

Producer → Marketplace → Consumer. 

As a result, a 10 percent to 20 percent marketplace fee can sometimes replace a traditional channel structure that consumed 30 percent to 50 percent of the final selling price.

source: Len Sherman 

In other words, a 30-percent take rate for sellers on a platform or marketplace might seem out of line, but might not actually be usurious. 

Measurable AI Returns; Technology J-Curve: Big Disconnect

Amara's Law suggests we will overestimate the immediate impact of artificial intelligence but also underestimate the long-term impact. 

And that is going to be a problem for financial analysts and observers who demand an immediate boost in observable firm earnings or revenue, as well as the firms deploying AI that will strive to demonstrate the benefit. 

“Most people overestimate what they can achieve in a year and underestimate what they can achieve in ten years” is a quote whose provenance is unknown, though some attribute it to Standord computer scientist Roy Amara and some people call it “Gate’s Law.”

In fact, decades might pass before the fullest impact is measurable, even if some tangible results are already seen. 

Error rates in labeling the content of photos on ImageNet, a collection of more than 10 million images, have fallen from over 30 percent in 2010 to less than five percent in 2016 and most recently as low as 2.2 percent, according to Erik Brynjolfsson, MIT Sloan School of Management professor.

Likewise, error rates in voice recognition on the Switchboard speech recording corpus, often used to measure progress in speech recognition, have improved from 8.5 percent to 5.5 percent over the past year. The five-percent threshold is important because that is roughly the performance of humans at each of these tasks, Brynjolfsson says. 

A system using deep neural networks was tested against 21 board certified dermatologists and matched their performance in diagnosing skin cancer, a development with direct implications for medical diagnosis using AI systems.

Codified or understood as Amara's Law, the principle is that it generally takes entities some time to reorganize business processes in ways that enable wringing productive results from important new technologies. 

Source

It also can take decades before a successful innovation actually reaches commercialization. The next big thing will have first been talked about roughly 30 years ago, says technologist Greg Satell. IBM coined the term machine learning in 1959, for example, and machine learning is only now in use. 

Many times, reaping the full benefits of a major new technology can take 20 to 30 years. Alexander Fleming discovered penicillin in 1928, it didn’t arrive on the market until 1945, nearly 20 years later.

Electricity did not have a measurable impact on the economy until the early 1920s, 40 years after Edison’s plant, it can be argued.

It wasn’t until the late 1990’s, or about 30 years after 1968, that computers had a measurable effect on the US economy, many would note.

Likewise, economic historians such as Erik Brynjolfsson and Paul David have documented that transformative, general-purpose technologies tend to follow the J-curve pattern. 

Initial deployment generates negative or flat productivity returns relative to investment, often for a surprisingly long time. 

David's famous 1990 paper on the "dynamo paradox" showed that electrification of US industry began in earnest in the 1880s but didn't produce measurable aggregate productivity gains until the 1920s.

The reasons are structural: firms must reorganize workflows, retrain workers, build complementary infrastructure, and abandon legacy processes before the technology's benefits materialize. 

The productivity gains, when they finally arrive, are real and large, but they accrue after enormous sunk costs and a long gestation period.

source 

Maybe AI really will prove different. But there is ample evidence that quantifying impact could be difficult in the near term. Buckle up. 

Telco AI Monetization on the Revenue Front Will be Difficult

Mobile executives these days are talking about ways to monetize artificial intelligence beyond using AI to streamline internal operations. Generally speaking, these fall into three buckets:

• Personalizing existing services to drive higher revenue, acquisition and retention (quality of service tiers of service, for example)

• Creating enterprise or business services (private 5G networks with AI-optimized performance,, for example)

• AI edge computing services for autonomous vehicles, for example

Obviously, those are AI-enhanced extensions of ideas already in currency. But some of us might be quite skeptical that such “AI services” owned by telcos will get much traction. History suggests the difficulty of doing so. How many “at scale” new products beyond voice have telcos managed to create? Text messaging comes to mind. Mobile phone service also was a big success. So is home broadband. 

All those share a common characteristic: they are network services owned directly by the service providers. Generally speaking, other application efforts have not scaled well. 

Mobile service providers have been hoping and proclaiming such new revenue opportunities since at least the time of 3G. But many observers might agree there has been a disconnect between the technical leaps (faster speeds, lower latency, better efficiency) and the ability to turn those into new revenue streams beyond the basic "sell more data" model. 

That is not to say that service providers have had no other ways to add value. Bundling devices, content and other measures have helped increase perceived value beyond the core network features. 

But the core network as a driver of new products and revenue is challenging for a few reasons. 

• Open networks mostly have replaced closed networks (IP versus PSTN) 

• Applications are logically separate from network transport (layers)

• Permissionless app development is the norm (internet is the assumed network transport)

• Vertical control of the value replaced by horizontal functions (telcos had full-stack control of voice, but only horizontal transport functions for IP-based apps)

As I have argued in the past, modern telcos have a hybrid revenue model. They are full-stack “service” providers for voice and text messaging. But they are horizontal transport providers for most IP apps and services, and sometimes are app providers (owned entertainment video services, for example). 

The point is that most new apps and revenue cases can be built by third parties without telco or mobile operator permission, which also takes transport providers out of the direct revenue chain. 

So I’d argue there is a structural reason why telcos and mobile service providers do not directly benefit from most of the innovation that happens with apps. Think about all the customer engagement with internet-delivered apps and services, compared to service provider voice and messaging. 

In their role as voice and text messaging providers, telcos are “service providers” (they own and control the full stack). For the rest of their business, they are transport or access providers (capacity or internet access such as home broadband), a horizontal value and revenue stream. ISPs get paid to provide “internet access,” not the actual end user apps. 

And that has proven a business challenge for now-obvious reasons. Once upon a time, voice services were partly flat-rate and partly usage-based. In other words, telcos earned money by charging a flat fee for access to the network, and then variable usage based on number, length or distance of voice calls. 

In other words, greater usage meant greater revenue. But flat-rate voice and texting usage subverts the business model, as  most of the revenue-generating services become usage-insensitive. That is the real revolution or disruption for voice and texting. 

In their roles as internet access providers, some efforts have been made to sustain usage-based pricing. Customers can buy “buckets of usage” where there is some relationship between revenue and usage. 

Likewise, fixed network providers have used “speed-based” tiers of service, where higher speeds carry  higher prices. Still, those are largely flat-rate approaches to packaging and pricing. And the long-term issue with flat-rate pricing is that it complicates investment, as potential usage of the network is capped but usage is not.  

So as much as ISPs hate the notion that they are “dumb pipes,” that is precisely what home or business broadband access is. So internet access take rates, subscription volumes and prices are going to drive overall business results, not text messaging, voice or IoT revenues. 

To be sure, we can say that 5G is the first mobile generation that was specifically designed to support internet of things applications, devices and use cases. But that only means the capability to act as a platform for open development and ownership of IoT apps, services and value. And even if some mobile service providers have created app businesses such as auto-related services, that remains a small revenue stream for mobile service providers.  

Recall that IoT services are primarily driven by enterprises and businesses, not consumers. Also, the bulk of enterprise IoT revenue arguably comes from wholesale access connections made available to third-party app or service providers, and does not represent telco-owned apps and services (full stack rather than “access services”). 

Optimistic estimates of telco enterprise IoT revenues might range up to 18 percent, in some cases, though most would consider those ranges too high. 

Region/Group	Total Mobile Services Revenue	IoT Connectivity Revenue (Enterprises)	Automotive IoT Apps Share of IoT Revenue	% of Total Revenue from Automotive IoT Apps
Global Average	$1.5 trillion (2025 est.)	10-15% (2025, growing to 20% by 2027)	25-35%	2.5-5.25%
North America (e.g., Verizon)	$468 billion (U.S., 2023, growing 6.6% CAGR)	12-18% (2025 est.)	30-40% (high 5G adoption)	3.6-7.2%
Asia-Pacific (e.g., China Mobile)	$600 billion (2025 est.)	15-20% (strong automotive industry)	35-45% (leader in connected cars)	5.25-9%
Europe (e.g., Deutsche Telekom)	$400 billion (2025 est.)	10-15% (CEE high IoT reliance)	25-35%	2.5-5.25%
Top 10 Mobile Operators	$1 trillion (2025 est.)	12-18% (based on 2.9B IoT connections)	30-40%	3.6-7.2%

Though automotive IoT revenues (again mostly driven by access services) arguably are higher for the largest service providers, their contribution to  total business revenues is arguably close to three percent or so, and so arguably contributing no more than 1.5 percent of total revenues, as consumer services range from 44 percent to 65 percent of total mobile service provider revenues. 

Category	Percentage of Total Revenue	Example products
Services to Consumers	55-65%	Driven by mobile data (33.5% in 2023), voice, and equipment sales; 58% in 2023
Services to Businesses	35-45%	Includes enterprise, public sector, and SMBs; growing at 7.1% CAGR
Business Voice	5-10%	Declining due to VoIP adoption and mobile data preference
Business Internet Access	15-25%	Rising with 5G, IoT (e.g., automotive apps at 2.5-9%), and enterprise demand

The point is that the ability to monetize AI beyond its use for internal automation is likely limited. Changes in the main revenue drivers (consumer and business mobile phone subscriptions and prices) are going to have more impact on revenue and profit outcomes than IoT as a category or automotive IoT in particular.