Aesthetic Reveries, Part 2
The Noosphere
The problem of understanding the road ahead is computationally irreducible. The only way to determine the answer is to run the “simulation” and see what happens. We cannot reduce the time required to compute the problem. In other words, we cannot predict what’s going to happen. Concepts like computational irreducibility are found in the heterodox discipline of Complexity Theory. When I started my career in the field of International Development I became interested in formalizing the models we used to affect change, and naively hoped that Complexity Theory could help. I became disenchanted with the field after I understood that the multidimensionality and nonlinearity of life is much larger than I had previously imagined.
The impossibility of formalizing the discipline of government intervention solidified my suspicion that unenlightened top-down change was not only unable to provide economic development but was also the culprit to most human strife. I think that’s when I resigned to the fact that there’s no shortcut to the political process.
This intellectual resignment gave way to the curiosity inspired by the ideas of complex system design which created modes of behavior that allow for flourishing streams of both top down and bottom up change. This topic was thriving in software engineering, although not in name but certainly in deed. Globalization and the Internet are two self-reinforcing vectors of change that are affecting humans at all scales, from the individual to the supranational state and everything in between. As I was thinking about the problem of local governance under these vectors of change, and how to balance the top down with the bottom up, I started suspecting that the technologist designing complex computational systems would be able to help.
"The future is dense with computers. They will hang around everywhere in lush growths like Spanish moss. They will swarm like locusts. But a swarm is not merely a big crowd. The individuals in the swarm lose their identities. The computers that make up this global swarm will blend together into the seamless substance of the Cybersphere. Within the swarm, individual computers will be as anonymous as molecules of air."
These words were written by Gerlenter. How appropo that an aesthetic pull brought me to his ideas and led me to move to Silicon Valley. I became especially interested in software engineering teams because their effectiveness was dependent on managing the complexity they generated from their software products with the desire of their users for more control. It seemed to me that the best way to understand how to act in a complex world, was to understand how software teams managed their activity.
Working in sales for both early stage startups and public companies like Google I started seeing a pattern in the organizations that I worked with. You had the classic old school company. They were IBM, Oracle, or Microsoft shops. These guys used software that mirrored their hierarchical and bureaucratic companies. Then you had a second type of company - the one that was building something new, an alien edifice some might say. These were the companies building the Internet, the noosphere encircling the earth.
The first type of company is highly hierarchical focused on formal processes and as a result the name of the game is top-down control. The first thing you hear when you talk to their SWEs is “that’s above my pay grade”. These companies use Waterfall and ITIL type methodologies which are implemented in a hierarchical command and control fashion. This works well enough when your products are simple, but as competition increases and customers expect more from their software, these teams are finding it hard to adapt. You can map their problems to that of senile government bureaucracies.
The second type of company is product and customer oriented, and their employees enjoy more degrees of freedom and trust to accomplish their goals. These cultural changes are reflected in how these teams build their products. Their go to methods are the result of the Agile philosophy. This philosophy was informed by the failings of Waterfall and the lessons gained by Lean Manufacturing and Design Thinking. Basically it constituted a rebellion from the rigid and bureaucratic mindset of the old guard. Now seeing that consultants also need to make money Agile has spawned a variety of implementations. These ideas were the basis that informed what is called DevOps.
Steve Denning goes so far as to apply the Agile ideals to the way the whole organization should work. In his view, only companies that understand these values will survive. But what’s behind these values? Why are they so effective? It seemed to me that at their core the Agile methods were effective because they did not pretend to embody knowledge they did not have. Globalization and the Internet, are basically the same phenomenon. They represent transformation at scale at and at an accelerating rate. If we think of an economy as primarily an information system we can see that GDP grows as knowledge flows increase. The faster a person, a company or country learns, the faster GDP grows. In an ever changing business environment Agile companies shunned the top down strategic plans and allowed more power to flow to their employees. In other words, if we cannot formalize the method that will create a successful business than we ought to give power to the edges of the company to engage in a process of discovery. The company has a vision and defines the function boundaries from the top down, but then it has faith in their employees to accomplish this vision from the bottom up.
Some companies are hooked into knowledge flows and are learning fast, others are stuck in a rent seeking mindset trying to protect their cash flows as their returns from knowledge stocks diminish from increased competition. It becomes practically impossible to tell apart an employee from an ITIL/Waterfall company from their government counterparts, as both these cohorts are stuck in a zero-sum-game rent-seeking mindset where signaling work by following a formal procedure is more important than work itself. Companies and countries stuck in this mindset, are not learning as fast as the others and their economies seem to rely more and more on national debt. Instead of contributing knowledge, and thus GDP growth, we are witnessing businesses and economies stuck in a vicious cycle of funding jobs we don’t need with money we don’t have.
Because both company growth and country growth is tied to an increase in knowledge flows, perhaps we can understand why global economy is not growing at the same rates by looking at why companies are not learning. As our knowledge grows the world changes faster and in higher dimensions making learning harder. The ones at the edges, the countries and companies creating the change are learning faster than the rest. The other issue is that a lot of minds are encumbered by top down standards, and are not able to see market signals about what is important to learn.
John Hagel and John Seely Brown argue that computing, storage and bandwidth are witnessing continual improvements and are not allowing competitive vectors to stabilize. These changes are enabling greater productivity and connectivity and are leading to an acceleration in the movement of capital in the form of knowledge and talent. The places and individuals connected to these knowledge flows are getting the most upside. These flows can be found in Shenzhen, LA, but also Twitter, Reddit and Hackernews. Pre-internet companies have not gotten the message yet:
“Their average return on assets (ROA) has steadily fallen to almost one quarter of what it was in 1965, despite the fact that labor productivity has improved. The paradox of falling ROA alongside growing productivity is explained at least in part by the rising total compensation of knowledge workers and other talented employees, and by consumers’ growing power over vendors that end up “competing away” their cost savings..”
And here is the core of their argument:
“Twentieth-century institutions built and protected knowledge stocks—proprietary resources that no one else could access. The more the business environment changes, however, the faster the value of what you know at any point in time diminishes.”
Until now, companies were designed to become more efficient by growing ever larger, and that’s how they created considerable economic value. The rapidly changing digital infrastructure has altered this equation. The mismatch between the way companies are operated and how the business landscape is changing helps explain why returns are deteriorating while talent and customers reap the rewards of productivity. Scalable efficiency ought to be replaced by “scalable learning” and growth in intercompany knowledge flows will be a particularly important sign that firms are adopting the new institutional architectures and operational practices necessary to take full advantage of the digital infrastructure.
Although this picture cannot answer all our questions and illuminate our future, it does give us an important perspective. The companies that are powering our growth are the ones that have found a way to scale the ability of their employees to learn. Paradoxically, the companies that are creating rigid mechanical systems have some of the most imaginative and inventive people at their core.
The S-curve
The S-curve models the usual growth path for a company and highlights the different cultural norms that tend to dominate in each successive part of the growth. It seems that genius is a side effect of madness, and most people who start at the early stage of a business are particularly high intelligence individuals who go through the pains of finding what we call product- market fit. If the company manages to ride through the chasm where most startups fail, between the early adopters and the early majority of their market, they have successfully found product- market fit and their growth accelerates.
This is a period of centralization and standardization. The people hired at this stage generally prefer stability and end up sticking with the company for the long term. Meanwhile, the earlier team moves on to create other companies. The issue is that the remaining group does not have the culture and attitude to restart growth. These companies either go bankrupt or get acquired by larger companies.
We can argue whether this model is scale independent but I think we are seeing the same core dynamic as it pertains to our decreasing rate of growth. If you are asking where did the creative entrepreneurs go, I am afraid the answer is a bit banal. I think they died, and we stopped creating more.
There’s a quote from Arendt reminding us that every generation, civilization is invaded by barbarians and we call them children. Culture molds our children and it seems that it is molding less entrepreneurs. There’s this valuable quality in entrepreneurs some call definite optimism that can’t be measured and yet it is the most important human capital. I don’t know how it’s created and destroyed but I have the presumptions that there are two vectors contributing to the evaporation of this valued memeplex. Our education system and our over saturated media.
As our children become institutionalized in schools created to standardize thinking and stabilize nation states it’s easy to forget that “the first Industrial Revolution created a chemical industry without chemistry, an iron industry without metallurgy, and power machinery without thermodynamics." In our postmodern world, the creatives and the rebels are slowly weeded out of our education system or made to conform through ritual practices. Society also collapses in the same standardized thought within the MSM-Advertising loop. Everyone seeks the same resources, cars, houses, and vacation destinations. The minds stultify into thinking that thought and creativity constitutes following the latest trendy self-improvement algorithm found in business books so you can pass a job interview. Optimism, imagination and hope disappear from young minds as over saturated media encumber their dreams.
That said, in the spirit of definite optimism, I think there’s hope! If software is eating the world, then the educational and governing systems are next in line. An update to society’s operating system could allow humanity to learn faster, work better and continue surfing the global S-curve. In “The Second Machine Age” we are reminded that “the benefits of electrification stretched for nearly a century as more and more complementary innovations were implemented. The same is happening with information technology. They think that “significant organization innovation is required to capture the full benefit of general purpose technologies” like the internet.
This change in our governing paradigms could have the same effect on our GDP as the creation of a new General Purpose Technology. GPTs have the ability to unlock productivity for many other areas of the economy that lie downstream. In Enterprise software sales it is understood that important complements to information technologies are the business process changes and organizational protocols that new technologies allow for. Paul David, an Historian from Oxford, reviewed the results of American factories when they were first electrified and found that they retained the same factory layout and productivity did not change. Thirty years after the original managers retired and factory layouts changed considerably with new management leading to the tripling of manufacturing productivity in the US. These changes resulted in manufacturing ideas like Six Sigma and Lean Manufacturing which evolved into DevOps. The same mechanism could apply to sovereign governments. As our information technology changes so do our business practices and ultimately so will our governing practices. It’s funny to think that the same ideas that led Netflix to beat Time Warner might also allow the Albanian army to take over the world.
At least this is one view that is held by advocates of cryptoanarchy. Reality on the other hand tends to be more nuanced. In the next part I will explore some of these nuances.