Capital and Calories

Kyle Ballarta
25 min readMar 18, 2020


An exploration of the dynamics and the environments in innovation systems that transform ideas into reality.

3D printing homes to solve homelessness, and autonomous drone networks creating a new world of access is on the near-side of science-fiction.
Icon and Matternet are two ventures building new categories by aligning technological systems.


Creation, invention, and the process of innovation is often met with much trepidation at first glance. Our natural inclination is usually one of caution because of the uncertainty, and the unknowns that are infinite. At the same time, there is something quite fascinating about innovation. It is in uncertainty, without borders or constraints, that there are infinite possibilities and new worlds on a horizon yet to be discovered. It is in this duality, that we start to understand that this dynamic is a characteristic of complexity — That two worlds (or more) can exist of the same thing — That innovation does not sit in isolation from the rest of the world, but it is a process that is interconnected with the many layers of the universe. A universe that is always changing; constantly evolving, and perpetually expanding in its complexity.

In this article, we explore the characteristics of the unique dynamics that occur in different stages of an innovation system. Acknowledging that creation and innovation cannot be forced to come about, one can only create the right environment, the right surroundings, that enables the probability for creativity to occur. Studying the dynamics that innovation experiences in complex systems, we understand that order emerges from stochasticity through the iterative oscillations between Exploration and Exploitation. Through each iteration, a system is able to explore without discrimination, and then exploit positive feedback that then starts to naturally select non-solutions out to arrive to the definition of discovery and order. In each system, there is a source of energy that fuels each iteration between Exploration and Exploitation; for organisms within a biological system this is the metabolic processing of Calories; and within technological systems, this tends to be the use of Capital. This article is an exploration of the dynamics of innovation systems; and by understanding these characteristics, we can understand how to fuel the iterations that naturally evolve a world of infinite possibilities to discovery. If we understand the unique dynamics of the innovation lifecycle as a system, we can potentially optimize the right environment and resources that progress the evolution of a technological life-cycle from idea to reality.

whether we are talking about a single organism in an ecosystem, a single gene in a genetic regulatory network, or even an innovation in a new technology category; all these different agents are both a product and a contributor of its relative network system

Innovation in Systems

Quite simply (or not simply) our world is made up of systems. Our world can be described as a metaphysical duality; that we ourselves are both products and contributors to the world we are a part of and connected to. That is true for any agent of this world, whether we are talking about a single organism in an ecosystem, a single gene in a genetic regulatory network, or even an innovation in a new technology category; all these different agents are both a product and a contributor of its relative network system. These agents behave and exist based on how it’s world informs or impacts it, and at the same time it contributes back to shaping the world that surrounds it. A single agent in a system seems to be both insignificant, and significant at the same time in relation with its greater network interdependencies. The greater network does not depend solely on a single agent, making it insignificant; but at the same time, that network would not be entirely the same without that specific agent’s contribution, making it significant — another sign of the duality that occurs in the complexity of a world full of systems.

It is the evolving alignment of these interdependent stakeholders in a value network system that creates order out of stochasticity — that defines discovery out of infinite possibility — that turns an idea into reality.

As I have explored in previous articles, value-network systems are what creates viability in technologies. It is the alignment of interdependent stakeholders that allows the full value of an innovation to be delivered and shared amongst all participating in the system. This is also evidence that innovation does not sit isolated, but it is a part of a constant tug-and-pull of interdependencies that jockey for alignment in its system. You can have a compelling invention, but without a way to deliver it to a customer (say through a retailer, a distributor, or channel partner) then it cannot be viable. In another scenario, you could have all the demand in the world for your widget, and if it cannot be properly produced at scale because of a dependency of a certain rare material, then it may not be viable to a certain level. It is the alignment of all these stakeholders that makes a technology viable just as much as an ecosystem requires every species’ participation to keep certain checks and balances in its food chain. It is the evolving alignment of these interdependent stakeholders in a value network system that creates order out of stochasticity — that defines discovery out of infinite possibility — that turns an idea into reality.

Dynamics of Systems

The innovation S-curve that describes the different stages and dynamics of the innovation lifecycle in venture.
The innovation diffusion bell-curve that illustrates the dynamic spatial distribution of adoption.

Now that we can acknowledge that innovation (whether it’s in ecology, genetics, or technology) occurs in systems for the sake of its viability, we understand that this process is not linear, but it is dynamic. In business school we have conceptually been able to grasp this concept through the study of the innovation S-curve which illustrates the acceleration of growth ranging from its early stages in ideation and incubation, to commercialization, all the way through scale and sustainability. We know from anecdotal, and some empirical data, that this lifecycle of technology evolves from idea to reality in a very dynamic way. We have also been able to grasp this through the study of how innovation is diffused. We understand that the diffusion and adoption of a new technology goes through defined segmentation of different adopters that are incentivized in different ways; ranging from innovators, early adopters, early-majority, late-majority, and then laggards, each one of these adopter segments find alignment in the value that is being created at different times for different reasons. The spatial distribution of this diffusion (in the shape of a bell-curve) is also dynamic as to illustrate the relative size of the adopting segment. We come to understand that even the adoption of a diffusing technology and its solution is not linear, but dynamic.

Conceptual framework complex network using a sample set of 20 nodes.
Computer simulation that connects a node(agent) at random at each turn.

In another article that I have penned, I walk through a deeper exploration of Phase Transitions in complex systems. In my exploration, I illustrate that in any system or network that is composed of agents (or nodes) and relationships (edges), regardless of size, all possess the similar statistical mechanisms to form order out of stochasticity. All networks and systems possess the same emergent properties that are not based so much on agent behavior, but more out of the propensity of the statistical mechanisms that are inherent in any network system. Whether we are talking about water molecules that hits a threshold of temperature that radically change the properties from water to ice, or whether we talk about evolving social species that evolve in complexity that become eusocial and cooperative like leaf-cutter ants, or even to how a idea or concept starts to form and create order between interdependent stakeholders that make it viable, all possess the same properties for emergent order to occur without a central authority figure. Using a computer program, we can simulate these statistical mechanisms that seem to be universal to any set of nodes or agents in a network. In a simulation we take a sample of nodes (agents); we then program rules that iterate repeating turns that connect a node to another node by an edge (a relation) completely at random each turn. When we observe the behavior of the network as each turn passes by, we start to notice an interesting dynamic. We start to see that the size of the largest cluster of connections increases exponentially as more iterations of turns occur. If we map out this dynamic on a diagram where the vertical axis signifies the Fraction in the Giant Cluster, and where the horizontal axis represents the Connections Per Node, you start to witness that the network starts to accelerate exponentially in how connected it is with the total available nodes. In this diagram we start to witness that the network takes on the dynamics of a sigmoidal shape that we are familiar with in the innovation S-curve. But how could this be? At each turn, a node is connected with another one, not by direction, but at random. Could sample size be a factor in this dynamic? As we run several other simulations using different network sample sets (n=50, 100, 250, 500), what we start to see is that all these network simulations experience the same Sigmoidal dynamics regardless of the number of nodes. All networks seem to reach a phase transition threshold that accelerates the connectivity and order within a collective of nodes or agents that start off stochastic. All networks and systems possess the same probability and propensity to experience this sigmoidal dynamic regardless of whether that agent represents a single organism, a single molecule, a gene, or even a single stakeholder in a technology value-network. Change and innovation that occurs in technology, biology, thermodynamics, or any system is not linear; they are dynamic. Simulations that simplify networks to simple nodes and edges can illustrate the statistical mechanisms for the natural propensities for those dynamics to occur.

All networks and systems possess the same probability and propensity to experience this sigmoidal dynamic regardless of whether that agent represents a single organism, a single molecule, a gene, or even a single stakeholder in a technology value-network.”

Exploration and Exploitation

Network simulation using a sample set of n=50
Network simulation using a sample set of n=100
Network simulation using a sample set of n=250
Network simulation using a sample set of n=500

Innovation occurs in systems; and if innovation occurs in systems, then it must also be a part of a process over a dimension of time. That is why systems constantly change, evolve, and expand, whether we are talking about fertilized zygote cells evolving into a beautiful infant, or how fauna species grow lush to fill out niches within a thriving ecosystem, or even how the universe continues to expand into infinity. Things made anew, occur in a process that is iterative over time — where things that were once stochastic can evolve dynamically into defined order. A system as a whole, learns to to develop order through the oscillation between Exploration and Exploitation. At first, when a network is stochastic, random, infinitely full of possibilities, an agent is free to Explore completely without any discrimination. I often think of the first time I saw my son as he was welcomed into this new world. His eyes stochastically wandered the room with complete wonder, soaking every bit of information in. As time continues in this process, an agent will be informed of certain information from that Exploration that provides positive feedback, which then it will Exploit in each subsequent iteration. That is like my son, starting to be familiar with his mother’s and father’s faces, and realizing that those faces were somehow connected to protection, nurturing, and love. The neurons in his brain, that were once unconditioned, that were set out in wide Exploration started to receive bits of information to Exploit over time that connected and conditioned him to start to recognize our faces. When you look at organisms, whether flora or fauna, in an ecosystem that opportunistically find niches to fill in nature, they do so through this back-and-forth between Exploration and Exploitation. These organisms openly explore until there is information that it can exploit that informs them of certain behaviors. Eusocial leaf-cutter ants will explore stochastically around the proximity of its colony, and then when a single ant (an agent) comes across something useful (say a grain of sugar), then it signals this information through chemo-communications by laying down a trail of pheromones to mark it’s path to this find. As adjacent sister ants stochastically come in contact with this pheromone trail (Exploration) they will confirm and exploit this positive information that this trail leads to something useful; they too lay down pheromone trails that further validate the exploited information and coordinate some of the most choreographed ballets of cooperation without the direction of a central authority figure. I often think about how certain bird of prey species have adopted unique hunting styles by naturally selecting and exploiting things that have worked relative to their environment and their abilities. Peregrine Falcons have adopted a dive approach to catch its prey off guard where they can reach speeds up to 240mph, versus a Eurasian Eagle Owl that possesses softer feathers so that it can attack its prey with complete stealth and silence. The process of natural selection is that of Exploration and Exploitation. Organisms tend to be opportunistic, but when there are certain advantages to exploit that maximizes its survivability and fecundity, then only certain phenotypic traits are naturally selected and passed along to progress the sophistication of the species. Even a scientific method is a process that starts off with uncertainty and infinite possibilities, but as a scientist moves back and forth between Exploration and Exploitation in iteration, the uncertainty of a hypothesis starts to reveal discovery. Starting with infinite possibilities, it is in the constant iterations over a dimension of time that exploits small discoveries, that eventually aggregates into the definitions of large discoveries. We have seen many examples of this relationship between Exploration and Exploitation in how machine learning adapts, how bacteria mutate, how organisms naturally select, and even how entrepreneurs turn ideas into reality.

technological systems iterate between exploration and exploitation that naturally selects exploited iterations to develop order within a system.
An adapted diagram of Exploration and Exploitation oscillations that more accurately depict the dynamics of a phase transition threshold.

Starting with infinite possibilities, it is in the constant iterations over a dimension of time that exploits small discoveries, that eventually aggregates into the definitions of large discoveries.”

Capital and Calories

The human evolution from bands, tribes, to civilizations were a result of the abundance and predictability to available calories which propelled its system evolution from stochastic opportunistic networks to sophisticated cooperative systems.

In all systems that oscillate between Exploration and Exploitation; that dynamically evolves from stochasticity to emergent order; there tends to be a sort of currency that fuels each iteration and its lifecycle. There must be something that propels or motivates another iteration to further explore the possibilities, and to further exploit learnings. We have witnessed this from the anthropological annals of human civilizations, as human species (Homo Sapiens) have evolved in its collective systems from hunter gathers that operated in bands, tribes, and then to fully cooperative civilizations. Much of this evolution was through the opportunity to maximize available resources to sustain life of its species: Calories. Humans that first operated in smaller numbers, wandered more opportunistically, taking advantage of any source of sustenance whether it was the fruit of a plant that they came across, or even the scavenging of a carcass that succumbed to another predator. This was not a predictable way to source much needed calories to sustain a species, and this was reflected in the smaller sizes of how we operated; mostly in smaller numbers of bands and tribes. As our species explored widely, there were small learnings that started to add to incremental ways to sustain larger amounts of food and predictable calories. Through food domestication, humans were able to start to farm and raise cattle such as cows, pigs, and goats, that offered up larger amounts of food and calories and offered a level of predictability for sources of food. It was in this motivation to maximize calories that affected the dynamics of the system of the human race. Our hunting and gathering practices could only sustain a collective behavior that was opportunistic in its foraging in smaller numbers; but then as new learnings of food domestication emerged, humans were able to produce larger and predictable amounts of calories to sustain a larger stationary population. Out of the growing number of people and complexity, we evolved from generalist behavior, that is opportunistic and somewhat undefined, to specialized and highly cooperative behavior found in complex civilizations. Along with survivability and fecundity, calories seem to be an important currency that is used in a natural budgeting of cost-benefit that incentivizes behavior, influences the ability to explore and iterate, and affects the dynamics of a collective system. Imagine if our species had not been successful in capturing a threshold of food (or calories) that allowed us to continue to iterate and explore different possibilities that would continue to progress the overall collective; we might have have hit a plateau that would have created a more parsimonious behavior of how we use our caloric energy to iterate (or worse; extinction). We might have remained a species operating in simpler bands and tribes. We have seen this caloric budgeting occur in other biological examples ranging from how ants are able to maximize cooperation in complex populations, to how certain flora or fauna species are able to optimize its survivability and reproductive value in niches in an ecosystem. This is all due to the ability to continue to iterate between Exploration and Exploitation, to maximize the survivability and fecundity, which is achieved by the available calories that propel these iterations — calories that are also required to be metabolized and sustain the species relative to its greater system. A sort of caloric economy is expended, and somehow profited from (hopefully).

Calories enable the energy for biological systems to iterate between exploration and exploitation which aims to maximize survivability and fecundity.
Similar to biological systems, technological systems require energy in the form of Capital to enable its iterative process that aims to maximize defensibility and sophistication.

The iterations within a technological system is very similar to other examples that include organisms and the budgeting and spending of calories. Technological systems iterate like any other emergent system, where it widely explores

the possibilities, and then starts to exploit useful learnings that benefit the system. You do this enough times, you can have a technological concept that starts to transform infinite possibilities into defined viability. Similarly to biological systems that require the spending of calories to iterate Exploration and Exploitation, in technological systems, the currency of this energy is Capital. Capital is often used as the energy that propels the evolution of technological advancement, whether it is the spend that transforms capital into materials to build a minimum viable product, or the use of capital to adopt a strategy to exploit an opportunity in the market. Capital is metabolized to give the technological system energy to iterate, which we hope that energy is well spent by providing a net return back. Capital is the source of energy to iterate and that is the reason why technological systems and ventures jockey to reach efficiencies where the yield of the spend of Capital returns something useful that will continue to perpetuate the defensibility, and the fecundity of the solution. If a technology is prematurely without Capital, it may not be able to iterate and face extinction. Regardless if we are speaking about a technological system or a biological system, it is the iterations that allow these systems to define and optimize — it is the energy of Calories or Capital that fuels and motivates these iterations.

Similarly to biological systems that require the spending of calories to iterate Exploration and Exploitation, in technological systems, the currency of this energy is Capital.

We have previously explored how systems are not linear, but they are dynamic. We have seen that all systems possess the same statistical mechanisms that naturally have the propensity to create order out of stochasticity. Whether we are speaking about molecules, ants, bacterium, or even technologies, all are agents in a collective system that naturally have the propensities to auto catalyze its definition. One starts to wonder, if systems have the statistical mechanism to self-reproduce, then is our role really to direct and strong-arm invention, or is our role to understand the unique dynamics in this process so that we can facilitate a process of innovation that is naturally inherent to itself?

Reviewing the dynamics of systems, we explored the sigmoidal characteristics that occur out of statistical behavior. We are familiar with this because we have come to experience its dynamics in the process of venture and technology creation. We experience that ideation and incubation occurs in a steady state, and when commercialization occurs (if successful), scale takes off exponentially, until its sophistication reaches a steady state of sustainability (until new lines of competing disruptors emerge).

In another system; one of evolutionary biology; we observe that the same dynamics of species evolution experiences the same Sigmoidal characteristics that the technology S-curve experiences. Evolving from Speciation, to Adaptive Radiation, and then reaching Evolutionary Equilibrium. This makes sense, since we had previously explored that they are both systems possessing agents that have the statistical mechanisms to evolve from stochasticity to order.

Similar to technology, organisms in biology are most innovative and inventive in environments that are isolated. Like environments that are ideal for ideation and incubation (e.g. R&D labs, skunk-works, venture studios, etc), speciation can occur in environments that are protected, that give organisms the time and space to iterate in new directions. Darwin’s observation of the Galapagos finches are examples of how species that are given the time and space in isolation can diverge and develop into truly unique or seminal species. Time and space is essential to give the permission to take risks and try new things.

Typically, once a technology ideates and incubates, it is the usefulness to a market application that translates it into share value across a value-network system. This is typically found when enough iteration finds viability in its product / market fit and commercialization. At this point, a technology has found the necessary alignment in its interdependent stakeholders that make it viable — the last missing piece. This is very similar to unique species that start to discover its niche within a great ecosystem. Finding an open niche is one of the last missing pieces for an organism to discover the ideal place where it fits and potentially where it can further exploit.

Once viability and product market/fit are achieved, the market starts to reveal new opportunities in which a technology can identify and exploit. These come in the form of new markets, new customer segments, opportunities where competition has missed; but now that these opportunities are revealed, technologies can further exploit these gaps to further increase its defensibility and sophistication. Similarly, ecosystems and organisms experience rapidly fast evolution, similar to a technology’s scale, through Adaptive Radiation. In Adaptive Radiation, an organism rapidly exploits the niches unique to new environments that start to create specialization and diversification.

After scale, a technology reaches a state of sustainability where there is a reasonable definition of market share between competitors and solutions are somewhat diffused to the total available opportunity. Similarly in an ecosystem, Evolutionary Equilibrium is achieved where the ecosystem is in balance with the right level of cooperation and competition amongst its interdependent stakeholders. At the same time, both systems experience the potential vulnerability because of the lack of adaptability and malleability at this stage. Parallel systems in the form of a disruptive technology or invasive species may enter into a system with asymmetrical advantages, leaving an incumbent firm or species open to being displaced.

the dynamics that naturally progress the evolution of the system exist because of, and as a result of, the different surroundings and environments that are unique varying levels of complexity in the system.

The stages of the different stages of the a technology lifecycle illustrated through the innovation S-curve.
The similar dynamics of increasing complexity and order found in evolutionary biology.

Alfred Wallace and Charles Darwin, were given credit as co-pioneers of natural selection, having arrived at the same observations and discoveries separately. Both Wallace and Darwin were fascinated with the dynamics of how species evolved and innovated and realized that the question of what species were, was inextricably linked to where species were. It was the study of Biogeography and species distribution that led Wallace and Darwin to observe that species uniqueness were a product of the environment that they were immersed in and a part of. As we have explored throughout this article, all systems are dynamic; and in each different stage of these systems their environments are equally different — the dynamics that naturally progress the evolution of the system exist because of, and as a result of, the different surroundings and environments that are unique varying levels of complexity in the system. Like Alfred Wallace and Charles Darwin, if we want to understand how technologies evolve, we must also be studiers of environments — and even more so, the different environments in the different stages of an evolving system like technology. It is in this understanding that we can then create and optimize the right surroundings that can facilitate the inherent propensity for its evolution to naturally take form.

beyond acknowledging the need for an energy source to propel iteration, we should be asking ourselves “what is the optimum environment that maximizes the probability of an outcome that can be created with our energy?”’

The Difference Between $50,000 and $50M

We have come to understand that 1) innovation occurs in systems, 2) that systems are inherently dynamic, 3) that systems create order out of stochasticity through the oscillation between Exploration and Exploitation, 4) these iterations are fueled by a form of energy specifically related to the system (e.g. Capital, Calories, etc).

We have also come to learn that environments are a result of, and also a contributor, to the dynamic characteristics that evolve a system to definition and order. That is why beyond acknowledging the need for an energy source to propel iteration, we should be asking ourselves “what is the optimum environment that maximizes the probability of an outcome that can be created with our energy?” That is the difference between asking ourselves if we are going to need $50,000 or $50M in capital at any given moment. It can be arbitrary if we do not empathize with the unique dynamics of an innovation system.

The different stages of an emergent network described by the varying levels of Exploration and Exploitation.

By taking a look at the sigmoidal characteristics that are universally experienced in a system or network composed of nodes(agents) and edges(relation), we can understand the dynamics of the different levels of maturity by studying the optimum, or required, levels of Exploration and Exploitation related to each state of maturity. It is the oscillation and iterations between Exploration and Exploitation that progresses the evolution of a system’s dynamics, and just as Alfred Wallace and Charles Darwin who studied the evolution of species, it is in the dynamics of the environments that will bring light to the optimum environment in relation to its maturity. As iterations of Exploration and Exploitation create emergent order out of stochasticity, the composition of Exploration and Exploitation are equally unique to the different stages of maturity.

Icon has an audacious goal to end homelessness by 3D printing homes. Big ideas are usually results of environments that give the permission to explore without any constraints.

Referencing a Sigmoidal diagram that represents a general network system that possesses universal statistical mechanism, we are able to map out the dynamics of those environments by simplifying it to the level of Exploration and Exploitation that is unique to that stage of maturity. In stochastic stages, that might represent Speciation, or Incubation, there is wider exploration with lesser amounts of exploitation. Just as we explored in island speciation, it is the time and space that allows a system to freely explore the possibilities, allowing it to be opportunistic and malleable to potential signals that will help it start to define order and direction. One of our portfolio companies at Falkon Ventures is a great example of this. Icon is a company that has developed the Vulcan II, which can 3D print full-scale homes in about 24 hours, for a few thousand dollars. Beyond developing futuristic technology, they are creating a new category by redefining the value-network; everything from the technology, to the material science, all the way to the alignment with permitting and design; it takes a new level of alignment for this to be viable. Icon and the Vulcan Printer was not something that was immediately defined, but it was something that was quietly ideated and incubated in stealth mode through a studio venture model. Icon and Vulcan, needed the time and space to allow it to properly explore the wide possibilities without the influence of external pressures. The result has become a great example of what big ideas can occur in quiet places. We have seen other examples where ideation and incubation occur in similar environments that provide wide exploration and less exploitation ranging from incubators that are taking technologies and exploring how to commercialize, to studio venture labs that orchestrate ideas, people, and resources to create venture opportunities. Even entrepreneurs who “moonlight” represent an environment where there is time and space that gives permission to widely explore. An entrepreneur may have a day job, but may begin to tinker and explore outside of office hours. There is sustainability in the entrepreneurs current job that keeps the lights on and keeps people fed, that continues to allow the opportunity to explore freely. An environment that has too many constraints is not ideal for ideation or incubation. We know that if you are worrying about receiving a paycheck, or if externalities are influencing, the exploration tends to be narrow and sometimes prematurely directed. It is in the wide exploration that is common in the stochastic stages of networks and systems and if we are to apply the right level of Calories or Capital, we must ask ourselves what is the level of resources that will result in wide explorations.

Matternet is on a mission to create a world with frictionless access to goods. Since achieving its historical part 135 cert with the FAA, they have been implementing autonomous drone networks with UPS, Swiss Post, CVS Health, Kaiser Permanente

As we also explored in the dynamics of networks and systems, as complexity increases, there is a phase transition threshold that is achieved. This becomes the delineation point between stochastic exploration and the fulfillment of a major exploitation. Before such a threshold, possibilities are more plentiful, but as small connections start to increase the probability of growing complexity in a network, iterations between exploration start to yield discoveries that can be exploited that becomes the threshold that triggers rapid order. We explored how in Adaptive Radiation, it is the discovery of niches in an ecosystem that gave first looks at viability in a greater system. In technology is the first completion of a value-network that becomes the major exploitation that represents this threshold. Technological systems sit incomplete without the total alignment of interdependent stakeholders. In ideation and incubation there is wide exploration to the possibilities of what alignment can occur between co-innovator and co-adopter to complete its viability. When there is the alignment of that missing stakeholder, then there is a rapid acceleration of exploitation because the unknowns to viability become less. A good example of this, is another company at Falkon Ventures, called Matternet. Matternet is reemerging a world that has frictionless access to goods through the deployment of autonomous drone networks. Delivering on this promise takes more than just creating a beautifully designed product, but it requires the alignment of a number stakeholders including customers, commercial partners, technology, and one of the biggest hurdles, the regulatory stakeholders. Even with a compelling workable technology, it requires the alignment of all stakeholders before the value of the solution can be fully delivered. Through its wide exploration, there were refinements to focus and strategy as a result of the aggregation of positive feedback to exploit, and with that, Matternet was able to achieve the first-ever Part 135 Certificate with the FAA in partnership with UPS — one of the last missing pieces that separates the pace of the technologies evolution. Since then, Matternet has been implementing network with CVS Health, Kaiser Permanente, AmerisourceBergen, Swiss Post, and UPS. It takes a major discovery to exploit that changes the tide and the pace for progress.

Understanding the characteristics of Exploration and Exploitation in each unique stage of this evolving system complexity, allows us to better understand what energy (Capital or Calories) enables the right environment that can facilitate the natural evolution of innovation that inherently self-reproducing.

Upon reaching a phase transition threshold, the dynamics of scale is a result of the definition of opportunities and discoveries to exploit. In previous stages, exploration is wider, but upon the completion of a viable value-network, much of the unknowns for viability are addressed, and opportunities in niches and markets start to clearly be defined. In these stages, lesser exploration and deeper exploitation is experienced. The viability of the value network is achieved and thus needs lesser exploration. It is in the definition of viability can be exploited and can be repeated, replicated, and sophisticated. Just as we saw with Adaptive Radiation, when new niches are revealed in an ecosystem, rapid acceleration of specialization and diversification occurs to take advantage of those opportunities which requires less exploration and deeper exploitation. We have witnessed this, when we have observed companies that are no longer in stages where they are searching for viability; but once product/market fit is achieved, it becomes an opportunity for companies to repeat and refine the learnings to further take advantage of market share and diffusion. When we are asking ourselves what energy (Capital or Calories) can we apply to enable this stage of scale, we must ask ourselves what are the opportunities that reveal themselves for us to deepen the exploitation. This can be a certain commercial partnership that accelerates the adoption of a new solution, this could be an untapped market that is strategic, or it could be a specific positioning that maximizes the defensibility of the technology. Similar to organisms in Adaptive Radiation that starts to exploit newly revealed opportunities in efforts to maximize its defensibility and niche, technological systems must be supported by the right Capital energy that allows it to exploit opportunities in the market that will further its defensibility and diffusion.

In stages of sustainability, exploration tends to be nominal or non-existent. Historically, this has been representative of a double-edged sword, where nominal exploration and exclusive exploitation represents full diffusion or a solution, it also is a vulnerability for parallel systems to enter and disrupt. Just like in evolutionary equilibrium, all interdependencies are defined and held in check. But as you know from complex systems, nothing sits in equilibrium for long. The only constant is change, and knowing that parallel technologies that have been opportunistic in its exploration that are sophisticating new lines of evolution, it is important to be on the right side of history if you are to be a part of it.

The need for Caloric or Capital energy in different stages of maturity in any innovation process is relative to the uniquenesses of those systems. The real question is how technological systems adapt their metabolic efficiencies of that Capital in different stages of a dynamic lifecycle to achieve different things. Technologies and ventures sometimes operate lean at earlier stages, without the need of large sums of Capital to prove minimum viable product, where other stages may need significantly larger amounts of Capital to deeply exploit an opportunity in the market. All examples represent different efficiencies at different stages that transform Calories or Capital currency into energy that allows a system to continue to Explore and Exploit. In our dedication to optimize the right environments and resources to propel the natural evolution of innovation, empathizing with the unique characteristics of each stage of maturity of that complexity is important. Understanding the characteristics of Exploration and Exploitation in each unique stage of this evolving system complexity, allows us to better understand what energy (Capital or Calories) enables the right environment that can facilitate the natural evolution of innovation that inherently self-reproducing.


Our world is made up of systems that are interconnected in a tapestry of infinite layers. It is the constant evolution of these systems that make our world so simple in their shared characteristics, and complex at the same time. Innovation occurs in systems, and we have come to learn that systems are not linear, but dynamic. When we study the dynamics of systems, whether we are talking about genetics, ecology, or even technology, we can better understand how to be facilitators of the natural propensities for those systems to emerge in order and definition. It is the through the oscillations and iterations between Exploration and Exploitation defines this order out of stochasticity and growing complexity, and there is a source of energy that enable those iterations to occur, whether it Calories in a biological system, or Capital in a technology system. If we are to be meaningful contributors to the process of innovation, we must study how our resources can create the right environment to enable the natural innovation process to occur. As participants in our own world of systems, we have come to learn that we cannot force innovation to occur, but we can play a role in shaping and molding the right environments for the probability of innovation to occur.