By: Al (Ali) Naqvi
My goal in this article is to expand the traditional disruptive innovation investment analytical framework with the additional constructs of nonergodic nature of disruptive innovations. Funds have a tendency to formulate investment thesis based upon a technology’s innovation potential and not necessarily its innovation path. Potential based analyses absorb all the nuances of behavioral elements, hype, marketing, and promoting a technology. Its reasoning mechanism is often comparative in terms of using a baseline historical event to establish performance potential of the disruptive innovation. When viewed from a path perspective, the analysis becomes far more complex – but the benefit of path-based analysis can result in far greater investment value.
The potential based analysis
Artificial intelligence has often been compared to electricity. Just as what the electric current flowing through the wires did, AI can revolutionize everything else. Some scholars and practitioners term such innovations as General Purpose Technologies, transformative technologies, or innovation platforms. For example, Brett Winton of ARK Investments argues that in the course of the last two centuries such innovations have triggered major market capitalizations (Winton, 2019). And today, he points out, waves of several transformative technologies are cresting, leading to a massive potential of market capitalization.
ARK identifies the attributes of disruptive innovations as being:
Across multiple sectors,
Upend existing or incumbent providers,
Create new business potential,
Deliver dramatic cost reductions,
Serve as a platform for new innovation, and
Propel global economic growth.
ARK keenly observes that dramatic decline in prices of such technologies creates rapid adoption, making existing technologies obsolete while creatively destroying established competitors. Similar observations were also made by Carlota Perez when describing the dynamics of technological revolutions (Perez, 2002) .
Fig 1 Adopted from ARK Investments (Winton, 2019)
Fig 2 Adopted from ARK Investments (Winton, 2019)
The above investment hypothesis, while impressive, covers only half the story. What it fails to capture are the distinct dynamics of the artificial intelligence revolution. These dynamics are not only widely different than anything we have experienced before, but are also far more complex than the subtleties and developments of technologies with ubiquitous adoption leading to market structures that gave rise to traditional public utilities.
The equilibrium is transitionary
In this regard the term “history matters” is more applicable than “history repeats itself”. Perhaps, history repeats itself in the sense that history does not repeat itself. Yet, history plays an important role, but not in the comparative sense of what transpired in the prior innovations somehow applies to the current innovation under the microscope, but instead in the sense that the evolutionary and dynamical development of the current disruptive innovation greatly depends upon its own history. The history preserved in the trajectory representing the evolutionary dynamics greatly affects the development and potential of the disruptive innovation. The transitions to the next states of development, adoption, and growth depends upon the prior states and the influences and conditions in the current states.
Unlike the stable equilibrium often claimed by the analysts, the development of technology follows more dynamical and evolutionary equilibrium where equilibrium’s stability is defined by the change itself.
Thus, discovering the right investment opportunities is not as much a function of generalizing broad equilibriums, as it is trying to understand the next transitory state and modeling the history and current state accurately to identify the specific trajectory of the innovation. Identifying this nonergodic development path is where the pool of opportunities is discovered.
Disruptive innovation is path dependent
A dynamical process whose evolution is governed by its own history is ‘‘path dependent.’’ (David, 2007). We have been cautioned against mixing economic history and economic theory. Nathan Rosenberg illuminated that innovation is path dependent and that the nonergodic nature of technological change requires analytical models to capture the inherent uniqueness and complexity (Rosenberg, 1994). Thus, analyzing the status of innovation at each state of its transitory movement to the next state can help clarify the trajectory. History matters. Furthermore, at each state of the path the numerous forces and state interval specific factors contribute to setting the subsequent direction of the innovation. Such forces may include previously committed investment, institutions, initial conditions, noise, and many other factors.
The scientific process is changing
The traditional scientific process is driven by seeking data as a function of hypothesis and experiment design. In the new era, scientific discovery can materialize from large datasets such that insights and findings precede hypothesis and experiment design. This reversal of the process introduces epistemological transformation and leads to hyper-acceleration and novelty. It can also create explain-ability challenge where innovators have to grapple with the findings that seem to hit the mark but lack theoretical explanation or justification.
Geopolitical constraints must not be ignored
Related to path dependent trajectories, geopolitical developments often alter the expected growth patterns. The emergence of China US trade tensions and eventual blocking of several Chinese firms by the US serves as a reminder that transitions in trajectories can introduce new patterns of unanticipated dynamics at the transition stages.
Noise impacts outcomes
Social systems are influenced by noise. Noise can result from faulty analysis and methods, misaligned incentives, managerial oversight, and other issues that can affect the transition into the next stage. Some of the path distractors can come from unanticipated forces. The highly admired dynamics of global brain to “help organize our social organism into a more coordinated, more efficient, more democratic, and more collectively potent entity” and in its ability “to foster more numerous and more diverse communications between both humans and technology, and then better link those communications to mechanisms of action” – do not necessarily lead to stability (Rosenblum, 2015). As seen in the recent retail investor mob raids to artificially raise the prices of assets – the global mind can become a counter force to stability.
The nature of institutions plays a major role in disruptive innovations and technological growth (North, 1990). The governing philosophy, operational mode, and strategic outlook of institutions greatly influence how innovation transpires in the economy. Technologies affect institutional performance and institutions impact technological trajectories. In this bidirectional nudge pattern, the role of institutions to both embrace and influence innovation requires constant monitoring.
Enabling technologies and data
The states of development and adoption of support and complementary technologies that form the production platform of the primary disruptive innovation are critical analytical factors (Mowery and Rosenberg, 1989). Since many of those complementary and support technological innovation now depend upon the availability of datasets, the growth potential and adoption trajectories can produce different outcomes.
The initial conditions at the beginning states of the trajectory can influence the subsequent states and that is why it is important to take a note of the initial conditions. There were many advanced economies with highly educated workforce, but the performance of India in the early stages of the computer and internet revolution greatly impacted the subsequent history where India emerged as a powerful player in the software development industry.
Reindustrialization Dynamics are different
This leads us to the main point I am trying to make. Reindustrialization takes into consideration both history and the evolution of history within the time segment or states being analyzed. That is why, while the relationships and projections presented by ARK in Figure 1 and 2, can be accurate for a time segment, the number of exogenous and unknown variables impacting the transition state are enormous. In my next article I show how to measure and track the trajectories that enable phase transition.
But the same element that produces the ailment also gives the cure. Deep learning can help assess the state of a fast-changing reality, assuming a wide enough net is thrown to capture data. ARK’s (or other disruptive innovation investors) assertions, therefore, must constantly be tested, measured, reevaluated, and reported. This means analyzing and measuring innovation trajectories with a path dependence perspective. Path must integrate with potential to create investment value.