cybernetics

»Our institutions are failing because they are failing to scale.«
Andreas M. Antonopoulos

Ashby’s Law of Requisite Variety is regarded as the basic law of cybernetics or control (i.e., steering) theory. Put simply, it says: »Don’t be more limited than your field of action.«

The most important basis of effective control is relevant information advantages. Accordingly, control is systematically successful as long as it has a stronger information base than its field of application.

With the exponential development of information technology, however, information flows in the control environment can no longer be managed by traditionally successful measures. Weaknesses in the application of tried-and-tested principles become increasingly evident in exponential times.

Depending on the observer’s perspective, this leads to useful — or even harmful — imbalances, which can result in organizational failure up to macroeconomic scales: Quite surprisingly, fundamentally new but often astonishingly simple business models successfully prevail against market leaders once considered unassailable. Here, »disruption« is ultimately nothing more than dominantly better competition. The central question is therefore not whether, but when it targets one’s own business field.
The successful new competition regularly makes the leap from underfinanced garage projects to billion-dollar valuations in just a few years, and — after overcoming the usual initial hurdles — pushes old market leaders out of the race seemingly without effort.
What is their secret?

Just as remarkable as these successes is their conceptual simplicity: In process and project organization, for example, the original two-person project Atlassian with JIRA prevailed in several categories against giants such as Microsoft, IBM, and Hewlett Packard. With increasingly agile organizational requirements (i.e., more decentralized planning), the established competitors proved less flexible than Atlassian’s simple, open approach.
Atlassian now has a market valuation in the double-digit billions and has inspired numerous imitators. Its system is so generic and versatile that it is actually difficult to pigeonhole (often it is simply described as bug-tracking software).

Much better known than Atlassian is the most prominent serial disruptor, Elon Musk. He not only took on the international automobile industry — which at first seemed overpowering — but also the nationally operated space industry (alongside various other projects that initially seemed equally hopeless).
He explains his entrepreneurial approach with first principles:

»Don’t just follow the trend. […] it’s good to think in terms of the physics approach of first principles. Which is, rather than reasoning by analogy, you boil things down to the most fundamental truths you can imagine and you reason up from there.«

A both simple and elegant innovation concept was published in 2008 under the pseudonym Satoshi Nakamoto: Bitcoin, probably the most secure digital money system. Its implementation has proved highly robust, even against the most powerful attackers. The »honey badger of money« is probably the most attractive — and at the same time the most insurmountable — honeypot for hackers, and remains in excellent health despite countless attacks and obituaries. Here again, simple empirical dominance consistently outperforms symbolism and value-laden debates. Bitcoin has the potential for disruption on the greatest conceivable scale: after all, money is a fundamental pillar of economic and social systems.

Andreas Antonopoulos describes the phenomenon of organizational control failure and its distortions aptly:

»History isn’t continuous. Decades go by when nothing happens, and then decades happen in weeks, and we’re living through that period of change right now.
[…] One of the interesting topics […] is the concept of a black swan: The idea that if you don’t have a sample of something happening in the past, you can’t imagine it happening in the future. […] We’re now living in an era of black swans […and] the internet itself is a machine that generates black swans.

When something happens that is completely discontinuous to our past experience, we try to wrap it in narrative. Narrative that relate it to something we understand, hoping that relating it in that way will help us make sense and also that it will help us predict the future. It will allow us to see more clearly what might be coming next. And of course that’s an illusion […:] the narratives are broken.

The institutions […] have started to fail, and they fail because they don’t scale, not because they’re headed by good or evil people, not because they’re rotten at the core, not because they’ve been taken over by mysterious forces: […] they’re failing because they are unable to scale to the enormous complexity of a modern world that is super interconnected and that exhibits chaotic behavior, and massive information flows that are impossible to process. […]

We now have a narrative machine, and the narrative machine is the internet. It is a machine for producing narratives, and these narratives are instantaneously global, very often viral.
It’s a meme machine, a memetic system that produces narrative. And it produces narrative much faster than any of the previous mechanisms for producing narrative.

Now this is important and it is important for a really simple reason: society is narrative, society is a collection of memes. All of our cultures are just a collection of stories that we have taken down through the generations. And when you have a meme machine operating within a society, then it can rewrite the narrative of society in real time.

Ironically all of this is happening at a time when people are most fearful. They are fearful of things that they do not understand, and in order to understand them, many people ascribe some dark force: ‚They‘.
‚They‘ are conspiring, ‚they‘ are going to vaccinate us all, implant us with chips, spray chemtrails on us or whatever ‚they‘ are doing this week. 5G creating coronaviruses, whatever it is, ‚they‘. ‚They‘ are the mysterious cabal, the conspiracy to control the world, and in every country there might be different ‚they‘. And in many cases ‚they‘ is assigned to government that somehow exhibits incredible ability to make decisions, and then make those decisions become reality through competence and efficient management.

The truth is, ‚they‘ are not in control. The reason they are not in control is because the institutions they use to govern are broken. And so the theme of our era is unprecedented incompetence that emerges from an unprecedented collapse of institutions, that is caused by unprecedented disruption through the sheer scale of […] information flows«.

»Failing to scale« is ultimately just another interpretation of Ashby’s Law.

There are numerous causes for a lack of adaptability to changing conditions. In simplified terms, these can be divided into »not wanting«, »not being able to«, and »not being allowed to«. In the following, I will concentrate on the more technical »not being able to« aspect and show a straightforward approach to solving the scaling challenges in the organization of organizations.

The international control solutions market is worth billions and generates enormous consulting demand, particularly in the area of Enterprise Resource Planning (ERP). Traditional options appear stuck in a contradiction: low-integration but flexible (and cost-effective) solutions versus standardized but expensive systems that rarely fit practical requirements and therefore require complex adjustments. In practice, both approaches are usually combined — and both are problematic.
Experience shows that standard systems are not only extremely expensive to implement, but also problematic from a process perspective: they regularly leave organizational gaps that must be closed with individual solutions. So far, the choice seems to lie only between the »disintegration rock« of individual processing and the »hard place« of rigid standard processes — or compromises between the two.

This is not for lack of effort by standard process providers. The real obstacle lies in the basic architecture. Once fundamental design decisions are made, a development path is set that becomes increasingly difficult to change over time. Path dependencies can grow so powerful that, in some cases, the only viable option is to »throw it away and build anew« — a daunting prospect, especially after major investments. The closer adaptations get to the system core, the more disproportionately expensive they become. And when non-IT aspects are involved, resistance to change can become virtually insurmountable.
For less capital-strong market participants, the path of least resistance often means throwing good money after bad, hoping it will hold out for as long as possible. The core challenge, once again, is flexible scalability — or »scale invariance«.

In the traditional model, scaling occurs through gradual aggregation of control information oriented toward organizational structures. Decision complexity is reduced statistically and enriched layer by layer with additional relevant information (i.e., horizontal integration). Limits are reached when the organizational context changes significantly and no longer fits the integration structure. In extreme cases, analyses for decision preparation can degenerate into tea-leaf reading and rampant micropolitics.

So what should a zero-based redesign of organizational control systems look like — one that combines the systematic strengths of previously irreconcilable scenarios while avoiding their weaknesses?

I propose the following first principles:

the best statistic is a complete survey
full vertical integration requires unrestricted availability of basic data
the basic structure must be rooted in networks (all organizational structures can be mapped as special cases of a network)
modeled structures must be modifiable by system users without collisions
internal structures must be dynamic, allowing not only parameter optimization but also real-time structural optimization (which also enables AI-driven coordination processes up to autonomous control solutions).

Because of the loss- and collision-free processing of dynamic data networks, internal system complexity inevitably becomes very high. On the one hand, this can be managed through simple processing principles; on the other hand, it can be abstracted away for user interfaces. (There is good complexity and bad complexity: good complexity enables scalable control, bad complexity obstructs it.)

Alongside technical complexity reduction, flexibly configurable transparency must be implemented: not everything technically accessible should be organizationally available at every interface, in order to meet privacy, information security, or policy requirements.

A small set of simple, generative rules can give rise to complex adaptive dynamics, while ensuring comprehensive controllability via those very rules.

As an additional benefit, this approach can directly coordinate AI-based interface systems.
The main challenge in the coming years lies in intelligent process integration and coordination of organizational units that can keep pace with exponential developments at any scale. cCortex offers a platform that is maximally flexible, resilient, and future-proof—at minimal marginal cost—even enabling evolution toward an independent AI system.

Because the approach is scale-independent, its introduction can be gradual, consensual, and cost-effective. There is no need for risky »big bang« projects; digitalization benefits accrue with each incremental step.
For example, many small local applications can be digitized individually and then seamlessly combined, integrated, and consolidated.

A simple example is the »decommissioning« of end user computing: the procedure enables integration of distributed expert systems (e.g., specialized planning or controlling tools) that were previously excluded from process optimization.

This simple solution thus unlocks not only the next but also the second-next evolutionary stages in enterprise resource management — and beyond.

Even small-scale applications have the potential to significantly improve organizations, with correspondingly massive “legacy effects“. Its successful introduction therefore requires strategic support across the board.