Blockchain. Innovation. How we got both of them wrong.
By Kyle Ballarta on ALTCOIN MAGAZINE
An exploration of phase transition and self-reproduction in technological systems
It is 2019 and many have been witnesses to a crash in the crypto space that some expected, others abnegated, and many dreaded. This comes in a time in our world where technology has reached a new level of abundance — compounding its sophistication in accelerating rates and connecting the world to access and resources like never before. As many debated the trajectory and legitimacy of digital currency, a common place was found in the potential of blockchain technology. We all recognize the potential of an immutable public ledger as a way to democratize, create access, and create transparency in a world that continues to become more unshackled from its inefficient systems. The real questions that we all have, and where the debate remains, is how this will all come to life with the infinite amount of applications possible.
The crash of the crypto market not only made us rethink about the way we view and approach blockchain, but more importantly it brought to light how we have an incomplete view and approach to innovation in general. Of all Initial Coin Offerings (ICOs) that were listed in 2018, more than 50% of them failed within the first 4 months. Much of the value in the upswings of the crypto markets was mostly created by speculative values and as a result much of the correction in the markets were because of a lack of true utility value (amongst other factors). In such a nascent space, a new category, we have brought with us the view we have always used in venture innovation into a space that still is without an established ecosystem. The same approach that we used to build dating apps, marketing SaaS platforms, and flavored sparkling water cannot, and will not, work in reaching viability or building a new category such as the crypto space. No wonder the bubble has burst. The old view, as it is taught in business school, is based on finding the product/market fit, analyzing the competitive landscape, understanding the total addressable market, and optimizing customer acquisition- focusing purely on product and developing the widget as its end goal. The challenge with such a narrow view that is focused solely on the development of the product is that it fails to account for co-innovation and co-adopters that are interconnected in a value network system. Building a product alone does not provide a solution. It requires other stakeholders like distributors, channel partners, regulatory bodies to deliver that solution to those who need it. It equally depends on other technologies, materials, and sourcing to develop a completed product. You can have all the demand in the world, but if you cannot develop it, or deliver it, the solution and value network is not commercially viable. Every innovation value network varies in composition and complexity but these are interdependencies that determines viability, among other things like defensibility, scalability, and adaptability. Blockchain on the other hand naturally requires more than the novel approach of building a widget. It requires one to be more conscious and aware of other stakeholders involved regarding token economics, shared value, cooperation, circulatory exchange, and multiple other dimensions of factors at play. Blockchain not only requires the innovator to be mindful of these interdependencies within a value network system, but requires a focus on actually solving a problem as its end goal- this is where value is created.
Innovation is made up of value networks of interdependent stakeholders that ebb and flow in evolution and adaptation — all of which jockeys to deliver the most optimized solution to a problem. A product is only a medium for delivering that solution. It is temporary. A technology that is created without the incorporation of this system of relationships is not focused on solving a problem. It is this arbitrary creation that has been one of the reasons why blockchain projects resulted in speculative value and a major down-market correction. Blockchain technologies that focuses on addressing a real challenge, away from the arbitrariness of building for the sake of novelty, naturally requires the innovator to create in systems. This lesson we have learned from blockchain has not only shed light to shortcomings in the crypto space, but the shortcomings in our approach to innovation as a whole. It is creation in the pursuit of solving problems, not the arbitrariness of creating technology, that creates value, and widening our lens to think in systems rather than solely products delivers that value. There is tremendous value in solving systemic challenges, but systemic challenges require systemic thinking and approaches.
“There is tremendous value in solving systemic challenges, but systemic challenges require systemic thinking and approaches.”
It was in the 18th century that a Prussian polymath named Alexander von Humboldt found his way to the virgin lands of South America through the good graces of King Charles IV of Spain. It was this fresh new frontier that extended much of the European sovereign power across the globe, but it was in the uncharted and unknown reaches of these lands where these powers had no reign. It was his observations as a naturalist that inspired people like Charles Darwin to follow in his footsteps to the new world to make discoveries that would later build the foundations for significant scientific theory such as Natural Selection. Through Humboldt’s exploration of Cotopaxi in the Andes mountains, he started to observe that plant and animal life in environments of different elevations varied uniquely. As he ascended up Cotopaxi he would begin to encounter different plant and wildlife that was varied from ones that he have previously encountered along his trek. It became clear to Humboldt that flora and fauna species in nature varied so differently because they were interlinked with their environment and external factors such as climate and altitude. Degrees of variations in climate, altitude, and light affects the outcome and interdependencies of plant and animal species, resulting in this observation of unique systems. A change of how we looked at our world and the sciences was born. Before Humboldt the study of Natural Philosophy (as it was called) compartmentalized and organized biological life solely on phenotypic characteristics, separating and isolating different aspects of life into a prosaic and banal world. It was when Humboldt made his observations and created his Naturgemalde(Fig. 1) that we started to view and organize our world in connected, interdependent systems. Humboldt’s Naturgemalde illustrated life that varied from region to region because of each system’s unique interdependencies. In his Naturgemalde a number of additional factors and forces were incorporated as contributors to how life is shaped, integrating factors of gravity, heat, light, electivity, magnetism, and chemical affinity. Alexander von Humboldt’s observations and contributions framed our world as one that was no longer studied in isolation, but a world that was made up of interconnected and interdependent systems.
Our world is not only made up of the ecosystems that Alexander von Humboldt observed in South America, but they can also be observed in so many other events and disciplines such as morphogenesis, ontogeny, genetic regulatory networks, even economic activity and urban city evolution. The reason why our world ebbs and flows is because it is the oscillation between interdependent stakeholders within interconnected systems. The same interconnected dynamics hold true in the systems that are necessary to translate invention into value in the process of innovation. Knowledge and technology alone does not create value, but it is when that technology fulfills the functions across a system of interdependencies that it converts into value. This can be observed in examples in my previous article about value network systems that illustrated varying approaches to developing the electric car between Tesla and Fisker Motors. Fisker Motors spent much of its time and energy in developing a well designed product. In the end, the company filed for bankruptcy, and the Fisker name is rarely known to mass consumer audiences today. How could such a beautifully designed product fail to succeed? Tesla on the other hand not only focused on developing an electric car as a product, but they were conscious of its value network system. Tesla redefined how they delivered product to the customer, circumventing the dealership model, and going direct to consumer. Tesla also redefined how they sourced materials and produced lithium batteries with the development of new manufacturing, supply chain, and the gigafactory. One approach focused on invention of a product alone which resulted in a failed attempt. The other approach developed a viable value network that created alignment across interdependencies in a system. This ultimately created a new category for electric vehicles and renewable energy. Fisker’s approach is beginning to sound a lot like how we approached cryptocurrencies. Maybe we need to consider a Tesla value network approach to blockchain, and innovation as a whole.
“If we were to create similar levels of visibility to the process of innovation, how would these observations impact the intentionality of how we invent?”
If we hold to be true that the process of innovation occurs in systems, that it requires the alignment of interconnected factors and stakeholders, what else can we learn? Just as Alexander von Humboldt brought a new lens to how we look at nature, away from narrow lenses that were pursued and studied in isolation, what if we created our own Naturgemalde lens of how we look at technology innovation? What observations could we make, and what actions and intentions would those insights inform? Currently, sophisticated next-gen sequencing has increased our visibility on genome composition, and as a result of that visibility we are able to be more intentional about how we affect positive desired outcomes. If we were to create similar levels of visibility to the process of innovation, how would these observations impact the intentionality of how we invent?
Like in nature, technology innovation is a stochastic process that occurs between the framework of exploration and exploitation. Systems in nature, and in innovation, explore unstructured possibilities, but opportunistically exploiting and responding to positive feedback just as ants respond to trails of pheromones to optimize a colony’s food forging, or even the positive feedback of a proven hypothesis in innovation that leads to incremental sophistication. In these complex system there are no certainties and no absolutes, but it is a constantly changing world of probability and risk. All an innovator can do in the process of innovation is maximize probability of knowns and mitigate risk of unknowns. Like nature, innovation has its own natural selection. As new iterations are spurred, informed by external pressures such as competition, cooperation, scarcity, abundance and a number of other factors. New technological adaptation and iterations are reconciled against dominant systems and are selected based on its fitness to relevantly contribute and receive value. Dependencies on other technologies, co-adopters, and market timings can be major contributing factors whether these technological adaptations find viability. It is within the scientific method of exploration and exploitation, iteration and feedback, chaos and order, that innovation sophisticates and naturally selects these fits. It is the rate of iteration that determines probability and the alignment of external factors that determines the timing of potential viability. This same framework that innovation experiences is observed in the natural selection of evolving systems found on levels ranging from bacteria, to single-celled organisms, to super-cooperative systems like cities, economies, and markets. When studying systems in other disciplines we understand that the systems that are viable, that are resilient, adaptable, and growing, occur when they reach a state where they become self-reproducing. When you get a cut or scrape on your skin, your body’s system activates blood platelets that amass and self-reproduce at the laceration to scab to develop new skin. In ontogeny a single fertilized cell, or zygote, starts to self-replicate to levels of complexity that specialize into organs and phenotypic characteristics (Fig.2). The city of New York, started off as a small Dutch trading colony named New Amsterdam. This colony grew to thresholds of interdependencies that reached self-reproducing characteristics that still impacts the way the city recreates itself in a decentralized way. Innovation is very similar as it starts off with a single-cell idea that incubates, and through trial and error, exploration and exploitation, it finds a fit amongst complex systems like value networks. The real questions that we still have in the process of innovation, is how do we create systems that are self-reproducing. How do we innovate in a way that is conscious of the relevant system that maximizes adoption, reception, and ultimately growth. In venture innovation, many call this “crossing the chasm”. It is understood that there is a certain threshold to cross, such as this “chasm” that results in adoption or the acceptance in the market. You have seen companies like Coca-Cola, Louis Vuitton, and Nike that have reached threshold levels of adoption where customers have developed an identity with the product and become stakeholders in how the brand evolves. You have seen platforms like Amazon, Google, and Youtube that have reached levels of adoption where its users are equally stakeholders in how the system evolves and influences others. These are self-reproducing and auto-catalytic systems that continues to grow beyond a single invention. It is a new threshold in the system’s adoption that starts to illustrate levels of alignment that causes the system to fulfill unto itself. In technology innovation, we try to achieve this to maximize the way innovation is diffused amongst markets and adopters. The problem that we have with an isolated and narrow view is that we are guessing where that threshold occurs. In a world that is stochastic within the frameworks of exploration and exploitation, one that arbitrarily guesses (and a percentage may get lucky) cannot be intentional about the use of finite resources, such as energy and time that is used to achieve viability. Understanding where this threshold occurs can accelerate the rate and the probability in which a technology is diffused and accepted amongst its adopters — ultimately putting the wind in your sails. The good news is that systems that reach this threshold of phase transition, that become self-reproducing, have similar characteristics whether we are talking about morphogenesis, genomics, city growth, thermodynamics, society, and yes, even technology value networks. You never know, you might be five feet away from gold. Wouldn’t you like to know?
The lifecycle of a technology explained in innovation science, or even felt viscerally on the rocket ship ride of entrepreneurship, can be explained in a sigmoidal diagram (Fig. 3.1) most commonly known as ‘The S-Curve’. The S-curve explains the rate of growth and performance that occurs during the lifecycle of a technology ranging from inception all the way to its sustainability, or its entropic decline. The x-axis is used to describe duration of time, and the y-axis illustrates performance sophistication that occurs over the lifecycle of the technology. The degree of the slope along the lifecycle illustrates rates of sophistication in different stages of its maturity. In stages of ideation and incubation it can be observed that the rate of sophistication occurs at a slower rate, illustrating dynamics of open exploration and iteration to properly define a solution. It is in the early and commercialization stages that exploitation, and refined iterations start to develop into a solution fit. Signs of increasing rates of performance can be observed at this stage. The highest rates of growth occurs at a stage where a technology is at scale which is witnessed at the largest degree of slope on the S-curve diagram. This sigmoidal diagram illustrates the evolutionary dynamic that a technology system experiences in the process of its optimization. What is also an interesting observation is that the same sigmoidal diagram can also explain the dynamics of other complex systems like the evolution of species within biology. In nature, very often new species origination occurs because of allopatric speciation that occurs on isolated islands or environments (e.g. Darwin’ finches). This dynamic can also be illustrated with the same S-curve that illustrates the system for technology. The dynamics of allopatric speciation occurs within a safe place, without predators, with the ability to iterate and explore (like incubators, R&D labs, etc). As species evolve and sophisticate in relationship to a relevant system, they continue by fulfilling niches in existing ecosystems in the form of adaptive radiation. At this stage, the rate must increase similarly as the dynamic illustrated in a technology’s scale. In nature, biology, and technology, all are evolving and aligning systems that illustrate a phase transition of self-reproducing characteristics. This same threshold where the system becomes autocatalytic and the momentum of adoption (or alignment) accelerates both occurs at the same threshold for both nature and technology systems, and if you observe, it occurs at the same phase transition in all complex systems.
Another way to describe the dynamics that occurs in the sophistication of technology in innovation science is through the diffusion of a solution amongst adopters. A bell curve diagram (Fig. 3.2) can be used to show the distribution of adopters over the course of the maturity of a venture. This is based on the aggregating fulfillment of sequential classification of adopters ranging from innovators, early adopters, early majority, late majority, and laggards. The spatial distribution of each segment of adopters illustrates the percentage of the total adoption that occurs at a certain stage of maturity. Very similar to the sigmoidal diagram that illustrated the dynamic of a technology’s rate of performance over maturity, the diffusion bell curve illustrates the dynamic of accelerating adoption that occurs through its spatial distribution. Both the S-curve diagram and the diffusion bell curve illustrates systems over maturity that experience a phase transition that accelerates fit and adoption. What is even more interesting is that if you overlap the two diagrams together (Fig. 3.3) using the same measurements, you will see that that phase transition occurs in the same spot of maturity. How can this be and how could this be so for other complex systems?
Complexity science has studied systems across a number of disciplines applied to different aspects of our world and universe, and somehow a phase transition to self-reproduction occurs in similar stages of maturity. It is kind of mind boggling to think of the parallels here, but if we try to simplify things that are inherently complicated, the way you can simplify the characteristics across disciplines are: 1) these are all systems across a lifecycle of maturity, and 2) They are beholden to universal statistical probability. Regardless if a system is comprised of molecules, genes, ants, or even stakeholders within an innovation value network, there is still a regular statistical behavior that occurs. The dynamics of this statistical behavior of the systems all experience a phase transition that begins to auto-catalyze at the stage of maturity where the ratio between stakeholders and connections becomes 0.5. Yes, this is a very specific number, but bear with me. To simplify things, let us forget whether or not we are speaking about technology systems or biological systems, let us just speak in terms of systems comprised of nodes and edges. Nodes could represent a stakeholders within a system, and an edge represents a relationship between those stakeholders. If we used a small sample set of twenty nodes in a system and over time started making edge connections at random, you would see a similar phase transition to self-reproduction that would change the rate of complexity of the system. In the diagram above (Fig. 3.4) you will see that as edges connect nodes at random over different stages, complexity increases and it is at an edge-to-node ratio of 0.5 that clusters start to be formed. The probability of you picking up a cluster of connected nodes at random becomes significantly higher at this ratio. Prior to this ratio clusters do not exist. As the ratio increases over the maturity of the system, the size of the clusters also increase. It is once the ratio of the system surpasses the ratio of 1.0 that closed pathways start to emerge, but it is at the ratio of 0.5 that illustrates the phase transition. If you are to plot our edge and node system on on a diagram where it’s x-axis represented the edge/node ratio, and the y-axis represented size of clusters, you would miraculously also see a sigmoidal diagram (Fig. 3.5) similar to what we saw with our technology innovation S-curve. In our edge/node comparison diagram we observe the same phase transition dynamic but based on a larger sample set of 400 nodes. Looking at node/edge diagram you can witness the size of connected clusters slowly increases until it arrives to a phase transition at a ratio of 0.5 in which large clusters start to be created in an accelerated rate. If we reference and compare our overlapping diagram (Fig. 3.3) between the innovation S-curve and our diffusion bell-curve that represented technology lifecycle, the phase transition occurs when the spatial distribution of adopters is at a ratio of 0.5 as well. If you add up aggregating segments of adopters (2.5%,13.5%, 34%) up to the phase transition of the diffusion bell-curve, you reach the same ratio of 0.5. If we are now to insert life back into these systems and use the nodes to represent stakeholders and the edges to represent interaction such as catalysis, relationships, or transactions, you can better understand the compounding influence of adoption (or fit) would have on one another that would cause self-reproductive and auto-catalytic characteristics at a phase transition. You can imagine the influence of a growing collective that impacts the accelerating diffusion and adoption. These systems become a swelling wave with aggregated momentum and weight, and if you are a surfer, you will want to time that wave correctly — that sweet spot is where phase transition occurs.
Studying the nodes and edges in a simplified version of an evolving system we have to remember that nodes represent stakeholders and edges represents a connection between them. As the complexity and clusters of a system grows, the system as a whole has a varying influence on individual stakeholders. If we reference the diffusion bell-curve (Fig. 3.2) we looked at previously, you will remember that innovation diffuses through innovators all the way to laggards only through the aggregation and fulfillment of sequential stakeholders. Each segment of stakeholders, whether they are innovators or laggards, adopt at a maturity where value (performance) related to risk appetite is a fit. It is this weighting between the direct solution and its ability to deliver value related to an adopters risk appetite that determines fit. Direct solution can be the incrementally sophisticating performance of a technology as it iterates and optimizes the value being delivered. Risk appetite could also be determined based on an adopters economic capacity to embrace imperfection. That is why it is very often stakeholders such as governments or the affluent that are innovators or early adopters because of their economic capacity to embrace imperfection of a fledgling technology (e.g. think space technology). Adopters closer to laggard segments tend to possess less economic capacity to embrace imperfection, and so a fit for adoption usually occurs when a technology is fully sophisticated and mature to deliver the direct value at minimal cost. What is important to gather from studying the dynamics of how technology is diffused is that adopters are triggered at a ratio between risk and direct value. Each segment of adopters (Fig. 3.2) have varying ratios of risk and direct value dependent on their risk appetite and ability to embrace imperfection. What we have come to understand is that the complexity of a system can impact risk through mitigation or exacerbation. As an example, growing adoption illustrated in growing complexity can create economies of scale that mitigates the cost or risk to adoption, resulting in an optimized fit with the current value of the solution. Larger volumes of products sold and adopted are usually valued at a volume discount pricing. You may have experienced this when you have bought a fast-food burger versus eating a farm to table restaurant, or when you shopped at chain brand versus a boutique. Reconciling this understanding with our observations of how growing complexity of relationships in an evolving system can create insights on how technology is diffused, how a system aligns, and ultimately where that phase transition occurs. These observations offer also an insight that the contributors to this diffusion and phase transition is based on the relationship between direct value and the influence of the collective network. It is based on these ratios of direct value and network influence that determines when a segment of adopters fit, and ultimately the rate of momentum of the total system. In behavioral economics, a utility curve is often referenced to illustrate the perception of fungible value. This utility curve helps explains how a $100,000 value increase for an individual with modest means (e.g. net worth of $250,000) will perceive this increase more significantly, where the same $100,000 value increase may be less significant of a change for someone that is has a more substantial net worth (e.g. net worth of $25M). Such a utility curve illustrates varying weighting between perceived value amongst increasing utility and increasing wealth. Similar to the utility curve used to describe shifting weighting between perceived fungible value, a utility curve can explain the shifting weight of influence between direct value and network influence. This utility curve (Fig. 3.6) can also describe the complexity of the system as it matches the sigmoidal diagram of edge to node ratios where, through phase transition, complexity increases on a steep curve (i.e. the more nodes determine how steep). It can be observed on this utility curve that upon reaching maturity, the size of the largest cluster maxes out at the total available nodes. If we apply the same increment of increased value at different stages of complexity on the utility curve, you can see the difference in the surface-area that is used to measure value. If you were to look at the total ratio that is put into consideration for fit and adoption, you can also see the weight of network influence in relationship of direct value. As illustrated across the other diagrams we visited such as the sigmoidal (Fig. 3.1) and diffusion bell-curve (Fig. 3.2), you can see that a ratio between direct value and network influence varies depending on the level of complexity. This utility curve diagram (Fig. 3.6) also illustrates how in more complexity, the influence of the network weights more in relationship to direct value. These are systems that grow to levels of complexity, where the system itself starts to influence itself. Relationship and connections created in complexity start to influence fit in relationship to the system. These are the autocatalytic and self-reproducing characteristics that are found at phase transition in a system.
Our world is made up these complex systems. Our world is not isolated and segregated, but they are interconnected and intertwined. If we are to take on this lens and view our world as these interdependencies, then we can start to approach things like blockchain technology, and innovation as a whole, as systems as well. Technology and the process of innovation is no different than other systems we have observed like genetic regulatory networks, species evolution, morphogenesis, economies, cultures, and city growth. These systems are still comprised of interdependent stakeholders, and they are all beholden to natural statistical behavior. If we start to think in systems, we can become more intentional about how we invent and create. We can become mindful of how we integrate and use our resources within the frameworks of exploration and exploitation. We can even become more intentional about how we create value across systems of stakeholders to achieve a system that is self-reproducing and autocatalytic. In a world that is full of stochasticity, using a lens that allows us to approach innovation as complex systems will allow us to become more intentional and empathetic to these common dynamics. Being intentional about building systems, not just products, will allow us to orchestrate the alignment of a system of stakeholders that can provide solutions to big problems — a convergence to a void that needs to be fulfilled — and in that, that is where value is created.
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