[SIGCIS-Members] Moore’s Not Enough: ​4 New Laws of Computing

[Joly MacFie]

[I thought this worthy of circulation - Joly]

https://spectrum.ieee.org/on-beyond-moores-law-4-new-laws-of-computing

*Moore’s Not Enough: 4 New Laws of Computing *
*Moore’s and Metcalfe’s conjectures are taught in classrooms every
day—these four deserve consideration, too *

*ADENEKAN DEDEKE *
*04 FEB 2022*

I teach technology and information-systems courses at Northeastern
University, <https://damore-mckim.northeastern.edu/people/adenekan-dedeke/>
in Boston. The two most popular laws that we teach there—and, one presumes,
in most other academic departments that offer these subjects—are Moore’s
Law and Metcalfe’s Law. Moore’s Law, as everyone by now knows, predicts
that the number of transistors on a chip will double every two years. One
of the practical values of Intel cofounder Gordon Moore’s legendary law
<https://en.wikipedia.org/wiki/Moore%27s_law> is that it enables managers
and professionals to determine how long they should keep their computers.
It also helps software developers to anticipate, broadly speaking, how much
bigger their software releases should be.

Metcalfe’s Law is similar to Moore’s Law in that it also enables one to
predict the direction of growth for a phenomenon. Based on the observations
and analysis of Robert Metcalfe
<https://en.wikipedia.org/wiki/Robert_Metcalfe/>, co-inventor of the
Ethernet and pioneering innovator in the early days of the Internet, he
postulated that the value of a network would grow proportionately to the
number of its users squared. A limitation of this law is that a network’s
value is difficult to quantify. Furthermore, it is unclear that the growth
rate of every network value changes quadratically at the power of two.
Nevertheless, this law as well as Moore’s Law remain a centerpiece in both
the IT industry and academic computer-science research. Both provide
tremendous power to explain and predict behaviors of some seemingly
incomprehensible systems and phenomena in the sometimes inscrutable
information-technology world.

I contend, moreover, that there are still other regularities in the field
of computing that could also be formulated in a fashion similar to that of
Moore’s and Metcalfe’s relationships. I would like to propose four such
laws.

*Law 1. Yule’s Law of Complementarity*

I named this law after George Udny Yule
<https://en.wikipedia.org/wiki/Udny_Yule> (1912), who was the statistician
who proposed the seminal equation
<https://en.wikipedia.org/wiki/Yule%E2%80%93Simon_distribution> for
explaining the relationship between two attributes. I formulate this law as
follows:

*If two attributes or products are complements, the value/demand of one of
> the complements will be inversely related to the price of the other
> complement.*


In other words, if the price of one complement is reduced, the demand for
the other will increase. There are a few historical examples of this law.
One of the famous ones is the marketing of razor blades. The legendary King
Camp Gillette <https://en.wikipedia.org/wiki/King_C._Gillette> gained
market domination by applying this rule. He reduced the price of the
razors, and the demand for razor blades increased. The history of IT
contains numerous examples of this phenomenon, too.

The case of Atari 2600 is one notable example. Atari video games consisted
of the console system hardware and the read-only memory cartridges that
contained a game’s software. When the product was released, Atari Inc.
marketed three products, namely the Atari Video Computer System
<https://spectrum.ieee.org/atari-2600> (VCS) hardware and the two games
that it had created, the arcade shooter game *Jet Fighter* and *Tank*, a
heavy-artillery combat title involving, not surprisingly, tanks.

Crucially, Atari engineers decided that they would use a microchip for the
VCS instead of a custom chip. They also made sure that any programmer
hoping to create a new game for the VCS would be able to access and use all
the inner workings of the system’s hardware. And that was exactly what
happened. In other words, the designers reduced the barriers and the cost
necessary for other players to develop VCS game cartridges. More than 200
such games have since been developed for the VCS—helping to spawn the
sprawling US $170 billion global video game industry today
<https://www.mordorintelligence.com/industry-reports/global-gaming-market>.

A similar law of complementarity exists with computer printers. The more
affordable the price of a printer is kept, the higher the demand for that
printer’s ink cartridges. Managing complementary components well was also
crucial to Apple’s winning the MP3 player wars of the early 2000s
<https://books.google.com/books?id=tGUYDQAAQBAJ&q=ipod#v=snippet&q=ipod&f=false>,
with its now-iconic iPod.

>From a strategic point of view, technology firms ultimately need to know
which complementary element of their product to sell at a low price—and
which complement to sell at a higher price. And, as the economist Bharat
Anand points out in his celebrated 2016 book
<https://books.google.com/books?id=tGUYDQAAQBAJ&vq=ipod&source=gbs_navlinks_s>*The
Content Tra
<https://books.google.com/books?id=tGUYDQAAQBAJ&vq=ipod&source=gbs_navlinks_s>p*,
proprietary complements tend to be more profitable than nonproprietary ones.

*Law 2. Hoff’s Law of Scalability*

This law is named after Marcian Edward (Ted) Hoff Jr.—the engineer who
convinced the CEO of Intel to apply the law of scalability to the design
and development of processors. Certainly, the phenomenon of scalability was
well known in the automobile industry before it made a significant impact
on the computing industry. Henry Ford was a notable example of the
application of this scalability law. Henry Ford’s company was perhaps the
first company to apply this law on a grand scale. Ford produced the Model
T, which was the first mass-produced car. At the core of Henry Ford’s
achievement was the design of an automobile that was made for mass
production. Ford’s engineers broke down the assembly process of the Model T
into 84 discrete steps
<https://www.mlive.com/news/detroit/2018/01/historic_photos_fords_assembly.html#:~:text=Ford%20was%20able%20to%20take,half%20of%20all%20automobiles%20sold.>.
The company standardized all the tasks and assigned each worker to do just
one task, thus standardizing the work each worker performed as well. Ford
further built machines that could stamp out parts automatically. Together
with Ford’s innovative development of the first moving assembly line, this
production system cut the time to build a car from 12 hours to about 1.5
hours
<https://www.history.com/this-day-in-history/fords-assembly-line-starts-rolling>.
The Model T is probably the paradigmatic example of how standardization
enables designing processes for scalability.

Intel also mastered the law of scalability early in its history. In 1969,
Busicom, a Japanese company, approached Intel about building custom chips
<https://spectrum.ieee.org/chip-hall-of-fame-intel-4004-microprocessor> for
use in its programmable computers. Gordon Moore was not interested in a
custom chip because he knew that it would not be scalable. It was the quest
to create a scalable product that led Intel’s Ted Hoff to partition the
chip into a general-purpose logic processor chip and a separate read-only
memory (ROM) chip that stored an application program. As Albert Yu shows in
his history of Intel, *Creating the Digital Future
<https://books.google.com/books/about/Creating_the_Digital_Future.html?id=AYa1AAAAIAAJ>*,
the fledgling semiconductor company’s general-purpose processor, the 4004,
was scalable and pretty much bequeathed the world the hardware architecture
of the modern computer. And it was Hoff who redesigned the 4004 to scale.
Hoff’s Law of Scalability could thus be described as follows:

*The potential for scalability of a technology product is inversely
proportional to its degree of customization and directly proportional to
its degree of standardization.*

In sum, the law predicts that a technology component or process that has a
high degree of customization and/or a lower degree of standardization will
be a poor candidate for scaling.

*Law 3. Evans’s Law of Modularity*

This law derives its name from Bob Overton Evans
<https://en.wikipedia.org/wiki/Bob_O._Evans>. He was the engineer who in
the early 1960s persuaded IBM’s chairman, Thomas J. Watson Jr
<https://en.wikipedia.org/wiki/Thomas_J._Watson>., to discontinue IBM’s
technology design approach, which had produced a hodgepodge of incompatible
computers. Evans advocated that IBM should instead embark on the
development of a family of modular computers that would share peripheries,
instructions, and common interfaces. IBM’s first product family under this
new design rubric was called System/360
<https://en.wikipedia.org/wiki/IBM_System/360>.

Prior to this era, IBM and other mainframe computer manufacturers produced
systems that were unique. Each system had its own distinct operating
system, processor, peripherals, and application software. After the
purchase of a new IBM computer, customers had to rewrite all their existing
code. Evans convinced CEO Watson that a line of computers should be
designed to share many of the same instructions and interfaces.

This new approach of modular design meant that IBM’s engineers developed a
common architecture (the specification of which functions and modules will
be part of the system), common interfaces (a description of how the modules
will interact, fit together, connect, and communicate), and common
standards (a definition of shared rules and methods that would be used to
achieve common functions and tasks). This bold move on Big Blue’s part
created a new family of computers that revolutionized the computer
industry. Customers could now protect their investments because the
instructions, software, and peripheries were reusable and compatible within
each computer family.

Evans’s Law could be formulated as follows:

*The inflexibilities, incompatibilities, and rigidities of complex and/or
> monolithically structured technologies could be simplified by the
> modularization of the technology structures (and processes).*


This law predicts that the application of modularization will reduce
incompatibilities and complexities.

One further example of Evans’s Law can be seen in the software development
industry, as it has shifted from the “waterfall” to the agile software
development methodology
<https://www.forbes.com/advisor/business/agile-vs-waterfall-methodology/>.
The former is a linear and sequential model stipulating that each project
phase can begin only the previous phase has ended. (The name comes from the
fact that water flows in only one direction down a waterfall.) By contrast,
the agile development approach applies the law of modularization to
software design and the software development process. Agile software
developments tend to be more flexible, more responsive, and faster.

In other words, modularization of software projects and the development
process makes such endeavors more efficient. As outlined in a helpful
2016 *Harvard
Business Review* <https://hbr.org/2016/05/embracing-agile> article, the
preconditions for an agile methodology are as follows: The problem to be
solved is complex; the solutions are initially unknown, with product
requirements evolving; the work can be modularized; and close collaboration
with end users is feasible.

*Law 4. The Law of Digitiplication*

The concept of digitiplication is derived from two concepts: digitalization
and multiplication. The law stems from my own study and observations of
what happens when a resource is digitized or a process is digitalized.

*The law of digitiplication stipulates that whenever a resource or process
> is digitalized, its potential value grows in a multiplicative manner.*


For example, if a paper is copied four times, one can now share the
resource with five people. But digitize the document and the value-creation
opportunities are multiplicative rather than additive.

Consider the example of a retail store. The store’s sales reps, tasked with
selling physical products to individual people, are able to service only
one customer at a time. However, if the same retail environment is placed
online, many customers can view the store’s products and services. Digital
text can also easily be transformed into an audio format, providing a
different kind of value to customers. Search functionality within the
store’s inventory of course adds another layer of value to the customer.
The store’s managers can also monitor how many customers are viewing the
store’s website’s pages and for how long. All of these enhancements to the
customer’s (and retailer’s) experience provide different kinds of value. As
can be seen by these examples, the digitalization of a resource, asset, or
process creates multiplicative rather than additive value.

As a further example, Amazon founder Jeff Bezos first began digitizing data
about books as a way to facilitate more and greater book sales online.
Bezos quickly transformed Amazon into a digitiplication engine by becoming
a data-centric e-commerce company. The company now benefits from the
multiplicative effects of digitalized processes and digitized information.
Amazon’s search, selection, and purchase functions also allow the company
to record and produce data that can be leveraged to predict what the
customer wants to buy—and thus select which products it should show to
customers. The digitization of customer feedback, seller ratings, and
seller feedback creates its own dimension of multiplicative value.

Conclusion

These four laws can be useful for engineers and designers to pose questions
as they begin to develop a product. For example: Do customer requirements
lend themselves to a product design that could be scaled (or
mass-produced)? Might the functional requirements they’re working with be
satisfied through the development of a modular product design? Could Yule’s
Law of Complementarity provide cues toward mass production or modular
design alternatives? Could product complements be developed in-house or
outsourced? Software engineers might also be led toward productive
questions about how data could be digitized, or how specific processes
could be digitalized to leverage the law of digitiplication.

The fields of IT and electrical engineering and computer science (EECS)
have become critical disciplines of the digital age. To pass along the most
succinct and relevant formulations of accumulated knowledge to date to the
next generation, it's incumbent on academics and thought leaders in these
essential technical fields to translate lessons learned into more
formalized sets of theorems and laws. Such formulations would, I hope,
enable current and future generations of IT and EECS professionals to
develop the most useful, relevant, impactful, and indeed sometimes even
disruptive technologies. I hope that the proposed four laws in this article
could help to trigger a larger discussion about the need for and relevance
of new laws for our disciplines.


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Joly MacFie  +12185659365
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