Is It the "Year of X"?

It’s that time of year when some feel compelled to prognosticate on “what will happen next year,” while others remind us of what did happen “last year.” And there always are a brave few who will try to capture the essence in a single phrase: “the year of X,” whatever X is said to be.

At a high level, we might well look back at such highly-distilled “year of X” predictions and note that it almost never happens. “The year of X,” whatever X is said to be, nearly always occurs (in the sense of commercial adoption or inflection point of adoption) some future year.

My simple way of describing this situation is to say that “whatever is said to be the ‘year of X’ trend almost ensures it will not be.” Of course, some will argue that is not what they mean.

Instead, they tend to mean this is the year some trend is popularized or discovered. Okay, in that sense, there is firmer--yet still tenuous--ground to stand on. Rarely does a big new thing just burst on the scene, in terms of public awareness, in a decisively-new way,

What does happen is that some arbiter “proclaims” that this has happened. It’s arbitrary.

The point is that any truly-significant new technology, platform or commercial activity takes quite some time to reach commercialization, and typically quite long after all the hype has been crushed by disillusionment.

source: Gartner

The point is that even highly-successful new technologies can take decades to reach commercial ubiquity, even if today’s software-driven products are adopted faster than innovations of the past.

It still can take a decade for widespread consumer use of any product or service to reach 50 percent to 60 percent adoption.

source: Harvard Business Review

Also, recall that most new products, and most new companies fail: they simply never succeed as commercial realities. Also, we sometimes overestimate the actual time any innovation takes to reach 10 percent or some other level of adoption on a mass level.

There is debate about how fast smartphones were adopted for example. Was it seven years or something greater than a decade for usage to reach half of consumers? Some estimate it took just seven years. Others have argued adoption never reached 50 percent after a decade.

And depending on how one defines “smartphone,” adoption levels of 50 percent took a couple of decades to nearly three decades.

source: Xapp Media

source: MIT Technology Review

For all such reasons, some of us tend to discount the notion of a “year of X.” Truly-significant innovations which achieve mass usage often take longer than expected to reach mass adoption levels. On the other hand, there arguably are points in time when public awareness seems to reach something like an inflection point.

In most cases it is difficult to measure the actual year when a shift becomes significant. Is it the point where 10 percent of people recognize a term, or say it is important? Or when 20 percent, 30 percent or 40 percent say so?

More significantly, at what point of innovation purchase or regular usage has something “arrived,” in a commercial sense?

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. 

Advanced Technology Takes Longer Than You Think to Become Mainstream

Advanced technology often does not get adopted as rapidly as the hype would have you believe. In fact, most useful advanced technologies tend not to go mainstream until adoption reaches about 10 percent. That is where the inflection point tends to occur. That essentially represents adoption by innovators and early adopters. 

source: LikeFolio

One often sees charts that suggest popular and important technology innovations are adopted quite quickly. That is almost always an exaggeration. The issue is where to start the clock running: at the point of invention or at the point of commercial introduction? Starting from invention, adoption takes quite some time to reach 10 percent adoption, even if it later seems as though it happened faster. 

source: Researchgate

Consider mobile phone use. On a global basis, it took more than 20 years for usage to reach close to 10 percent of people. 

source: Quora

That is worth keeping in mind when thinking about, or trying to predict, advanced technology adoption. It usually takes longer than one believes for any important and useful innovation to reach 10-percent adoption. 

source: MIT Technology Review

That is why some might argue 5G will hit an inflection point when about 10 percent of customers in any market have adopted it.

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.