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The Economic Entanglement Of Business Ecosystems

An Age of Transition

The Economic Entanglement Of Business Ecosystems

In this transitioning age, the physical ecosystems of the past are upgrading to the digital ecosystems of the future. Prior to the beginning of transition from physical to digital, many industries of the past had some sort of entanglement upstream, midstream and downstream with suppliers and distributors, competitors – indirect and direct, and customers albeit business-to-consumer (B2C), business-to-business (B2B), and/or business-to-government (B2G).

Thanks to some big bangs in technology over the last twenty years, particularly over the last decade, the landscape shifted to where cross-border data flows (digital) create more economic value than traditional flows of traded goods (physical).  All of a sudden, in order to compete and stay relevant, many companies have had to adopt a digital strategy. This monumental need drove a remapping of the ecosystem.

Today, the ecosystem looks more like an interrelated web with a focus converging on software. In a way, business ecosystems are morphing into technology ecosystems. Marc Andreessen famously stated “software is eating the world” and proclaimed that all companies will eventually become software companies, with the best one in each market winning. Those prophetic words are unfolding as the physical and digital worlds blend.

Today, software defined architecture and modularity between ecosystems matters to compete. Because of that, the new entanglement is more focused on digital platforms and applications (which can make them more valuable) than the physically-dominated ecosystem of the past.  Much of this has to do with the need to create new environments to keep up with technological accelerations as well as the market premium for companies that offer such platforms, also referred to as network orchestrators. Below is a visual summary from New Enterprise Associates in a slideshow about innovation physics.

Industrial Revolution(s)

Since 1771, there are said to have been a number of industrial revolutions. Some researchers say four, some say five, the point is moot, as the number depends on how history is organised. Carlotta Perez did extensive research on the fundamentals of industrial revolutions through the prism of technology and financial capital. Her research suggests we are living in the ‘fifth technological revolution’.

The first technological revolution was the Industrial Revolution that took place in Britain from 1771-1829. The second was the Age of Steam and Railways that also took place in Britain and spread to the US from 1829-1873. The third was the Age of Steel, Electricity, and Heavy Engineering and took place in the US and Germany as they caught up with Britain in 1875-1918. The fourth had an overlap with the third: the Age of Oil, Automobiles, and Mass Production took place in the US and spread to Europe from 1908-1974. And the fifth similarly overlapped with the fourth: the Age of Information and Telecommunications, which also originated in the US and spread to Europe and Asia from 1971 to the present day.

High-level Phases

Importantly, within each of the aforementioned technological revolutions, which each took place over about 60 years, there are two ‘high-level phases’ and two ‘sub-phases’. The high-level phases refer to the first 30 years, a technology installation period, which abruptly halts with a financial crash, which is then the turning point for institutional recomposition and a subsequent 30-year period technology deployment. It is important to understand the symbiotic relationship of technology and capital as it does change through each phase.


The sub-phases of the installation period are called ‘irruption’ and ‘frenzy’, whilst those of the deployment period are ‘synergy’ and ‘maturity’ – each lasts approximately 15 years.  It is worth mentioning that not everything happens so linearly: for example, multiple irruptions and frenzies can occur over the course of a 60-year technological revolution, as will become clear shortly.

An irruption occurs when there is a passionate relationship between financial capital and emerging production capital. Entrepreneurs crack the code on new ways to make profit and do so in a bigger way than market incumbents. The result is a rush of financial capital to the support more similar innovation.

The market’s incumbents (old production capital) face diminishing returns and financial capital flight, and a techno-economic split begins.  Next, the frenzy phase occurs and decoupling begins to happen. The early successes of the new drive a departure from the old – the divorce of production and financial capital begins to settle. Market incumbents (the old) set investments of production capital towards innovation and new products as investment trends are in part being led by financial capital.

Together, this duplicated allocation sums to a paradigm shift, and the result is a ‘new economy.’ Bubbles begin, inflation and debt soar, and the bubble bursts. During a collapse and recession, financial and production capital re-establish their once complementary relationship and allow production capital to take the lead again during institutional recomposition. Then then synergy phase happens: the renewed linkage results in real growth and real dividends.

Financial capital continues to flow and complement production capital. Lastly, the maturity phase occurs, and there is disappointment as productivity and market growth slows. Financial capital looks for new ways to drive returns and seeks making loans to emerging markets and technologies – the latter fuels the next irruption.


The fifth ‘Technological Revolution’ began in 1971 when the IT industry began to take off.  In the 1970s, mainframes were the provider of back office automation. In the 1980s, client servers were the provider of front office productivity. Where the gains of the 1970s and 1980s had a tilt towards businesses, it was in the 1990s that IT broke loose, once it saw mass consumerisation catalysed by the web’s facilitation of e-commerce and e-mail.

Though there was a dot com bubble and burst, the industry found synergy with the rise of the cloud in 2007. The cloud birthed business ecosystems as it first helped lines of business via self-service. And it came at a good time, because 2008 saw the Great Recession. As the dust settled, the sharing economy took off around 2011, affecting communities via social engagement. Next came the Internet of Things hitting the mainstream in 2014, which brought about huge advances in real-time optimisation.

By 2016 it could no longer be ignored that companies needed a digital strategy to help unify people, devices, and machines. At this point many market incumbents adopted digital business transformation initiatives in order to stay relevant. According to Accenture, “Digital is the reason just over half of the companies on the Fortune 500 have disappeared since 2000.”

Finally, 2017 is seeing artificial intelligence as a mainstream enabler to help make sense of Big Data. Though there are many approaches, the two most popular  methods are machine learning and deep learning.

… and Now

This walk in history involved just over 35 years of technological installation – both hardware and software – then a turning point, marked by a recession, and a technological deployment now. Today’s phase is largely the synergy phase: production capital is leading financial capital.  And markets are driving towards industry convergence as new technologies are being applied to age-old business problems.

At the same time, production capital led to a record year for tech mergers and acquisitions (M&A) in 2016. Internet of Things deals led the way with $103.4bn according to Ernst & Young, which marked the second year in a row that global technology M&A led all types.

Important Breakthroughs

The innovation physics that irrupted the information age consists of a continuous cycle of progress and development in the four C’s – computing, consumption, connectivity, and componentisation.  In all cases, as time went on, costs fell immensely, enabling consumerisation of multiple technologies.


Innovation happened inside ecosystems. To understand an ecosystem today, it is important to understand the construct of one. Companies that have gone digital or were born digital rally their business value around their platform. First, in its simplest form, a multi-sided market creates value through software, applications, or services by facilitating direct interactions between parties on multiple sides. That platform will have a core interaction which includes its participants, the value unit, and the filter. As a platform grows, new types of interactions can be layered on top of the core which assists growth. For a company to understand where they live in the ecosystem in the digital economy, they must consider how they interact with other platforms and/or whether they have their own.

A key enabler for an ecosystem is information storage – in the modern day, that storage is the cloud. Data storage in the cloud has become incredibly cheap, secure, and gained wide public adoption as information flows could be handled with confidence. Some of the specific attractions were smartphones with 4G network speeds – consumers bought in heavily and gained the bandwidth to move information seamlessly from more locales.

While storage was important, another key breakthrough had to do more with the process of doing work itself – that process is open source.  Open source was a process catalyst because it allowed for modularity in software. At a time when large corporations had closed source systems, they learned that innovation could happen faster if they moved to open source processes. This had a lot to do with developers wanting control and proving, on their own Linux systems, that with control they could do a lot more together than corporations could do in their siloes.

Of course none of these innovations would have happened if there was not some sort of capital support to get ideas off the ground. Worldwide financial assets have been building steadily: in 1990, they were 6.5 times global GDP, in 2010 when they were 9.5 times GDP, and they are projected to be greater than 10 times GDP by 2020. The age of information and telecommunications goes hand-in-hand with the age of superabundant capital. Capital could not fuel innovation without being widely plentiful and available.

The Principles that Matter Most

As in nature, ecosystems have a lifecycle and feedback loops that require nurture for sustenance. The Deloitte Feedback Loop is perhaps the clearest depiction of what the Internet of Things ecosystem looks like with respect to raw aspects that drive value. In this ecosystem, there are multiple parties (i.e. companies, regulators, and academia) that are in one or many parts of the value loop.


This is where it gets interesting. The intertwining of technology and capital with social networks was studied extensively by Dr Ted Zoller, who studied the density of social networks as related to raising money for new ventures. He pioneered a dealmaker’s algorithm, where dealmakers are those that have equity in three or more companies concurrently. Through examining places in the US, he found that those with a cohesive social capital network of serial entrepreneurs and serial investors tend to build an economy that creates new firms.

In deconstructing the makeup of any new venture, Dr Zoller emphasizes a Stochastic Venture Model for new business ventures (which is also relevant in a time of transformation) with five ‘M’s: meaning, market, model, management, and money. The following is a quick explanation with some context for today’s environment:

The Five Ms

Meaning: This is the value proposition for any new venture. This is typically the identification of some sort of pain point and a solution that resolves it.

Market: This is a customer development phase where a new venture seeks to determine if there is a pull for their solution, if there is a market validation (perhaps competition), if there is a logical segmentation to pursue, and lastly if how to differentiate and go to market (a beachhead).

Model: Business models of the past are changing, and there is a need to re-think how and where revenue can be made. The Harvard Business Review concisely summed the differences below.

Management: On the non-technical side of the management part of the equation, there is a fundamental need for a leadership that can exemplifies the following five skills: innovative and adaptive thinking, virtual collaboration and social intelligence, the ability to work across disciplines, literacy in different types of media, and computational thinking and analytics.

On the technical side, especially as a company seeks to develop a new product, there are new decision frameworks to work through in IoT; here is a product roadmap in practice. It is important for leadership to have good fundamental understanding of the entire stack with expertise in at least one layer in the stack, to have good understanding of how to shepherd processes, and to understand the big picture of where the product fits. All points are weighted equally, however, the last point is helpful as an organisation uses modern methodologies (like Agile, Scrum, rapid prototyping, and so on) in their feedback loop to find a fit between product and market.

Money: There are two parts to this: first, how a new venture will actually make money; second, new venture financing to launch (i.e. conventional, bootstrap, strategic, angels, venture, institutional, and so on). To take two important types of available sources of capital – corporate cash in the S&P 500 was at $1.456trn as of the end of the second quarter of 2016 and private equity fundraisings in 2016 set an all-time record at just under $1trn. Importantly, this is not to say that $2.5trn is available for innovation, as some will certainly go toward market consolidation activities and other similar things. But it is astounding nonetheless to see such high levels of capital available for deployment in general.

… and the Sixth

There is a sixth, bonus ‘M’ is maturity: it relates to a market incumbent doing a new venture and the management team’s experience (examples would be GE Digital for a new venture, and Otto (the former Google self-driving car team) for experience management).Maturity’ also refers to where a new venture lives and exists on a particular path. If a new venture lives in a large organisation, all of the previous 5 Ms have a slightly different look compared to a new startup that is trying to launch a venture. Maturity refers to a point in the journey where the venture is on a spectrum from immature to mature – if it has been around for a while and gained momentum and grown, it is closer to mature and the 5 Ms might feel different than if new.

Getting to ‘There’

The spheres of change are economic, institutional, and technological. And despite much of the discussion having been about the role of technology and finance as related to the cyclical progression of the economy and its current state, the three things needed to run any company are people, process, and technology. The following specifics of each are current or emerging trends of what it will take to thrive in the ‘new economy.’

People: ‘cross-functional’ is perhaps the most concise way to describe the most mobile type of worker needed in future organisations.  Getting people with the right skills with some domain expertise and general awareness of how technologies and systems work in other domains are highly valuable, as business models evolve.

The most important quality, given the accelerating adoption of key technologies like AI, IoT, and blockchain, is disciplined curiosity. What that means is identifying individuals who have the ability to filter the signal from the noise but have the passion to look. The implications of those types of individuals is they will be more likely to lead by example to be the life-long learners that will be more required for the ‘new economy’ than ever before. This type of person might pursue more education – formal (degree) or informal (nanodegrees, classes, training) – in order to keep up.

Process: removing friction is the means to generate speed. This is found at a high-level with niche process catalysts to help new ideas form to businesses like incubators, accelerators, and invention networks. It is also as important with mature ventures.

Business process management should be considered in a digital manner with easy, simplified, low-code methods for companies of all digital maturities. Other elements of process include analytics – data-driven decisions should be the chief type of decisions made within any organization. Incumbents, depending on the industry, are more likely to be on the low end of the spectrum versus companies that were born digital with the benefits of modern processes and technology at their quick disposal as opposed to older companies who have had to evolve.

Technology: this means constantly monitoring progress. Keeping up technology can help shape organisational strategy. This can be done in a few ways: following the money (investment trends), following the patents (new technology trends), or following the research (academic trends). By getting ahead with monitoring technologies, an organisation can accomplish an overarching goal of not letting technological change result in a reactionary stance within leadership, rather a proactive stance to best plan and adapt for the future.

The Future State

If futurists like Kevin Kelly are right, then businesses are headed for a future state of even more technological dependence but also more intense productivity due to expected efficiencies. An optimistic view is that people, machines, and rules and regulations will all balance each other in ways that have never existed before. Right now, there is still much ethical debate about what technology should or should not be allowed to do, and that will continue. Ideally, technological innovation will continue to lead regulation and not the other way around – the reverse can stifle progress.

The ecosystem will continue to evolve and markets will reshape with new technology innovations and implementations. It would be a wonder if some day there were a Cloud of Clouds, a blockchain of blockchains, a Deep Learning Neural Net chain of chains, and others, as connective tissue to the vertebrae of interconnectedness grows all in a quest for the next killer app and market consolidation. Perhaps the modularity between all will play out with similarities to that of a Fund of Funds in finance.

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