New paradigms are discovered by asking questions that can’t be answered.
Old paradigms are kept in place by insisting on answers that can’t be questioned.
New paradigms are discovered by asking questions that can’t be answered.
Old paradigms are kept in place by insisting on answers that can’t be questioned.
The Australian government’s recently introduced News Media and Digital Platforms Mandatory Bargaining Code is not an isolated phenomenon. I believe it sets a firm precedent for other countries and trading blocs to introduce similar legislation in the near future. This is part of a complex process of an evolutionary realignment of the internet with long-reaching consequences. To simplify, we can look at this realignment at three scales, let’s call them tactical, operational, and strategic.
On a tactical scale, media bargaining codes of the type recently negotiated between the Australian government and Google and Facebook give established legacy media companies a stable income from their content circulating within the Google and Facebook walled gardens. This is represented by these same legacy media as a victory against the social media giants but is in fact a victory against smaller media competitors who would find it much harder to negotiate similar payouts. The income stability gives legacy media companies in the newspaper, TV and radio sectors a brief life extension in the face of collapsing audience numbers and advertising revenue. However, the respite will be brief because Millenials and Gen Z consume their news and media entertainment in completely different formats, and from platforms outside of legacy media control. In the short term, the media bargaining codes do not affect the social media giants in any meaningful way because their revenue does not come from content but from leveraging the behavioral data of their users.
There is an argument that tech giants can retaliate against this type of legislation by scaling back their operations in a given country. I think this is a possibility in a few isolated cases where the loss of users does not seriously affect advertising revenue. However, tech giants are far more dependent on their users than their users are on them. Of course, they do not like their users to know that, but the logic of revenue generation is simple. The business model of social media giants is built around content delivery and advertising based on user behavioral preferences. When users start migrating to other platforms advertising revenue starts falling and the entire model is in crisis. Facebook made an important mistake in closing news channels in the Australian market as this only managed to generate bad optics with the public and more support for the government to call Facebook’s bluff. Which it did, and Facebook folded immediately.
On an operational scale, media bargaining codes set a precedent for direct government interference in the revenue streams of internet and social media companies. From now on this interference will only intensify, with governments around the globe pushing the envelope on what is possible. If content qualifying as news can be legislated in this way, then so can all other content. For example, governments can start legislating different monetary values for different content, based on content types or the semantics of the information being displayed. Or, they can start imposing penalties for censorship, as the legislation currently being discussed in Poland, or for its absence. In other words, what is at stake is the entire modus operandi of social media, built around content delivery and advertising based on user behavioral preferences. Break content delivery, or make it too expensive, and you break the entire model.
Fundamentally, the tech giants have no effective retaliatory measures against these types of legislation, short of lobbying against them with legislators. After years of creeping selective censorship, they have long lost whatever good will they had with users. Remember Google’s “don’t be evil”? After the spectacle of US social media giants coordinating to shut down and censor the voices of a sitting US president and his supporters in an election year, no sane government will stop to consider the ethics of legislating against these companies. Their time is up.
On a strategic scale, this realignment is part of a tectonic process of clusterization of the internet. The network was built to be information-agnostic, that is, data was to be able to travel freely across the network regardless of the semantic value it carried. The internet was, and to an extent still is, a “river of copies” as Kevin Kelly put it. With the selective legislation of content, we are seeing the appearance of dammed lakes on the river of copies. The long-term effects of this process lead to the emergence of different sovereign internet clusters with their own legislative frameworks around content, and a highly filtered information flow between them.
I don’t think it will be a full fragmentation, because the network is far too valuable to break it completely. Instead, I believe we will witness the emergence of sovereign internet clusters organized around national and supranational borders. The Chinese internet is an obvious example, and I think Russia will soon close off its own fully sovereign internet as well, to be joined by an EU internet, possibly a Commonwealth internet, and so on. Information flow between clusters will still be possible, just like it is possible to access the open internet from within the great Chinese firewall by using a VPN. However, I think clusters will try to keep content within the cluster as much as possible. There would also probably emerge a fully distributed internet 2.0 which would act as a wild west periphery to the sovereign clusters.
Yesterday the NYT published this piece, describing what appears to be a veritable mental health pandemic among Gen Z and late millenials in the developed world, ostensibly resulting from the COVID-19 social distancing measures.
The article reports that youth psychiatry wards in many European countries appear to be filled to record capacity, while in the US a quarter of 18- to 24-year-olds have seriously considered suicide.
It is not only the loneliness associated with social distancing, but also the loss of purpose caused by economic collapse and gigantic youth unemployment. There is a massive spike in anxiety, depression, and a sense of guilt from ‘missing out’ on the bright future of carefree consumption promised by the global media-entertainment complex.
Someone in their early twenties describes how they are struggling to envision a future after a year of social distancing and massive job losses. The NYT aptly frames this as “a world with a foreshortened sense of the future.”
I would describe this as a catastrophic horizon loss.
The future is not ‘foreshortened’, it is completely absent. The horizon has been disappeared. Where? Perhaps somewhere between planned obsolescence, environmental collapse, a parasitic global financial system, forced isolation, economic collapse, deliberate social atomization, a global ersatz-culture celebrating hyper-consumption, and a gerontocratic global elite completely out of ideas and utterly divorced from the everyday reality of the 99%.
This horizon loss has nothing to do with the Gen Z and late millenials who are on the receiving end of its arrival. I also don’t think it is caused by the global reaction to COVID-19. The pandemic only sped up and made it visible earlier than it probably would have been otherwise. The disappearance of the future was baked in the paradigm whose death spasms we are living through now. After all, when Fukuyama celebrated the ‘victory’ of global liberal democracy as the death of history, he also inadvertently announced the death of the future and the arrival of an eternal present.
The good news is that this brief and terribly destructive 30 year paradigm has come to an end. There are no more horizons left within it, and many possible futures outside of it.
Every system is in its essence a network of actors that perform it from moment to moment into existence. The participants in the system, or actors in the network, enact and perform it through their daily routine operations.
Some of these routine operations are beneficial to the system being performed, and some are not. Some add to the energy of the system and therefore reduce entropy, while others take away from that energy and increase entropy. If the former outweigh the latter, we can say the system is net positive in its energy balance because it generates more energy than it wastes. If the latter outweigh the former, we can say the system is net negative in its energy balance as it wastes more energy than it generates. How to distinguish between the two in practice?
The rule of thumb is that any action that increases complexity in a system is long term entropic for that system. In other words, it increases disorder and the energy costs needed to maintain the internal coherence of the system and is therefore irrational from the system’s perspective. For example, this includes all actions and system routines that increase friction within the system, such as adding steps needed to complete a task, adding reporting paperwork, adding bureaucratic levels a message must go through, etc. Every operation a piece of information needs to go through in order to travel between the periphery, where contact with external reality happens, and the center, where decision making occurs, comes at an energy cost and generates friction. Over time and at scale these stack up and increase entropy within the system.
Needless to say, the more hierarchical and centralized an organization is, the more entropy it generates internally.
In addition, what appears as a rational action at a certain level is irrational from the perspective of the system as a whole. For example, if a layer of management increases paperwork this is a perfectly rational action for that management layer, because it makes it more needed and important within the system’s internal information flow; however, this is a totally irrational action from the point of view of the system because it increases its internal operational costs.
Put differently, from the point of view of a system such as a large hierarchical organization or a corporation, the only actions of the agents comprising it that can be considered rational are the ones that increase the net positive energy balance of the system – i.e. reduce internal friction and/or increase external energy intake.
Importantly, this should be viewed across a time axis.
For example, when it comes to a complex operation such as a merger between two departments, or two companies, it might be a good idea to compare the before and after energy net balance for the two systems and the new system that has emerged as a result of their merger. It is also important to look in high enough granularity in order to understand the specifics of each network within the system, and its operations in time.
Say you had two admin structures servicing two different departments, and, now that the departments have merged, senior management optimizes the two admin structures into one, and cuts 50% of the stuff due to ‘overlapping roles’. On the face of it this is logical and should reduce internal energy drag, as admin structures are net negative – they don’t bring in new energy and have no contact with external reality.
However, the new merged admin structure now must service a twice larger part of the system than before, and as a result ends up delegating 30% of that new work back to the front line staff it is nominally servicing. As a result, the front line staff now have to perform 30% more reporting paperwork, which is net energy negative, and that much less time to bring in new energy into the system. In effect, the long-term effects of this ‘optimization’ are net energy negative and result in increased friction within the entire system that was supposed to be ‘optimized’.
I had an interesting conversation about my essay on the Red Queen Trap with someone on LinkedIn, and it made me think about something I did not explain in the essay.
In an ideal environment each element of a system will be acting rationally and striving towards its own preservation and, by extension, the preservation of the system. Rational action here can be understood as the action resulting in optimal energy efficiency from a given number of viable options, where optimal energy efficiency is a function of the energy that must be spent on the action vs the energy that is gained from performing the action. The scenario I describe in the Red Queen Trap essay is set in such an ideal environment.
However, in the real world individual network actors do not often act rationally towards their own or the system’s preservation. This is not necessarily out of stupidity or malice but is often due to limited information – what Clausewitz called ‘the fog of war’ – or a host of other potential motivations which appear irrational from the perspective of the system’s survival. What is more, the closer an actor is to the system’s decision-making centers, the higher the impact of their irrational decisions on the overall state of the system. The irrational decisions of front-line staff [the periphery] are of an entirely different magnitude to the irrational decisions of senior management [the decision-making center].
In practice this means that in complex hierarchical systems decision-making centers will have much higher entropy than the periphery. In other words, they will be dissipating a lot of energy on internal battles over irrational decisions, in effect actively sabotaging the internal cohesion of the system. As a reminder, the lower the internal cohesion of a system, the more energy the system must spend on performing itself into existence. The higher entropy of decision-making centers may be harder to observe in the normal course of operations but becomes immediately visible during special cases such as organizational mergers or other types of system-wide restructuring.
Interestingly, it is in such special cases when senior management is often tempted to make the internal environment of the system even more competitive – through the layering of KPIs or other means – in order to ‘optimize the system’ and protect its own position in the hierarchy. While on the face of it this appears to be a rational decision, it invariably ends up lowering internal cohesion even further, thereby increasing energy costs and routing even more resources away from the periphery and contact with reality [market competition].
The Red Queen Trap is to be found in the famous Red Queen paradox from Lewis Carroll’s Through the Looking Glass. In this story, a sequel to Alice’s Adventures in Wonderland, Alice climbs into a mirror and enters a world in which everything is reversed. There, she encounters the Red Queen who explains to her the rules of the world resembling a game of chess. Among other things, the Red Queen tells Alice:
It takes all the running you can do, to keep in the same place.
On the face of it, this is an absurd paradox, but it reveals an important insight about a critical point in the life of every system. Let me explain.
Every system, be that a single entity or a large organization must perform itself into existence from moment to moment. If it stops doing that it succumbs to entropy and falls apart. Spoiler alert, in the long run, entropy always wins.
To perform itself into existence every system must expend a certain amount of energy, which is a function of the relationship between its internal state and the external conditions it operates in. In other words, it must expend some energy on keeping its internals working smoothly together, and then expand some energy on resisting and adapting to adverse external conditions.
The better adapted a system’s internal state is to its external conditions, the less energy it must dedicate to perform itself into existence, and the larger the potential energy surplus it can use to grow, expand, or replicate itself.
However, external reality is complicated [not to be confused with complex] and changes dynamically in ways that cannot be modeled over the long term and require constant adjustments by the systems [organisms, humans, organizations] operating within it. In other words, an external state observable at time A is no longer present at time B.
This is a problem for all systems because it requires them to change how they operate.
It is a small problem for simple systems which are usually internally homogeneous and highly distributed. Their homogeneity means they don’t need to spend much energy to maintain their internal state, and their distributed topology means they make decisions and react very fast.
It is a serious problem for complex systems [large organizations] which are usually rather centralized and heterogeneous. Their heterogeneity means they must expend a lot of energy to maintain a coherent internal state consisting of various qualitatively different elements, and their centralized topology means they react and make decisions rather slow.
It is a profound problem for complex hierarchical systems [large organizations with vertically integrated decision making] which consist of multiple heterogeneous elements stacked along one or more vertical axes. Vertical integration means that each successive layer going up is further removed from direct exposure to external conditions and is, therefore, slower in adjusting to them.
A system might be quite successful in adjusting its internal state to external conditions at time A, but a later time B might present a different configuration of conditions to which the internal state of the system at time A is profoundly inadequate. The more complex the system, the more energy it must expend in adjusting to changes in external conditions from time A to time B.
Complex hierarchical systems have the hardest time in making these adjustments because key strategic elements of their internal state [i.e. decision-making centers high in the hierarchy] are far removed from direct contact with external conditions. To orient themselves and perform the system’s OODA loop they rely on communication about external conditions reaching them from the periphery of the system, while orders on necessary adjustments must travel the other way, from center to periphery. This takes time, and the more layers the signal communicated from the periphery must pass through on its way to the center the more abstracted it becomes from external conditions. In other words, the center receives a highly imperfect version of the external conditions about which it must make adaptive decisions.
Over time, this generates a growing number of errors in the internal state of the system, requiring more and more energy to be routed to internal maintenance [i.e. bureaucratic paperwork], leaving less and less surplus energy for adaptation, growth, and expansion. Eventually, and this stage can arrive very fast, the system reaches a state of pseudo-equilibrium in which all energy it can produce goes towards internal maintenance and there is zero surplus energy left. This is where the Red Queen Trap kicks in:
The system does all the running it can do, to keep in the same place.
How does the trap work? First, from the inside everything in the system still seems to be operating smoothly and things are humming along following external conditions at present time A. However, this is a false perception of equilibrium, because when external conditions invariably change in future time B the system will have no surplus energy reserves to adjust to the new conditions.
The more imperfect the version of external conditions reaching the center of decision-making, the more pronounced the system’s inertia in this state of pseudo-equilibrium, and the deeper it goes into the Red Queen Trap.
Second, having eventually discovered there are no more surplus energy reserves left, the system must now make a choice. In the absence of surplus energy and provided there is no energy transfer from the outside, it must somehow free up energy from within its internal state to adapt. The question is, which internal elements should be sacrificed to free up that energy? This is where the Red Queen Trap’s simple elegance is fully revealed.
Essentially, there are two options – a seductively easy one and an unthinkable one. The seductively easy option is to sacrifice the periphery, or elements of it, and preserve the decision-making center. It is an easy choice for the center to make because it naturally sees itself as the key element of the system and this choice allows it to remain intact. It is a seductive choice because the center suddenly finds itself with a flush of spare energy which it can use to maintain the pseudo-equilibrium and often even to grow itself at the cost of the periphery. Alas, the elegance of the trap is in the fact that the seductively easy option removes the center even further from external conditions; less periphery equals fewer opportunities to observe and react quickly to external reality, thereby further magnifying the initial conditions that brought the system to this state in the first place. By making that choice the center sinks further into the trap.
By contrast, the unthinkable option is to sacrifice the center and preserve the periphery, thereby flattening the internal structure of the system into a less hierarchical form. It is an unthinkable option for the center to make because, as pointed out above, it naturally sees itself as the key element of the system, and this choice forces it to sacrifice itself. It is also unthinkable because it involves a thorough rethinking of the internal structure of the system, which until that moment was organized entirely around vertically integrated decision making, with little to no autonomy in the periphery. The center must not only sacrifice some of itself but also reorganize the periphery in a way allowing it to perform those functions in place of the center. This would allow the system to free itself from the trap.
Most systems choose the seductively easy option and the Red Queen Trap eventually grinds them into oblivion. Those few systems that go for the unthinkable option escape the trap and, if they remain persistent in their application of the unthinkable, learn how to go different places with running to spare.
This is a paper on provenance and smart garments I just published together with colleagues from advanced materials, engineering and information sciences. Here is the abstract:
There is a significant nascent market for ethically produced products with enormous
commercial potential around the world. A reliable method to signal the provenance of products is
therefore critical for industry, given that competition based on price is not a viable strategy. The
ability to trace and signal ethical treatment of animals is also of significant value to textiles
manufactures. The efficacy of such a method can be measured with respect to the cost of
implementation, scalability, and the difficulty of counterfeiting. The key to traceability is to win the
trust of the consumer about the veracity of this information. Wearable sensors make it possible to
monitor and improve the management of traceability and/or provenance. In this paper, we
introduce a method for signalling the provenance of garments using radio frequency watermarks.
The proposed model consists of two levels of authentication that are easy to use by legitimate
vendors, but extremely difficult to imitate or hack, because the watermark is built-in and based on
the radiation signature of electroactive materials.
And here is the full paper: