MINDSET FOR SCALE

Thinking Big, Thinking Exponential

Part 1: Defining exponential organizations

This is part 1 of a 2 part series on how we have been creating exponential disruptions across history, the driving forces behind them, their implications, and what we can learn and apply to our present and future organizations and societies.

Introduction

Technological disruption is at the forefront of shaping our societies by improving the quality of life and democratizing access to resources. For example, since the mid-1700s, we have managed to increase the average life expectancy of humans by 2.3X, with nearly most of that improvement happening in the last 100–120 years.

Average global life expectancy from 1700 to 2020
Average global life expectancy (AGLE) from 1700 to 2020 — shown is the period of life expectancy at birth, the average number of years a newborn would live if the pattern of mortality in the given year were to stay the same throughout its life. Between 1700 to the early 1900s, AGLE only grew by 12% while over the last century, it grew by +2X — Source(s): Riley (2005), Clio Infra (2015), and UN Population Division (2019)

In the 19th and 20th centuries, social and scientific advancements enabled by the second industrial revolution helped improve sanitation conditions, public water treatment, sewage management, food inspection, garbage collection, nutrition, housing conditions, air quality, literacy rates, diminish child labor, and introduce antibiotics and vaccines. As a result and as seen in the graph above, since the early 1900s, human life expectancy has improved exponentially.

Today, the growth in life expectancy is still positive and is improving year over year, potentially waiting for another wave of massive social, scientific, and technological breakthroughs to help it further improve.

Average global life expectancy growth rate from 1700 to 2020—the most acceleration in life expectancy improvement was observed in the 1950s largely due to massively reduced infancy death rates and potentially a change in data collection processes — Source(s): Riley (2005), Clio Infra (2015), and UN Population Division (2019)
Share of children surviving the first 5 years of their lives — the largest acceleration in the rate was observed in the first half of the 20th century — Source(s): Our World in Data, Gapminder, and the World Bank — note: historical figures before the 1950s are estimations — further data found at ChildMortality

Another example is communication and transportation costs. Relative to the 1930s, we have decimated the costs of international phone calls (i.e. voice communication), reduced passenger air transport costs by ~90% (i.e. geographic reach), and decreased sea freight costs by ~80% (i.e. transportation).

We collectively strive every day to help our societies move from scarcity to abundance by reducing the cost of access to resources

The decline of transport and telephony communication costs relative to 1930
Sea freight corresponds to average international freight charges per tonne. Passenger air transport
corresponds to average airline revenue per passenger mile until 2000 spliced to US import air passenger fares
afterward. International calls correspond to the cost of a three-minute call from New York to London — Source(s): Transaction Costs — OECD Economic Outlook (2007)

Exponential disruptions before the digital era

15th century: ‘THE’ Gutenberg moment

Historians argue that Johannes Gutenberg’s Printing Press is one of the most revolutionary inventions in human history, and rightfully so as it reduced the cost of spreading knowledge and ideas like never before, democratizing access to written scripture, and knowledge.

The Movable Type Printing Machine was invented and commercialized in 1450 by Johannes Gutenberg and his business partners in Mainz, Germany as a for-profit enterprise. Before 1450, books were generally written by hand, which was a slow and expensive process to produce and distribute content.

The Printing Machine had previously been invented and used in China, however, due to 1000s of unique characters in the language, it did not mass commercialize. Having metal molds helped Gutenberg’s machine to have durability and uniformity in prints which led to typography and fonts. Gutenberg kept the manufacturing of the molds as a trade secret for a century and made large profits for his shareholders with this monopoly.

In under 50 years after the introduction of the Printing Press, the number of books in Europe increased from 30 thousand to 12 million units, half a million of which still survive today. This 400X growth helped Europe go through a cultural revolution known as the Renaissance and the spread of the Protestant Reformation in the 1500s.

While the distribution of the Printing Press was slow due to the monopoly, the cities that received the machine witnessed huge economic growth, for example, at one point, the city of Venice was the central publishing hub of Europe bringing massive trade into the city.

Nations that adopted the Printing Press witnessed reduced costs and price-performance improvement in publishing that helped the circulation of knowledge in their societies and increase literacy rates.

Literacy rate as share of the total population, from 1475 to 2015 — estimates correspond to the share of the population older than 14 years that can read and write — Source(s): WDI, CIA World Factbook, and others

Growing literacy rates had the spillover effect of creating knowledge networks through the migration of high-caliber talent, schools, and universities to the cities that used the Printing Press at scale which led to the creation and development of new employment opportunities and advanced technologies.

Gutenberg’s printing press was an innovation that revolutionized Europe and propelled nations such as the Dutch and British Empires into global dominance over the next 2–3 centuries.

19th century: the industrialized use of Electricity

Humans have been intrigued by electricity since antiquities as they thought lightning is the release of energy when two clouds are in love and are about to make a baby cloud or when Thor rode into battle in Valhalla.

Until the early 19th century, humans had no conceptual understanding of current — the flow of electrical charge — and the relationship between electricity and magnetism until several European scientists including Alessandro Volta, Hans Christian Ørsted, André-Marie Ampère, James Clerk Maxwell, and Michael Faraday, across several decades, invented the battery and electromagnetic motors, generators, and transformers.

But despite these inventions, until the 1870s, there was still no system in place for using electricity on a commercial or industrial scale. Thomas Edison was the first person that saw the potential for the electricity grid including the generation of power, its distribution to homes and businesses, and the invention of useful products that required electricity to work with. To get this going, Edison partnered with J.P. Morgan as his investor and for the first time built commercial electric light bulbs that were durable and economic.

Ask History: Who Invented the Light Bulb? | History Channel

Before Edison’s incandescent light bulbs, nights were dark, and candles and gas lamps were the only solutions that were weak, smelly, and dangerous. With the light bulb and the power distribution grid at work, people stayed up longer and more work got done resulting in increased economic productivity. Beginning with the incandescent light bulb, Edison, and other inventors, used the discoveries of the early electrical physicists to exponentially transform the world.

The LINEAR price for lighting in the UK — the price per million lumen-hours in British Pound. 1-lumen hour is equal to the luminous energy emitted in 1 hour by a light source emitting a luminous flux of 1 lumen. For comparison: a standard 100W incandescent lightbulb emits ±1700 lumen — Source(s): Fouquet and Pearson (2012) — Note: The price is adjusted for inflation and expressed in prices for the year 2000. Shown is a 5-year moving average

From the linear graph of the price for lighting in the UK, it is clear that across history we have managed to improve the price performance of lighting, but electricity has had the most radical impact on the cost of lighting beginning in the 19th century.

The LOGARITHMIC price for lighting in the UK — Source(s): Fouquet and Pearson (2012)

As better observed from the log graph of price for lighting in the UK, we see that with the commercial introduction of electricity in the 19th century and the invention of the incandescent light bulb, the price of lighting drops dramatically — which was a social paradigm shift and a move from scarcity to the abundance of lighting.

The 20th century — paving roads for automobiles

The First Industrial Revolution is believed to have occurred around 1800, starting in Britain with the invention of steam engines which led to many scientific discoveries by scientists and researchers including Volta, Faraday, and Maxwell that lead to the commercialization of electricity.

The Second Industrial Revolution resulted from the industrialization of electricity and mass manufacturing in the US around the 1900s. The world at the end of the 19th century looked very different than it did at the beginning of it as people could see at night, manufacturing had scaled, and automobiles were now accessible to the few.

While Carl Benz invented the practical automobile in 1885, in the late 1800s most people were still moving around on ships and/or trains between cities, and on horses within them, as cars were expensive. The problem with horses was that they got sick and into accidents, died, and cities couldn’t figure out how to clear their manure fast enough.

People loved the idea of cars but couldn’t afford them. In 1903, Henry Ford, the chief engineer at Edison’s Illuminating Company in Detroit and a famous inventor who modeled his life on Thomas Edison, quit his job and co-founded the Ford Motor Company, at a time when many other small automobile companies produced unreliable and non-standardized gas, electric, and steam-powered cars for the rich.

In the early years, Ford Motor was a small-scale operation that carried out experimental work with no uniform processes for mass manufacturing. However, Ford’s team drummed up a lot of public interest with its cars including the Ford 999 which broke the land speed record, covering a mile in 39 seconds.

In 1908, Ford set out to manufacture a car that was affordable, easy to operate, and tough, releasing the Model T, which changed the world. In the first month of production, only eleven Model Ts were produced, but within two years — in 1910 — Ford produced 10,000, and in 1914, the year the first World War broke out, the company shipped 250,000, and in 1916, Ford sold nearly 500,000 cars — each for less than half the 1914 price!

Ford Model T production numbers — compiled by R.E. Houston, Ford Production Department, August 3, 1927 — source: MTFCA
Ford Model T production numbers — in 1927 Model T was dropped and Model A replaced to compete with competitors’ offerings, compiled by R.E. Houston, Ford Production Department, August 3, 1927 — Source(s): MTFCA
Ford Model T price in US dollar — compiled by R.E. Houston, Ford Production Department, August 3, 1927 — Source(s): MTFCA
Ford Model T’s toady price in US dollar adjusted with inflation — compiled by R.E. Houston, Ford Production Department, August 3, 1927 —Source(s): MTFCA — Within 1.5 decades, Ford dropped its model T price by 340%

In 1927, Ford produced fifteen million cars, plus tractors and lots of other machines, cracking mass production by focusing on specialization and standardization using the assembly line. For example, the Ford plants at Highland Park and River Rouge only produced one thing — the Model T. In the early days, workers could assemble a Model T from raw materials in only twenty-eight hours, and later using sheet metal, they could assemble a Model T in one hour, without robots or computers.

Mass production of automobiles meant that road infrastructure, gas stations, and mechanic shops needed to scale up to cater to millions of motorists. The automobile increased access to the country, more people moved to cities, and new areas of study at schools and universities were introduced.

To catch up, other car manufacturers emulated Ford’s assembly-line philosophy and while Ford concentrated on producing as many Model Ts as possible, General Motors created many different models, giving Americans more options. By the mid-20th century, Ford, GM, and Chrysler dominated the American automobile market, pushing out small, regional manufacturers or consolidating them.

US car sales shares by automaker

Increasingly, being an engineer wasn’t something you could just do, but needed university training, financial backing, and professional licensing, and corporations such as the Edison companies and Ford Motor offered paths for professionals to follow by studying what engineers did all day.

US vehicles per 1000 people — linear graph—Source(s): OEE&RE

As visible from the graph above, people loved the automobile in the US at its inception. From the early days of Ford’s entry in the early 1900s, sales picked up and didn’t stagnate until recently.

US vehicles per 1000 people — log graph — source: OEE&RE

As the log graph shows, the reception towards the commercial automobile and Ford’s offering kicked off in ~1905–1910. It continued till 1930, nearly two decades of strong growth and demand until it had become a socially accepted phenomenon. By 1930, life before the automobile was just a distant memory.

The digital era

While it took airlines 68 years to reach 50 million users, Pokemon Go reached that milestone in just 19 days. The question is why?

The number of years it took various phenomena to scale to 50 million users — it’s fair to say that airplanes and automobiles haven’t gotten cheap enough to be mass consumed — however, the marginal costs of scaling and using YouTube and Twitter are near zero (if not zero). Buy why?

Before the introduction of the internet in the mid-1990s and the widespread adoption of digital communication tools, offerings could only spread as fast as they could be manufactured and distributed along a lengthy value chain. This required substantial upfront investment into plants, workforce, inventories, maintaining relationships with middlemen and distributors, and advertising to spread. Therefore, traditional products often required decades to get to scale.

However, in the digital era, to create value, all you need is a useful piece of code that can be replicated or reused indefinitely at small marginal costs, and inherently, adoption rates become exponentially quick, especially with viral referral loops in play. And 3 fundamental laws are driving the digital era’s exponential impact.

  • Wright’s law
  • Moore’s law
  • Metcalfe’s law

Wright’s law

Wright’s law in the simplest form suggests that we learn by doing. It’s modeled as a mathematical equation that describes the relationship between the cost of producing a good and the experience a manufacturer gains as production scales. This model is also called the learning or experience curve.

The experience curve

The experience curve is quite simple:

  • In the beginning, production is slow and expensive and there are a lot of economic inefficiencies
  • However, as production scales, the organization accumulates knowledge and experience and through shortcuts and optimization the economics improve significantly

Therefore, the experience curve states that the more experience an organization accrues, the cheaper it can produce. Theodore Wright was the first person to quantify this curve as an equation being Y = aX^b while studying airplane manufacturing. In this equation:

  • Y is the cost per unit production
  • a is the cost to produce the first unit
  • X is the cumulative number of units produced
  • b is the percentage reduction in costs

Wright’s law is not just used in the manufacturing industry but across industries and various technology sectors. The general rule of thumb is that for every doubling in production scale, we should witness a constant reduction in unit costs, which can vary depending on the sector.

There are several reasons behind Wright’s law including:

  • With more experience and repetition, labor efficiencies improve
  • Standardization of production, its parts, and process reduces cognitive load and improves efficiencies
  • With standardization, specialization takes over and with increased focus, improvements appear with more acceleration
  • Feedback from markets and consumers and improve non-necessary features and products and hence become more efficient in serving the market demand
  • Economies of scale help companies and their employees gain access to capital and technologies that can’t be accrued by individuals, and therefore scale helps improve efficiencies and reduce production costs

The implication of Wright’s law for new technologies, products, and companies entering the markets is that the first mover advantage, if managed correctly, can be important as the organization’s accrued experience should become an economic barrier to entry. This is key to becoming an exponential business.

Moore’s Law

In 1965, Gordon Moore, the co-founder of Fairchild Semiconductor and Intel, postulated that the number of transistors in a dense integrated circuit (IC) will double about every two years (or 1.6 years to be precise). Moore’s postulation which later came to be known as Moore’s law is an observation and projection of a historical trend and rather than a law of physics, it is an empirical relationship that is gained from experience in manufacturing (a branching of Wright’s law).

Acknowledging Moore’s law and looking back at humanity’s computation power per dollar spent since the early 1900s, we observe that our capacity to compute has compounded for as long as we can find data, starting long before the appearance of Intel, IBM, and the semiconductor industry, from the electromechanical computation era to relay, vacuum tubes, transistors, and integrated circuits.

An expanded view of Moore’s law across 122 years beginning in 1900 — as you can see, no global events, including the World Wars, were able to derail this trend — Source(s): Ray Kurzweil, Steve Jurvetson

The steady and predictable nature of Moore’s law is driven by the fact that when a domain, discipline, industry, or technology is digitalized, and given information properties, its price performance improves rapidly and exponentially, with a doubling factor every few years, and once that pattern starts it does not seem to stop.

This trend is observed and documented across different industries and sectors during the third and fourth industrial revolutions across a variety of spaces including AI, robotics, energy, transportation, media including the print, music, and video spaces, retail, drones, hospitality, banking and finance, communications, biotech, nanotech, neuroscience, medicine, agriculture, space travel, and many more. This expanded view of Moore’s Law whereby information properties disrupt industries is called Kurzweil’s Law of Accelerating Returns.

Furthermore, as these accelerating technologies and spaces intersect (e.g., Deep Learning and Biotechnology), the pace of innovation, change, and price performance improves exponentially and not linearly.

Metcalfe’s Law

Metcalfe’s law states that the value of a communications network is proportional to the square of the number of connected users in the system, or with N being the number of nodes or users. The bigger the number of nodes, the more exponentially the system adds value to the nodes.

This form of presenting the value of a network (i.e. mathematical equation) was first formulated by George Gilder in 1993 and later attributed to Robert Metcalfe who co-founded the Ethernet. Metcalfe’s law was originally presented in 1980, not in terms of users, but rather of “compatible communicating devices” (e.g., fax machines, telephones) in a communication network. It was only later with the globalization of the Internet that this law carried over to users on networks and the term ‘network effects’ was coined.

Source: The Reliant Project

Metcalfe’s law had a predecessor, Sarnoff’s law, that applied to the broadcasting industry which was a one-directional communication medium. Sarnoff’s law equates the value of a one-directional network such as a TV or Radio network to N — hence the more people subscribing, the more that network has value.

In 1999, David P. Reed of MIT while acknowledging that some networks grow as a proportion to the square of network size, suggested that group-forming networks that allow for the formation of clusters (clustering happens on networks that scale exponentially and have large many-to-many node structures such as social networks) scale value even faster than other networks. Reid suggested that group-forming networks, increase in value at the rate of 2^N, with N being the total number of nodes on the network.

The reason why Reed suggested 2^N instead of N² is because the total number of connections in a group-forming network is a function of the total number of nodes plus the total number of possible sub-groupings or clusters, which scales at a much faster rate with the addition of more users to the network. This is known as Reed’s law.

Nodes of the human civilization

Today we have more than 5 billion people connected to the internet and this figure is projected to reach ~7.7 billion by 2030. That is the addition of ~2.5 billion new nodes over the next 8 years. Imagine the possibilities of this many human interactions, the knowledge created and shared, the economic value generated, and the consequential amount of change we will be seeing in the coming decades.

Number of global people online in millions — Source(s): Internet World Stats, Cybersecurity Ventures

We also need to acknowledge the number of connected devices that generate data on the internet. While there were only 5,000 connected devices in 1950, we currently have more than 22 billion, and IBM forecasts that this figure will reach more than 100 billion by 2050 (and will probably be more than that).

Number of connected devices — Source(s): IBM, Our World in Data

While we are just at the beginning of manipulating our surrounding environment with software, data, and analytical technologies such as AI, imagine what our world would look like in 2050 when nearly everyone will be online and the number of connected devices will be beyond 100s of billions and our tools to interpret this abundant data would have respectively improved.

How many industries and sectors will get disrupted? How often? How large? If history has taught us anything, it’s that the pace and scale of change will only grow — exponentially

The shortened lifespan of companies…

Average company lifespan on the S&P 500 index from 1965 to 2040 in rolling 7-year average — This figure was 61 years in 1958 and was at 18 in 2016, at these rates, by 2030, 75% of the companies on that list will have disappeared. Based on the red and grey trendlines, the average lifespan of companies is quickly dropping and will continue to drop well into the future — Source(s): Innosight; McKinsey: Traditional company, new businesses

…and the accelerated birth of unicorns

Time to market capitalization or valuation of a billion USD — figure in () is the year the business was established. As seen from the graph, the speed with which startups and companies are reaching unicorn status is continuously dropping — Source(s): Various publications

Consequent to the growing degree of connectivity and the transformation of industries by the influence of information-driven technologies, we see startups quickly entering the market and reaching the scale needed to become a unicorn (i.e. valued at $1B). This is placing growing pressure on larger and established businesses and reducing their lifespan. A few examples of how quickly startups scale include:

  • Avant, founded in 2012, an unsecured lending startup that uses proprietary technology to measure creditworthiness, only needed 1.5 months to become a unicorn
  • Jet.com, a shopping platform with real-time pricing, only needed 4 months to become a unicorn and got purchased by Walmart in 2015
  • iCarbonX, founded in 2015, is a Chinese startup that combines genomics and AI to provide data-based insights into how diseases progress in the body and what steps individuals can take to slow that progression, only needed 5 months to become a unicorn
  • Clubhouse, the social audio platform everyone knows these days, took ~10 months to become a unicorn in 2021
Number of unicorns to hit the market globally — 2021 produced more unicorns that the combination of those since 2007, which will only lead to more rapid innovation and accelerated change to come in the next decades — Source(s): CBInsights, Crunchbase, and PitchBook

Check the link below to better study the scale and speed at which startups are entering markets and disrupting current norms of the business.

A driver for this rapid observation is that the cost of starting a software startup is quite low these days and hence, at scale, there are millions of entrepreneurs across the globe experimenting with new ideas and business models to remove frictions and deliver new experiences using lean go-to-market strategies.

Costs to launching an internet tech startup — source: Mark Suster’s blog: Both sides of the table

Furthermore, as products and services become information-enabled, major business functions can be transferred outside the organization to users, fans, partners, or the general public (e.g. Waze or Google Maps) and the cost of delivery and/or go-to-market and experimentation further drops.

The economic model of disruption

Case study: Digital Photography

When an industry becomes disrupted, in recent times through digitization, we move from a scarcity model to abundance and democratized access. In the case of photography, in the pre-digital era, one could only carry a limited number of films and prints because of the space required and the high marginal cost of producing a new photograph, considering the raw material and time it took to get prints. Which is quite difficult to imagine these days.

When disruption happens, three key economic and social paradigm shifts happen:

  1. The marginal cost of producing an additional unit offering drops significantly. In the case of digital photography, we can now as many photographs as we like at no additional unit cost
  2. With reduced marginal costs, the domain explodes and grows exponentially. In the case of digital photography, we now take billions of photographs daily, which compared to the film era, is millions larger
  3. As the domain explodes from scarcity to abundance, new value propositions, business models, and innovative offerings become a must. In the case of digital photography, we don’t need to take courses in composition to optimize every film photograph we take because now we all have eight copies of our photographs on 10 different devices, and businesses such as Instagram and TikTok have appeared around the power of the digital camera and photography

We can find many examples of these trends occurring in recent years across several sectors including the following:

  • Apple vs. Nokia (or Blackberry)
  • Waze vs. all hardware-based navigation devices such as Navteq or TomTom
  • Netflix vs. Blockbuster
  • Streaming services vs. record label holders
  • Google vs. Yahoo!
  • Amazon vs traditional retail including bookstores and RadioShack
  • Myspace vs. Facebook

And there are plenty of industries that are currently getting ready for disruption: moving from scarcity to abundance.

Case example: the newspaper industry

With newspapers, we are at all-time lows in circulation numbers in recorded history. We’ve moved from a scarce and difficult to procure and distribute product to a space of abundance of written content by professional, semi-professional, and ordinary people on the internet. We produce and copy written content at a mass scale daily and the marginal cost of producing a new piece of written content is zero for us. Now, we only pay for this content type if it’s highly differentiated.

Newspaper circulation in the US in millions
Ad sales revenues Google vs. Newspapers — Google’s scalable business model and flexibility to cater to various needs have expansively grown the value of its ad market beyond what newspapers had experienced, across a much shorter time horizon. Up to 2021, Google has made 61% of the revenue that all newspapers in the US have made since 1956 — Source(s): Pew; Google

Case example: the music industry

Since the 2000s with the advent of digital, physical means of storing and distributing music and audio content including tapes, cassettes, and vinyl quickly lost their ~97% market share of value poll position to 20% in 2020 and only 11% in 2021.

Today, with music streaming services, the marginal cost of listening to additional tracks is zero, from a period of scarcity we have shifted to an era of abundant access to audio content, and monetization of the category has shifted from limited access to tracks to personalized experiences on platforms such as Spotify.

Nominee for disruption: Space travel

From the second half of the 20th century until now, space access has been costly and limited to governments and billionaires. However, the cost to launch people and cargo to lower Earth orbit continues to fall each decade as new technologies are developed and the sector becomes more commercialized (e.g. SpaceX).

Advances in the future that will include lighter materials, the use of inflatable modules, new fuel types, space planes, and more efficient engines will further reduce the cost of space travel and democratize access whereby visiting a space hotel could be a routine trip in the second half of the 20th century.

Cost per kg of cargo delivered to Lower Earth Orbit in USD and adjusted for inflation of the year 2000 — Source: Future Timeline

Nominee for disruption: Autonomous vehicles

In the first version of the Google car, all the sensors used to give it vision cost about $300,000 per vehicle, which ruled it out as an economic offering that could scale. However, only two years later, in 2012, the same package cost $75,000, a 75% drop in costs, and in 2017, the Lidars of an autonomous car cost only $50 per vehicle and $10 in 2020.

And we will continue to see this steady and exponential price performance improvement until autonomous vehicles are commercialized, which will rapidly change how we commute and live in cities, very similar to Ford’s impact on US society back in the early 20th century.

Nominee for disruption: Biotechnology

In biotechnology, we have been witnessing a rapid price performance improvement since the mid-2000s.

The first human DNA sequence in 2000 cost ~$2.7 billion, the second one was about $400 million, the third $150 million, the fourth about $14 million, and today it costs ~$1,000. Compared to the price in 2000, human DNA sequencing has improved by 3,000,000X, and pretty soon, it will cost around ~$100 and be commercialized.

Log graph of cost per human genome in USD — Source(s): DNA Sequencing Costs: Data (genome.gov)
Log graph of cost per human genome in USD — Source(s): DNA Sequencing Costs: Data (genome.gov)

This access and price performance improvement will mean we can study incurable diseases such as cancers closely and find remedies and deliver personalized and customized medication for individuals than the average solutions currently in the market.

Nominee for disruption: Solar energy

The future of energy needs to be sustainable and clean, and solar energy will be a massive source to rely on.

Solar energy has been improving its price performance for about 30–40 years, doubling every 22 to 30 months, and at this pace, solar could rise to deliver 100% percent of the world’s energy needs within the next ~2 decades.

And if its performance continues to improve, in the continuing decades we could have a surplus or abundance of energy, which if not regulated, the socioeconomic and geopolitical implications of this for regions such as the Middle East could be massive as it has historically depended on the export of energy for economic growth.

Log graph of global average solar PV module prices, measured in 2019 as USD per Watt — Source(s): Our World in Data — note: the price in 1976 was $106/Watt and 0.38 $/Watt a drop of 280X in 4 decades
Log graph of global average solar PV module prices, measured in 2019 as USD per Watt — Source(s): Our World in Data — note: the price in 1976 was $106/Watt and 0.38 $/Watt a drop of 280X in 4 decades

We know that our current oil, coal, and natural gas resources on the whole are equivalent to only five days of sunlight hitting the Earth. Therefore, if/when solar energy is used on a commercial scale, our energy consumption paradigms will shift significantly.

The exponential deception

We have lived in a largely linear natural world for most of our history. For example, time moves on a 24-hour per day, and a year consists of 365-day cycles. And we have evolved this linear intuition as it makes predictions of our environment easier and more comprehensible. Millions of years of evolution have conditioned us to think linearly, making it difficult for us to make sense of exponential trends and predict them.

The linear vs exponential deception

Since we started living in tribes hundreds of thousands of years ago and upon the agricultural revolution, we have come to perceive that any growth should be linear — if you want to harvest more, add more land and resources and you will get a linearly growing return.

In the industrial revolution, we replaced humans with machines, but the scale of what we could produce was also limited to the machines we deployed, and that’s why we see hundreds of automobile brands across the globe, due to the linear nature of expanding these businesses, which is slow and cumbersome.

The greatest shortcoming of the human race is our inability to understand the exponential function — Albert Allen Bartlett, emeritus professor of physics at the University of Colorado at Boulder

To clarify the deception, imagine taking 30 linear steps one after the other. This is easy for you to imagine and you can build a relatively accurate sense of how far 30 steps will be from where you are stationed. However, if you were to think of taking 30 exponentially growing steps where every step is twice as far as the previous one, it will be intuitively difficult for you to quickly conclude where you’d end up being in the end.

Just to ease your pain, 30 exponentially growing steps by 2X, depending on your step length, will on average be 1 billion meters, which is equal to going 20 times around the Earth. And because it’s hard to understand these trends intuitively, we will continuously make false predictions surrounding the impact of exponential trends.

This error in judgment happens to the best of us:

  • In the early 1980s, McKinsey & Company was commissioned by AT&T (whose Bell Labs had invented cellular telephony) to forecast cell phone penetration in the U.S. by the year 2000. The consultant’s prediction of 900,000 subscribers, was less than 1% of the actual figure of 109 Million in 2000. McKinsey was using linear tools and the trends of the past to predict an accelerating future, which is generally off the mark by a large amount

In 2000, nearly 900,000 new mobile subscribers were joining the global mobile network every 3 days. McKinsey noted the problems it saw with the new devices as:

  • The handsets were absurdly heavy
  • The batteries kept running out
  • The network coverage was patchy, and
  • The cost per minute was exorbitant

However, in 1998, cellphone quality was up and prices were dropping rapidly.

Bell Labs’ Research (Prof Brian Kernighan) — Computerphile

At the time McKinsey’s advice persuaded AT&T to pull out of the market, however, a decade later, to rejoin the cellular market, AT&T acquired McCaw Cellular for $12.6 Billion, a hefty price for not understanding the linear vs. exponential deception.

Another interesting story is the fact that the digital camera was invented at Kodak Eastman but was rejected because they feared it would cannibalize existing businesses. This proved a bad decision that led to Kodak’s bankruptcy in 2012. This is all while companies such as Apple, Facebook, Instagram (launched in 2010), and TikTok (launched in 2016) have scaled to substantial values riding on the digital camera’s paradigm shifts.

Furthermore, we need to accept that demand is often a lagging indicator of technological progress. For example, in 2008, who thought we needed iPhones until it was there to be had? Therefore it’s important to understand technology evolution and pull consumers towards exponential curves. To survive, and grow, it’s important to understand how quickly a market is going to change, and forecast it before others including employees, colleagues, seniors, investors, and customers do.

Three emotions of the deception lifeline

Across the exponential and linear trends, that are three social and technological phases that we experience:

  1. The disappointment phase: with established current offerings in the market, exponentially riding offerings start slow and leave consumers disappointed. For example, the battery life is terrible, design aesthetics are not meeting expectations, the finished price is too high and does not match the functionality, etc.
  2. The ‘aha’ moment: gradually, technological improvements help improve the offering, and that’s when we expect to see product-market fit and new offerings cross the chasm. This is very similar to the feeling people had during the iPhone reveal in 2008 when a portable mini-computer that was also a communication device was introduced
  3. Chaos, amazement, paradigm shifts, and new norms: this is the world where organizations who haven’t planned long term and were not predicting exponential forces will get left behind, such as Nokia, and new businesses emerge. In the case of the smartphone industry, we saw many global smartphones appear, mobile internet penetration rates grew from low digits to 70–80% of the population in a matter of years, and in that period, entrepreneurs launched new startups across the globe and scaled them into exponential businesses, with Uber as an example that depended on mobile connectivity to commercialize

Companies and businesses that remain on their linear paths are destined to fail. This is the path that Nokia chose after the arrival of Apple’s iPhone and within five years fell from the largest cell phone manufacturer in the world to bankruptcy.

Overcoming the linear-exponential deception

The best solution to overcome our linear thinking bias is to constantly scan for exponential updates through systematic thinking. To do this:

  • Uncover social and technological trends that are currently in the making — for example, Social Virtual Reality seems in its initial stages and will need more time to disrupt our lives, if successful at all
  • Realize and educate regarding exponential trends that are going unnoticed but have the potential to disrupt our lives and businesses if they cross the chasm — for example, currently, 3D printing, solar energy, and Blockchain are deceptively exponential technologies that will change our lives soon
  • Contemplate how these trends will dematerialize, demonetize, and democratize our lives

It’s important to consistently think about what’s going from a deceptive to a disruptive stance because you will either be the disruptor, whether a startup or corporation or a disrupted. To expand on the last point:

  • Dematerialization is the notion that a bunch of products and services can be aggregated into a single point of access. In the case of smartphones, it’s now a camera, telephone, video player, music player, navigator, etc. In the case of Spotify, it’s the single point of access to all audio content and their associated experiences. With Airbnb, it’s all the world’s underutilized room that can be shared and optimized for economic performance, hence, dematerializing Real Estate. Uber is currently all inner-city commute and logistic-related needs in one spot coined as a super-app
  • Demonetization is a direct consequence of dematerialization and expansion of the service, where the price performance of the trend improves, costs decline rapidly, and the economics of the trend now make sense. In this regard, Uber demonetized taxis, Amazon books, Craigslist newspaper ads, Skype long-distance calls, etc. Demonetization is perhaps the easiest indicator to track in the market for corporations to react to, however, it might be a little late to respond to the trend by then
  • Democratization is a consequential effect of dematerialization and demonetization that leads to an abundance of access to previously scarce resources whether that be audio and video content through Spotify and Netflix, all restaurants of the city via Uber or Doordash, or a place to stay the night through Airbnb or Booking.com

The impact of exponential trends and organizations

Exponential organizations are those whose measured output is immensely larger than those of their peers in the space because of building organizations that leverage accelerating technologies while riding exponential trends

We truly are living in extraordinary times because technology is helping us gain democratized access to what used to be scarce and every day, every single one of us is helping humanity get to a state of abundance. Over recent centuries we have managed to reduce global poverty and famine rates, wars, the death of children under the age of 5, annual hours worked, death due to natural catastrophes, cost of internet, digital storage space, computing, and launching a startup, while increasing the number of democratic states, and life expectancy. So while there are adverse effects to having an exponential organization such as climate change and inequality, overall, they can be deemed as a net positive for our societies.

The case of poverty

Average global extreme poverty as the share of total population defined as living under ~$2/day- Sources: Source: Clio-Infra — within 2 centuries, we have managed to lift 70% of the global population out of extreme poverty while the global population has also been growing exponentially
Average global extreme poverty as % of the total population defined as living under ~$2/day — within 2 centuries, we have managed to lift 70% of the global population out of extreme poverty while the global population has also been growing exponentially — Source(s): Clio-Infra
Distribution of population between different poverty thresholds, World, 1981 to 2017
All figures are adjusted to account for inflation and price differences across countries and are expressed in international dollars at 2011 prices — in addition to lifting extreme poverty rates, we have managed to shift people toward the global middle-class, helping with the capital required to plan for a better future for ourselves and our next generations — Source(s): Our World in Data

The case of democracy

Share of World Political regimes based on the criteria of the classification by Lührmann et al. (2018) and the assessment by V-Dem’s experts — 50% of the world's 177 recognized political regimes is a democracy. This was at 5% at the beginning of the 20th century — Source(s): OWID based on Lührmann et al. (2018) and V-Dem (v12)

The case of annual hours worked per person in the US

Average working hours per worker over a full year in the US — before 1950 the data correspond only to full-time production workers (non-agricultural activities). Starting in 1950 estimates cover total hours worked in the economy as measured from primarily National Accounts data — at the beginning of the 20th century, the US managed to reduce total working hours by 40% in the span of 2 decades and kept it stable since — Source(s): Huberman & Minns (2007) and PWT 9.1 (2019)

Building exponential organizations

The implications of exponential megatrends

The challenge today is that all of our organizations, whether for-profit or not, are built for efficiency, predictability, and to maintain the status quo and are not ready for the change caused by external disruptions.

If you are working at a legacy business and are up against an exponentially geared competition, it will seem as though the competition is racing away from you, and you are sliding backward into demise. Furthermore, the board and seniors at legacy businesses find that dedicating more resources to the problem, is risky for their short-term goals and generally doesn’t return results and key talent is locked into projects that will not bear fruit.

A “legacy business” or “traditional organization” is defined as a static, siloed, structural hierarchy that is primarily designed for stability

Goals and decisions rights flow down the hierarchy, with the most powerful governance bodies at the top

It operates through linear planning and control to capture value for shareholders.

Its skeletal structure is strong, but often rigid and slow moving

In essence, legacy businesses are locked into linear thinking and short-term goals, setting up matrix structures geared towards managing physical assets and sales targets derived from some form of market monopoly. This is while exponential organizations in the digital era are managing information and knowledge and very few physical assets — imagine Uber or AirBnB or any company or asset built on a Blockchain such as Ethereum. Managing an exponential organization requires a different belief system, new operating models and structures, and long-term thinking.

There are four problems with matrix structures when it comes to responding to change: 1) creation of organizational silos and lack of transparent communication and alignment, 2) delays in decision making and response time, 3) lack of accountability, and 4) focus on short term results (i.e. sales)

The rise of the exponential entrepreneur

In the last two decades, we have seen a new breed of entrepreneurs, with little or no experience in the domain that they are entering, leveraging new technologies and attacking legacy businesses with impunity, by riding on exponential megatrends and using information-enabled assets. This includes Elon Musk who has little background in the automobile or space industries but is at the forefront of disrupting them with his companies: Tesla and SpaceX.

The question is: what more is there to be learned from their operating philosophies?

TED: from a niche conference to a global media brand

TED Talks is a great example of a niche annual conference that kicked off in 1984 and did not pick up much traction until 1990 with a small number of passionate attendees. For more than a decade TED Talks wasn’t a global brand until Chris Anderson took it over in the early 2000s, and he did three things to scale the brand:

  1. Devised an ambitious and powerful purpose “TED is a nonprofit devoted to making great ideas accessible and sparking conversations”
  2. Released all the TED talks for free and online, demonetizing and democratizing access
  3. Allowed anybody to go create a TEDx event, scaling high-quality content across the globe by building and leveraging its community

Within five years, Chris Anderson created a global media brand that nobody had attempted before or achieved that scale in such a short period and at such small costs. TED Talks reached more than 1 billion views in November 2012.

Building an information-enabled exponential organizations

Drivers of success in building information-enabled exponential organizations: 1) Having an ambitious and transformative purpose, 2) Utilizing externalities to capture data and scale, 3) Aligning internalities to derive insights and foster scale
Drivers of success in building information-enabled exponential organizations: 1) Having an ambitious and transformative purpose, 2) Utilizing externalities to capture data and scale, 3) Aligning internalities to improve productivity

There are three key success factors in building an information-enabled exponential organization:

  • Having an ambitious and transformative purpose
  • Utilizing externalities to capture data and scale
    1. Recruit flexible and on-demand staff
    2. Leverage community and crowds
    3. Be data-driven and automated
    4. Leverage non-mission critical assets
    5. Build virtuous and positive feedback loops
  • Aligning internalities to improve productivity
    1. Build self-provisioning platforms
    2. Utilize dashboards and OKRs
    3. Experiment, experiment, and experiment more
    4. Empower through autonomy
    5. Build a real-time productive organization

Ambitious and transformative purpose

Exponential organizations, for-profit or not, aim BIG because if they think small, it’s highly unlikely that they will pursue a business strategy built on rapid growth, which is the best barrier to entry to have when riding a disruptive megatrend. Therefore by nature, exponential organizations need to aim high.

That’s why looking up the vision of exponential organizations leaves us with statements that at their initiation, could have been deemed outrageous. For example:

To organize the world’s information and make it universally accessible and useful — Google search mission statement

All exponential organizations have a massive transformative aspiration that creates community, social traction, and cultural movement. This cultural movement inspires employees and customers and builds goodwill around it. Furthermore, an ambitious purpose, makes it easier to retain talent as it combines and concentrates individual aspirations into collective ones.

Furthermore, in legacy businesses, seldom do employees know what their purpose is but everybody in Google Search knows that their North Start is organizing the world’s information which changes the focus of the organization’s political games from internal affairs to value creation for customers, which is key to staying relevant in a rapidly changing market environment.

Utilizing externalities to capture data and scale

Traditionally, to scale, businesses would acquire a physical asset and a workforce, put legal and financial barriers to entry around them, and sell access to scarce resources. Currently, the new model which leads to exponentially scalable organizations is about keeping a small asset footprint and utilizing a network of abundant low-cost resources outside the organization using information and communication technologies.

Utilizing externalities to capture data and scale — at the core of all the actions above, an exponential organization is either trying to gain data intelligence and insights from the market external environment, either at scale and/or quick turnaround which will result in delivering better solutions which means revenues, or reducing its capital and operating expenditures
Utilizing externalities to capture data and scale — at the core of all the actions above, an exponential organization is either trying to gain data intelligence and insights from the market external environment, either at scale and/or quick turnaround which will result in delivering better solutions which means revenues, or reducing its capital and operating expenditures

For example, in the case of Uber’s ridesharing services, its core asset is matching drivers to passengers, and neither side is controlled, owned, or employed by Uber.

1. Recruit flexible and on-demand staff

In a fast-changing world, having flexible and on-demand staff is necessary for speed, functionality, flexibility, agility, and reducing operating costs, which can create a financially lean company that can grow bigger and innovate faster and better.

Furthermore, depending on the industry, no matter how talented internal employees may be, most will become obsolete and uncompetitive due to rapid market changes in a short period and a flexible work and employment model can help maintain the advantage. Additionally, employees and staff can learn to manage themselves as individual businesses, such is the case with Uber drivers, which could mean higher income compared to working on fixed employment opportunities.

Having an on-demand staff policy can help an organization scale its employees to large numbers which would lead to increased diversity and innovative problem-solving.

The number of people working independently in the U.S. — figures in millions — in 2021 51 million people in the US, 24% of the working age population, worked as independent workers- Source(s): MBO Partners
The number of people working independently in the U.S. — figures in millions — in 2021 51 million people in the US, 24% of the working age population, worked as independent workers— Source(s): MBO Partners
The number of people working independently in the EU27 — figures in millions — nearly 200 million people in the EU27 work as independent workers, which is 42% of the working age population- Source(s): Eurostat
The number of people working independently in the EU27 — figures in millions — nearly 200 million people in the EU27 work as independent workers, which is 42% of the working age population— Source(s): Eurostat

These days several business sectors are using on-demand staff including:

  • Logistics sector: Uber and its flexible work with drivers and Doordash and its food deliverers
  • Media sector: YouTube, TikTok, Spotify, and other social media platforms and their content creators and influencers
  • Travel sector: Airbnb and its army of hosts
  • Freelancing sector: that includes a host of freelancing platforms such as Upwork

In 2016, up to 162 million people in the EU-15 and US (20–30% of the working-age population ), engage in some form of independent or flexible work, mainly driven by the digital and gig economies. Furthermore, most independent workers have actively chosen their working style and report high levels of satisfaction with it — Report: Independent work: Choice, necessity, and the gig economy, McKinsey

2. Leverage community and crowds

To become an exponential organization in the age of information-based business models, you need to outsource the non-critical parts of your value chain to minimize operating costs and maximize focus on the core drivers of value and success.

Traditionally we have formed communities and tribes based on our geography and ideologies. Today, the Internet is helping us form trait and interest-based communities where the members share the same intent, beliefs, resources, preferences, desires, and other characteristics, irrespective of their geography.

Our digital communities can be made up of our core team members or employees, alumni, partners, vendors, customers, users, and fans that are truly engaged as peers. Although, the more open and interactive a digital community, the more of a well-thought-out governance structure will be needed to regulate and monitor the engagement.

Communities and crowds can be leveraged to generate, develop, validate, communicate, distribute, and sell new ideas and sometimes fund them. Examples of these include Waze, Google Maps, Wikipedia, all social networks, and Kickstarter. People are intrinsically motivated to help at no cost, out of benevolence and generosity, rooted in our evolution as a species.

The world has over a trillion hours a year of cognitive surplus of free time to commit to shared projects — Caly Shirky, 2012, TED

How cognitive surplus will change the world — Clay Shirky looks at “Cognitive Surplus” — the shared, online work we do with our spare brain cycles. While we’re busy editing Wikipedia, we’re building a better, more cooperative world

3. Be data-driven and automated

The volume of data created, captured, copied, and consumed worldwide from 2010 to 2025 (in zettabytes)- By 2025, we will circulate 181 ZB of data which is 90X the volume we created in 2010 — Source(s): IDC; Seagate; Statista
The volume of data created, captured, copied, and consumed worldwide from 2010 to 2025 (in zettabytes)— By 2025, we will circulate 181 ZB of data which is 90X the volume we created in 2010 — Source(s): IDC; Seagate; Statista

With abundant data available, we need to move away from intuitive hypotheses and cognitively biased guesses toward data-driven and validated decisions. Using Machine Learning and Deep Learning technologies, Netflix and TikTok have built highly personalized recommendation engines, and Google procures, prioritizes, and presents the world’s text, image, and video data on a simple and minimalistic website. What makes them successful: these companies are ruthlessly data-driven right down to their hiring practices.

Utilizing Big Data, Machine Learning, and Deep Learning can help us improve productivity, prevent losses, enhance engagement and participation, personalize, and predict.

However we need to build the core processes to give way to meaningful algorithms including gathering and organizing data, discovering and applying automated algorithms, and if required, sharing data with partners or publicly to collaborate and build other valuable services, new functionalities, and layers of innovation.

4. Leverage non-mission critical assets

We have lived in the sharing economies and collaborative consumption eras and are now transitioning to web3.0 and a decentralized world— we don’t need to own capital-intensive assets any longer unless they are mission-critical to the business model to create differentiation or serve as barriers to entry such as Tesla’s ownership of its battery factories or Amazon’s fulfillment centers. In other words, if an asset is somewhat commoditized, access trumps ownership.

Some companies outsource their mission-critical assets, such as Apple which has several important partnerships including that with Foxconn. In the age of communication, Apple can access physical assets anytime anywhere around the globe.

Being light on financial assets helps exponential organizations become flexible, nimble, agile, and focused on their core value propositions and this allows them to meet and exceed expectations and scale quickly and rampantly.

An “agile” or “nimble” organization is designed for both stability and dynamism in a VUCA environment

It is a network of teams within a people-centered culture that operates in rapid learning and fast decision cycles which are enabled by technology

It is guided by a powerful common purpose to co-create value for all stakeholders

Such an agile operating model has the ability to quickly and efficiently reconfigure strategy, structure, processes, people, and technology toward value-creating and value-protecting opportunities

An agile organization adds velocity and adaptability to stability, creating a critical source of competitive advantage under VUCA conditions

5. Build virtuous and positive feedback loops

As communication barriers have dropped and social platforms have taken lives, exponential organizations have seized the opportunity to improve engagement with customers by building gamified features such as digital reputation systems, likes, Emojis, etc. that provide the opportunity for virtuous, positive feedback loops. These loops in turn allow for faster growth due to the availability of more innovative ideas while also raising customer and community loyalty simultaneously.

Gamified engagement allows collaborative and playful social behavior to flourish and with collaboration at scale, individuals can now do what once only large centralized organizations could. Everything that an organization does is to create value for humans, and their engaged feedback will help organizations ride megatrends and withstand disruptive storms.

Exponential organizations use engagement interfaces that help with:

  • Ranking transparency and social comparison such as a leadership board
  • Gives consumers a sense of control, agency, and impact
  • Elicits positive behavior and long-term attitude change
  • Instant and short feedback cycles
  • Clear and authentic rules, goals, and rewards
  • Virtual currencies or points, especially in the age of Web3.0 and digital currencies

Aligning internalities to improve productivity

As an organization scales exponentially, its internal organization and processes need to become robust, precise, and properly tuned to process all the input that is pouring in from the external marketplace. This will require distinctly different internal operations that encompass mindsets and business philosophies, communications and interactions, performance tracking, and risk management.

All of the internally action items are to improve organizational performance and productivity to help the organization scale with the wave of intelligence that is incoming from externalities
All of the ‘internality’ action items are to improve organizational performance and productivity to help the organization scale with the wave of intelligence that is incoming from externalities

1. Build self-provisioning platforms

Filtering and matching processes are required to help deliver data to internal control frameworks and the right people at the right time. In many, if not all cases, these processes start as manual and gradually become automated algorithms and workflows that serve as self-provisioning or self-serving platforms as the organization scales. These automated processes result in more effective and efficient operations with reduced margins of error.

Most of these self-provisioning platforms are unique to the organization that developed them and are deemed as intellectual property of considerable market value. To work and function well, these platforms and automated processes require a great deal of human-centered design thinking to optimize every instantiation — meaning, just as an organization thinks about building products that external customers love, the same level of attention needs to be paid to building the internal platforms.

These self-provisioning platforms empower the enterprise to manage at scale. An example is Apple’s App store, which now contains 1.8 million apps that have collectively been downloaded more than 130 billion times and with ~30 million developers that made $260 billion from July 2014 to January 2022. To manage this massive ecosystem, Apple uses three critical levers as a self-serving platform:

  1. Business health: an editorial board that vets news apps and change requests as well as recommendations from employees — design choice: semi-automated
  2. The consumer side of the marketplace: recommendation algorithms for the homepage to maximize engagement and sales — design choice: automated
  3. The developer side of the marketplace: communication about new policies and features at the WWDC conferencedesign choice: non-automated due to the B2B nature

Another great example of how exponential organizations use algorithms and automated interfaces to build a self-provisioning platform is how navigation software such as Waze or Google Maps project ETAs. The more an organization automates internal operations, the more time is freed up for employees to solve more challenging problems and deliver new and innovative solutions that can compete in the marketplace.

2. Utilize dashboards and OKRs

Given the large amount of data available to exponential organizations to measure and manage the organization with, real-time, adaptable, and customizable dashboards accessible to everyone, are essential. An exponentially oriented organization needs fine data collected across its value chain of acquisition, operations, monetization, loyalty, and repurchase.

Growing at a rapid pace requires that measuring individual and team performances are integrated and carried out in real-time as small mistakes can become a major bottleneck, very fast.

Utilizing data science tools, organizational intelligence should become real-time, refined, and visualized, empowering individuals and teams to self-manage. This essentially will push an exponential organization from traditional forms of top-down driven performance tracking KPIs to the Objectives and Key Results (OKR) methodology that tracks individual, team, and company goals and outcomes openly and transparently focused on:

  • Where the employee should be heading? (Objectives)
  • How well are they progressing towards their goal? (Key Results to ensure progress)

The key advantages of OKRs are:

  • OKRs are bottom-up driven while KPIs are top-down. In effect, OKRs create a sense of empowerment, autonomy, and accountability which is key to employing founder-minded employees to scale exponentially
  • Objectives and key results as measures of success, help employees build a vision, break down efforts, and problem solve along the way
  • Objectives are built to push employees out of their comfort zone
  • In practice, a total of 5 objectives, and 4 key results per initiative are set with an expected achievement rate of 60–70%
  • The frequency of setting OKRs can vary and are reflective of the projects and goals in mind, but they could be set every week (high-frequency), bi-week, month, or even quarter
  • With OKRs, feedback cycles are short, frequent, and specific that drive behavior change, minimize errors and their costs, and are impactful as they energize and motivate morale

At Google, all OKRs are completely transparent and public so that employees get a transparent view of what each unit and member of the organization is working on and towards, making conversations, interactions, and collaborations specific and meaningful while aligning everyone to the organization's transformative purpose. At Google:

  • Everyone has individual and group objectives (Os)
  • Everyone has individual and group KRs
  • Everyone creates OKRs quarterly
  • OKRs are reviewed and scored every quarter to create accountability
How Google sets goals: OKRs / Startup Lab Workshop — Google Ventures

3. Experiment, experiment, and experiment more

The biggest risk is not taking any risk. In a world that’s changing really quickly, the only strategy that is guaranteed to fail is not taking risks — Mark Zuckerberg

Based on a large body of work including The Lean Startup by Eric Ries, experimentation is defined as implementing the Lean Startup methodology of testing assumptions and constantly experimenting with controlled risks.

The Build, Measure, Learn cycle is the key component of the Lean Methodology to gather and analyze feedback, first prested in the Lean Startup Book by Eric Ries. The process is all about learning fast, building fast, gathering data, and measuring performance against hypotheses quickly — read more here

In an ever-changing market environment, constant experimentation and process iteration are the only ways to reduce business risks. Large-scale bottom-up idea generation if assessed, filtered, and backed properly — termed “scalable learning” — will trump top-down thinking in any industry and/or organization.

Focus on how to motivate people to learn faster, how to foster practices that drive faster learning and how to create environments that amplify the potential for learning — John Hagel

And as most digital markets are winner-takes-all markets due to network effects, this makes a culture of continuous experimentation even more important. Building and growing partnerships and sharing data in the ecosystem will be key to expanding the scope for experimentation to better understand consumers and delivering new and innovative solutions. Experimentation allows organizations to continuously improve and it needs to become baked into the organization’s culture and DNA.

The modern rule of competition is whoever learns fastest, wins — Eric Ries

Failing fast and often while minimizing waste should be implemented at every organization with agile project management processes at the forefront of testing new ideas and offerings. When failure is not an option, corporations end up with safe, incremental innovation, with no radical breakthroughs or disruptive innovations, and get left behind as new players shift market paradigms.

The problem in legacy business environments is that failure, more often, results in severe financial consequences due to long lead times and large investments driven by waterfall product development mindsets, which reduces the overall risk appetite. Furthermore, sunk-cost bias also decelerates latent and dynamic motivation for experimentation, and companies find themself spending money launching doomed products despite the existence of data that indicated the failure beforehand.

Killed by Google, while to some may represent wasted capital, is a representation of an exponential thinking organizational culture that is fine with failure, encourages it, and invests in innovations, despite eventual project deaths. At an exponential thinking startup/organization, it’s key to celebrate failures, as employees and members need to be understanding or be “Ok” with failing, otherwise, it’s unlikely that a scalable and economically viable business model will be uncovered.

Experimentation helps an organization become exponential as it keeps internal processes in check with external changes, lowers development and marketing costs, accelerates time to market for feedback, and builds a culture of risk-taking that is at the core of healthy competition.

4. Empower through autonomy

Empowering an organization with autonomy can be described as creating self-organizing and multi-disciplinary teams that operate with decentralized authority (somewhat similar to what Blockchain does). Autonomy is an important prerequisite for permissionless innovation in exponential organizations.

Moving from hierarchy to autonomy — an autonomous organization looks and feels very much like an ant colony or a megacity while a hierarchical one, feels like a military base, and this social organization structure is the reason why megacities scale to millions of inhabits while military bases, don’t

In an autonomous organization, we move from a hierarchical machine-like corporation to a company that very much resembles complex organisms such as our bodies or a megacity such as New York. Consequentially, as an organization becomes flattened with independently functioning teams, bottom-up-built OKRs will replace top-down-driven KPIs, as discussed earlier.

With a constantly changing landscape, the need for autonomy and decentralization is exacerbated by increasingly critical and knowledgeable consumers who expect zero latencies and are quick to complain online if their expectations are not met.

Autonomous organizations distribute control and authority, are dynamic and flexible with openness towards change, employees are the highest authorities in their roles and great team collaborators, tensions become fuel for progress and problem solving rather than a bottleneck, and team members generally have high motivation to take initiative, leaders do not bear the full responsibility of decisions made, and upon failure, the organization learns together and moves forward. Autonomy should deliver increased agility, efficiency, transparency, innovation, and accountability.

With autonomy, there are still hierarchies within the network of teams, but they tend to be competence-based, relying more on peer accountability than on authority-based accountability. This means that accountability is towards members that are specialized, rather than to positions, regardless of competence levels. This approach is a change in the role of the manager.

How Haier Works | Corporate Rebels — the story of how Haier transformed itself under its rebellious CEO, Zhang Ruimin, from a hierarchical and machine-like manufacturing company to an agile and autonomous organization, deserves attention

An example of an ecosystem that works and creates with autonomy is the movie and entertainment industry in California. In Hollywood, actors, directors, music composers, photographers, agents, writers, etc. manage their careers as individual business entities as it is the nature of the movie industry to be a series of discrete projects that require quick turnarounds and agile experimentation. As these singular self-managed business units aggregate in a location such as Los Angles, they can build complementary business networks and cumulatively create massive value. This same autonomous ecosystem and organizational model is the key driver of success in geographies such as Silicon Valley, and Wall Street.

5. Build a real-time productive organization

To become an exponential organization we need to remove communication and collaboration latencies and accelerate decisions and learning cycles while infusing a culture of openness to cooperation.

If any organization desires to scale, it needs to reduce the distance between obtaining information, processing it, and decision making. And there are plenty of social communication and collaboration tools in the market that will help build internal connections, engagement, trust, and transparency.

Several fronts can help an exponential organization improve collaboration through productivity and communication tools including virtual communication, file sharing, task management, event planning, etc.

The advantages of exponential organizations

1. Accelerated knowledge metabolism rates

With data, analytics, and AI-driven automation at their disposal, exponential organizations can quickly digest changes in their market environments and respond with intelligent solutions that create winner-take-all markets by unlocking and utilizing network effects while living with disruption as a norm. In essence, Exponential Organizations are 21st-century resilient corporations or social entities. At Exo, everything is measurable and anything is knowable.

2. Reduced operating costs

The onset of the digital era reduced marketing costs significantly and expanded the market reach for businesses. Economies of scale through globalization and lowered demand side operating costs helped democratize access to resources for consumers across the globe.

3. Truly people-oriented organizations

As the market environment becomes more competitive, the less we will see ‘expert’ employees, as there is little time to gain knowledge so quickly across fields, and 5-year financial plans will become obsolete as forecasting the future will be difficult. However, we will witness the rise of truly empowering organizations that recruit fit talent, and trust them to problem solve on the go. It needs to be pointed out that the skills required from employees at Exponential Organization will be different from traditional hierarchical organizations and will require agile thinking and cross-functional collaboration.

Furthermore, exponential organizations have their consumers’ experiences and needs on the top of their agenda and utilize all technological tools available to understand and deliver on their consumers’ desires. By being open source and creating a trustful business environment for data sharing and collaboration, exponential organizations continuously get access to knowledge and insights that beat closed social and corporate business ecosystems.

4. Big, but nimble and agile

Exponential organizations, no matter how large their scope of activities, keep their structures flat and self-organized to remain lean and quick to react to market developments while taking many projects and diversifying corporate risk. They also outsource non-critical parts of their value chain to keep the focus on the core of their IPs while reducing operational costs.

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A conversation around how a startup can move towards scaling

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