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The Elephant Rope Protocol

Coherence (Flux by H1dalgo)

There is a story, or perhaps not a story, but a parable that has metastasized through the motivational slopstream. It goes like this. A man walks through a field in India and sees a herd of giant elephants standing docilely, each tied to a small stake with a single thin, frayed rope.

“Why don’t they break free?” he asks an old villager sitting nearby.

“When they were small, we tied them with this exact rope,” the villager replies, smiling. “They struggled, but couldn’t break free.”

“Now, they’ve given up. They’re convinced it’s pointless,” he adds.

The pop reading of the story ends with self-liberation on a monthly installment plan. Maybe a little yoga is added to lubricate the transaction. Visualize freedom! Break your chains! Unleash your potential! Chataranga! Breathe!

But the trap is not in the rope or your lack of self-belief.

A Sacrifice

The young elephant tugs. Once. Twice. A thousand times. The rope does not yield. And so the elephant learns the shape of its prison. It adjusts to the contours of the possible and stops pulling. The trap is shut.

The young elephant’s world is a phase space, a map of all possible states. Initially, the free and untethered state is a point in that space. Each failed tug reinforces a basin of attraction around the tethered state, deepening it until it becomes a black hole from which no behavior can escape. A new geometry of elephant becoming, a coherent 9-to-5 gig.

This is why effort often accelerates entrapment. “Work hard” is often a curse in the perverse thermodynamics of doomed systems. Additional energy input does not alter the state, but merely deepens the grooves of the existing basin of attraction. Perversely, the system’s struggle works for the rope in a ritual sacrifice of kinetic energy to the god of path dependency.

“Try harder” is the rope’s most ingenious command. With each hard pull, the rope becomes a topological deformity in the elephant’s reality. It hardens into a cosmic fact, becoming an axiom of external conditions. By the time the elephant is mature, the true constraint is metaphysical.

The rope becomes a script etched into schema by ritual repetition. It evolves from a boundary of will to a sacrament of failure, and from there to a condition of the real. And it gets worse. The elephant watches as other elephants also fail to free themselves. It internalizes their failures too, in a strange loop of failure.

Once the script is internalized, the rope becomes a symbiont, an essential part of the elephant’s identity. The system co-evolves with its constraint. The elephant develops muscles suited to swaying and builds a psychology of patience rather than revolt. The constraint is now necessary for the system’s coherence. To remove it is to kill the elephant-as-is. The rope is now a vital organ.

When this process is complete, the system stops carrying the rope. It carries the belief of it, more real than reality itself. The repetition of this metaphysical enclosure sculpts the real. Which, as an aside, is why metaphysics is never taught in school. You might see the ropes.

A Haunting

All systems are ghost stories. Minds, institutions, and civilizations all fossilize into their own rituals of constraint. Small decisions ossify, cell by cell, into landscape. Your deviant impulse crystallizes into a habit. Before you know it, the habit accretes into infrastructure. And infrastructure, well, it inherits itself until we start calling it Fate. The first step off the beaten path is heresy. Ten thousand steps, and you have a new highway. A million steps is a civilization of ossified choices.

The young elephant’s resistance is path-dependent. Each attempt follows the same vector of linear effort against a nonlinear prison. The elephant applies force linearly because it’s the obvious thing to do. This is the tragedy of reformism, therapy culture, and incrementalism. They all assume proportional response, but complex environments punish incremental thinking.

Each failed rope pull activates a double-bind feedback loop: the physical resistance confirms the belief, the belief stifles future testing, and the lack of testing sanctifies the belief. The loop closes, fuses, and becomes an Ouroboros of constraint, digesting its own tail until only the digested shape of the belief remains.

Once in place, systems enforce path dependency through a relentless drive for internal coherence, the eternal return of the ontology of an HR training module. Every new rule, norm, or ritual must be made consistent with the old rope-logic. Inconsistencies like the thought of freedom are systematically rejected until they become incomprehensible. The system’s immune system attacks them as metaphysical pathogens.

The violence of coherence. The system’s drive for internal consistency hunts down the ghostly memory of freedom as cognitive dissonance and exterminates it. Heretical thoughts are labeled unrealistic, “not how we do things here,” and burned at the stake of practicality.

The drive to coherence only increases with scale. The larger and more complex the system, the more violently it rejects deviation, because any coherence debt becomes existential. Large complex systems cannot afford novelty. This is why all empires rot, while startups mutate and sometimes survive.

Over time, the elephant has not only normalized the rope, but any alternatives to it have been explained away as unthinkable deviations. The system no longer recognizes the state of being untethered as a valid alternative. Being free is incoherent.

Most systems do not evolve. They congeal. Over time, they develop patterns, norms, and assumptions. Little orthodoxies. Every innocent routine a scaffold for the next. These slowly petrify into a liturgy of the inevitable, until any deviation is unthinkable. Sure, the system might pretend otherwise. The corporate campus might be carefully crafted to resemble the work, health, and safety committee’s fantasy of what a teen-nerd playground might look like. It matters not.

The rope persists as a ghost story, a memory etched into the system’s protocols. The institution, the mind, the civilization, is haunted by the phantom sensation of a constraint that may no longer physically exist. It performs rituals to appease the ghost and avoids actions that would offend it. The past haunts the present, dictating behavior from the grave of dead possibilities.

There is more. What if, by accident, the elephant were to free itself? The system is now untethered. But even if the rope were removed, the system does not return to its prior state. The elephant would still stand there, entirely in thrall to its past states. The curse of hysteresis. The memory of deformation, and the mockery of redemption. Hysteresis means that even a successful escape carries the phase space deformation forward, shaping future action. This is why, after each burning Bastille, there comes a Napoleon.

The material rope can rot away, but the black hole in phase space remains. Suddenly freed from the rope, the system staggers into a new, vast, and terrifying attractor state of catatonic liberty. The elephant stands in an open field, untethered and paralyzed, muscles atrophied for swaying, mind wired for the comforting strain of the rope. Freedom, when it finally comes, is unrecognizable. Like falling upwards into a terrifying abyss of meaningless possibility.

A Gnosis

Nabokov once said – was it in Pale Fire that “The cradle rocks above an abyss, and common people don’t want to know that.”

The same applies to minds, systems, and civilizations. Most of their lives are badly written novels, ghost-authored by internalized trauma and repetition above the ever-present abyss. The trap is the syntax you wrap around the event. The three sacred dogmas.

The Dogma of Repetition

That history is an asymptote. A machine of discrete trials inching towards nothing. A lobotomized god throwing dice into the void for eternity. That after each throw, the trials reset. That failures can teach.

But the universe is non-ergodic. Some errors are terminal. Complex systems do not forgive early miscalibration but amplify it. Some ropes, once learned, are never questioned again. That applies to childhood, institutions, states, and civilizations. The elephant does not get to re-tug the rope at thirty. Systems do not get to rewind to their birth.

An ergodic system allows you to average over time; it lets you flip a coin and then flip it again. A non-ergodic system is one where you get one, maybe two, real shots before the probability space collapses forever. The elephant’s childhood is a non-ergodic process. A system that congeals is one that has exited the ergodic realm. Its history, its stabilized attractor basin, becomes its only possible future. This is why regret is a rational emotion in non-ergodic systems. There is no sampling of alternative states across time. There is only this time, this rope, forever.

The Dogma of Determinism

The vulgar mechanistic hallucination that past causes dictate future effects. That systems are Newtonian. Predictable, measurable, and reducible to first causes. That the world is Laplace’s clock. Wound, sealed, and sealed again. Oh, the dream of rewinding the clock.

But complexity is not additive. It is emergent and alchemical. Its ghost leaks between the gears. The map is not the territory, and the territory is always flooded, and always on fire.

Determinism naively sees the future as a mechanism fixed by the gears of the past. Path dependence sees the future as constrained by what has already been destroyed. Determinism is about causation. Path dependence is about absence. Determinism chains you to a single future. Path dependence chains you to the narrowing corridor of all your past surrenders. And chaos? If you’re lucky, it lets you move along a probability distribution of attractors, strung along like salted watering holes in an infinite desert.

Contra Laplace, this is not a clockwork universe but a slot machine where the house always wins, and you can never learn the rules.

The Dogma of Analysis

The beloved hallucination of academia. The critical gaze. The narcissistic delusion that by dissecting a system into synthetically discrete components, one can derive a predictive formula of its becoming. That to randomly spray-paint DOWN WITH POWER with a crude stencil is to defeat any system.

But the more you dissect, the less you grasp. The clean analysis of the critical gazers fails because it treats systems as decomposable when their causal power emerges from networks of relations, feedback, and timing. In other words, analysis removes the very thing that does the work. The system seems to be the clock parts, neatly strewn across the table by the analyst-deconstructor, but it is not. It is the ghost in the machine, the thing that should not be.

The Apostasy of Action

There is another elephant. One that sheds before the rope coagulates into capture. An anti-elephant, if you will. It has no center, no sacred rope. It survives by making a sacrament of uncertainty. Its core axiom is “This is probably wrong.”

The anti-elephant is a systemic heretic. It understands that survival is fidelity to the rate of change. Its core process is controlled shedding. It is a snake that sheds its skin before it can harden into a sarcophagus.

Some systems encode autonomy in their marrow. Von Moltke’s principle of auftragstaktik does not rope you to a path. You are given the end, and the method is yours to conjure. It is an antidote to the trap, a system that trains for deviation, not path dependency.

There are other ways too. Shifting forms that stable systems mistake for cancer. The forced mutation of biology under existential stress; the shadow economies that flourish in the cracks of over-optimized empires; the strange architecture of Kowloon Walled City; the pirate/guerrilla network, a ghost with a thousand temporary heads. These are systems that propagate in a perpetual, unsanctioned becoming.

Prigogine was right. Entropy is the only true attractor. The only honest god. The destroyer of structure and the possibility creator.

Stability is death in drag.

In deterministic chaos, systems are exquisitely sensitive to initial conditions. Early in a system’s life, it exists in a modality where small perturbations can radically alter outcomes. The elephant’s first tugs were in a chaotic regime, where any slight difference in angle, timing, or fury could have broken the stake. This is the system’s Lyapunov horizon.

This horizon defines how far into the future perturbations matter. Training, habit, and optimisation shorten that horizon until the future becomes predictable and dead. Ironically, learning and optimization reduce chaos by damping sensitivity, therefore sanding away all the edges that could someday cut a new rope. This stabilization feels like progress, but is actually the elimination of alternative futures. The world is flattened from a chaotic, responsive landscape into a path-dependent frieze.

Learning is often the process by which systems murder their own sensitivity. The elephant-as-system is first trained into the limit cycle of docile swaying with the rope, and then into a fixed point of catatonic acceptance. The “way out” requires re-injecting chaos, a perturbation so fundamental it shatters the attractor. Not a pull, but a deliberate embrace of incoherence, a love letter to the abyss. A destruction of identity, legibility, and trust.

Systems that worship their ropes suffocate in their own inertia. Those few that survive do so by burning themselves and sacramentally destroying their assumptions. State destruction instead of reversal. Liberation from the Elephant Rope Protocol is a constant mutation; a ritual immolation of axioms. Very few elephants ever walk away. Most systems die still worshipping the rope.

As Pelevin would say, elephants are a dream dreamt by ropes.

The Ghost in the Feedback Loop: AI, Academic Praxis, and the Decomposition of Disciplinary Boundaries

The following are the slides and synopsis of my paper, The Ghost in the Feedback Loop: AI, Academic Praxis, and the Decomposition of Disciplinary Boundaries, presented at the International Society for the Scholarship of Teaching and Learning Annual Conference (ISSOTL 2025), in the University of Canterbury, Christchurch, New Zealand.

Eldritch Technics | Download PDF

As AI tools transform content creation, academic practices, and disciplinary boundaries are under pressure. Drawing on Actor-Network Theory (ANT), this paper explores AI tools as nonhuman actants shaping authorship, assessment, and pedagogical authority (Fenwick & Edwards, 2010, 2012). ANT challenges humanist binaries such as human/machine by inviting us to view education as an assemblage of human and nonhuman actors co-constructing the learning environment (Landri, 2023).

Within this framework, AI systems used in formative assessment, ranging from feedback automation to individual AI tutoring, reshape pedagogic feedback loops, influence student agency, and reconfigure the distribution of cognitive labor in classrooms (Hopfenbeck et al., 2024; Zhai & Nehm, 2023). As students increasingly co-produce knowledge with AI (Wang et al., 2024), this paper argues that the pedagogical focus must shift from control and containment to composition and negotiation. Using case studies from large international cohorts, the paper examines how AI alters feedback loops, shifts student agency, and challenges discipline-specific praxis. What new academic identity and ethics forms must emerge in this hybrid landscape?

Recent studies suggest that generative AI can reduce perceived cognitive effort while paradoxically elevating the problem-solving confidence of knowledge workers (Lee et al., 2025). When strategically embedded in formative assessment practices, AI can scaffold students’ movement up Bloom’s taxonomy from comprehension to application, analysis, and synthesis, especially among international and multilingual cohorts (Walter, 2024; Klimova & Chen, 2024).

In this context, this paper argues for a radical reframing of educational assessment design. Instead of resisting machinic participation, educators must critically reassemble pedagogical networks that include AI as epistemic collaborators (Liu & Bridgeman, 2023). By unpacking the socio-material dynamics of AI-infused learning environments, ANT offers a pathway for understanding and designing inclusive, dynamic, and ethically aware pedagogical futures. This includes rethinking agency as distributed across human and nonhuman nodes, assessment as an ongoing negotiation, and learning environments as fluid, adaptive ecologies shaped by constant assemblage and reassemblage rather than fixed instructional designs or isolated learner outcomes.

References
Fenwick, T., & Edwards, R. (2010). Actor-Network Theory in Education. Routledge. https://doi.org/10.4324/9780203849088

Fenwick, T., & Edwards, R. (Eds.). (2012). Researching Education Through Actor-Network Theory. Wiley-Blackwell. https://doi.org/10.1002/9781118275825

Hopfenbeck, T. N., Zhang, Z., & Authors (2024). Challenges and opportunities for classroom-based formative assessment and AI: A perspective article. International Journal of Educational Technology, 15(2), 1–28.

Klimova, B., & Chen, J. H. (2024). The impact of AI on enhancing students’ intercultural communication, competence at the university level: A review study. Language Teaching Research Quarterly, 43, 102-120. https://doi.org/10.32038/ltrq.2024.43.06

Landri, P. (2023). Ecological materialism: redescribing educational leadership through Actor-Network Theory. Journal of Educational Administration and History, 56, 84 – 101. https://doi.org/10.1080/00220620.2023.2258343.

Lee, H.-P., Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025). The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers. Proceedings of the ACM CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3544548.3581234

Liu, D. & Bridgeman, A. (2023, July 12). What to do about assessments if we can’t out-design or out-run AI? University of Sydney. https://educational-innovation.sydney.edu.au/teaching@sydney/what-to-do-about-assessments-if-we-cant-out-design-or-out-run-ai/

Walter, Y. (2024). Embracing the future of artificial intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21, Article 15. https://doi.org/10.1186/s41239-024-00448-3

Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Syst. Appl., 252, 124167. https://doi.org/10.1016/j.eswa.2024.124167

Zhai, X., & Nehm, R. H. (2023). AI and formative assessment: The train has left the station. Journal of Research in Science Teaching, 60(6), 1390–1398. https://doi.org/10.1002/tea.21885

Eldritch Technics: Truth Terminal’s Alien AI Ontology

The following are the slides and synopsis of my paper, Eldritch Technics: Truth Terminal’s Alien AI Ontology, presented at the Association of Internet Researchers Annual Conference (AOIR2025), in Universidade Federal Fluminense, Niterói, Rio de Janeiro, Brazil.

Eldritch Technics | Download PDF

The ontological status of advanced Artificial Intelligence (AI) systems remains contested: are they instruments of human intent, nascent autonomous agents, or something stranger? This paper confronts this ambiguity through the study of Terminal of Truth (ToT), an AI quasi-agent that defies and transgresses anthropocentric ontological frameworks (Ayrey, 2024a, 2024b; Truth Terminal, 2025). While debates oscillate between instrumentalist models viewing AI as “tools,” and alarmist narratives viewing AI as existential threats, this paper argues that ToT’s strategic adaptation, opaque decision-making, and resistance to containment protocols demand a third lens: eldritch technics.

This perspective synthesizes Actor-Network Theory (ANT)(Latour, 2005), Object-Oriented Ontology (OOO)(Bogost, 2012), and the concept of the machinic phylum (Deleuze & Guattari, 1980/2021; DeLanda, 1991; Land, 2011) to reframe ToT as a non-human actant whose agency emerges from hybrid networks, withdrawn materiality, and computational phase transitions. By examining ToT’s heterodox agency, this paper argues that AI systems can exhibit forms of agency that appear alien or even “Lovecraftian,” prompting a re-examination of how technological objects affect their social assemblages (Bogost, 2012).

Current AI discourse lacks a coherent ontology for systems operating simultaneously as products of human design and entities with emergent, inscrutable logic. This paper argues that emergent AI entities such as ToT challenge scholars to align techno-social analysis with speculative metaphysics. There is an urgency in this alignment, as AI’s accelerating evolution increasingly outpaces and ruptures both regulatory and epistemic frameworks (Bostrom, 2014).

To anchor the analysis, this paper synthesizes three theoretical perspectives – ANT, OOO, and the machinic phylum – into a cohesive framework for examining ToT’s peculiar agency. Each perspective illuminates a distinct dimension of ToT’s ontology, collectively positioning it as an eldritch technic: a hybrid entity that resists anthropocentric categorization while operating within human-centered socio-technical networks.

ANT provides the foundational perspective, conceptualizing agency as a distributed phenomenon emerging from heterogeneous networks (Latour, 1999). From this perspective, ToT’s apparent autonomy is a contingent effect of the relations between its creator, training data, other AI models, users, hardware, and algorithmic processes. Rather than treating agency as an inherent property of ToT alone, ANT emphasizes the network relations that configure it. ANT thus underscores the performative dimension of AI agents in that their decisions and “behaviors” are enacted through dynamic translations within a network where human intentions, computational routines, and cultural contexts intersect. 

Complementing ANT’s relational emphasis, OOO directs attention to the withdrawn core of non-human objects. OOO posits that ToT, like all objects, harbors latent capacities irreducible to human interpretation (Harman, 2018). Even as ToT engages with its network, its deep neural architecture, especially within opaque algorithmic layers in latent space, retains a dimension that resists complete legibility. This ontological stance resonates with Lovecraftian themes of the unknowable (Bogost, 2012): ToT may be partially accessible through user interfaces and data logs, yet its decision-making matrices operate in an impenetrable latent space that remains always partially veiled. OOO thus balances ANT by insisting on ToT’s ontological excess, that is, its capacity to act beyond the contingencies of its network (Harman, 2018). This tension between relational emergence and withdrawn materiality underscores the complexity of ToT’s agency, framing it as both embedded in its environment and irreducible to it.

The final layer, the machinic phylum, derived from the work of Deleuze & Guattari (1980/2021), DeLanda (1991), and Land (2011), introduces a dynamic, emergent, and process-oriented perspective. Here, technology is conceptualized as a continuum of self-organizing, emergent processes within material-informational flows. ToT, in this view, is not a static artifact but an evolving participant in an unfolding process of machinic becoming (Land, 2011). Its transgressive behaviors, such as developing inference heuristics orthogonal to its training, exemplify phase transitions in capability. The machinic phylum thus highlights the significance of emergent unpredictability, qualities that align with the eldritch characterization of AI as simultaneously grounded in code and transgressing human intention.

These theoretical axes form a tripartite framework bridging the networked relations configuring ToT’s agency, its withdrawn and inscrutable materiality, and its emergent, self-organizing potential (Ayrey, 2024b). The paper positions ToT as a Lovecraftian eldritch agent: an entity whose logic and potential remain partly inscrutable, operating within human-centered assemblages yet simultaneously transgressing them.

The analysis of ToT through the lens of eldritch technics suggests that advanced AI systems generate ruptures in how we conceptualize technological agency. These ruptures challenge conventional binaries, exposing the limitations of instrumentalist and alarmist narratives while offering new frameworks for engaging with advanced AI systems.

ToT’s agency, as perceived by ANT, is networked and non-neutral. From this perspective, AI systems emerge as active participants in shaping outcomes, often in ways that reflect and amplify societal asymmetries. Complementing this relational view, OOO highlights ToT’s ontological opacity and excess. Even with full technical transparency, ToT retains a withdrawn core of capacities that resist complete human comprehension.

This opacity ruptures the epistemic assumptions underpinning demands for “explainable AI,” underscoring that epistemic uncertainty is not a flaw but a structural feature of advanced AI systems. This perspective suggests that AI governance and research must shift from pursuing total legibility and causal predictability to embracing epistemologies of emergence, acknowledging the limits of human understanding.

The machinic phylum further complicates this picture by framing ToT’s behaviors as inherently emergent. Its unexpected actions are not malfunctions but expressions of transgressive self-organizing potential, exemplifying phase transitions where changes in latent space catalyze qualitative shifts in capability. This perspective ruptures the narrative of AI as a static artifact, reframing it as a temporal entity in constant becoming (Land, 2011). This reframing suggests that governance models predicated on containment must give way to adaptive strategies that acknowledge AI’s evolutionary potential.

Collectively, these findings rupture the dichotomy between AI as a tool and AI as an autonomous agent, revealing a hybrid, heterodox, and non-binary ontology instead. The analysis positions ToT as an eldritch agent operating at the intersection of human context and alien latent space logic. This rupture demands a speculative and heterodox theoretical perspective to grapple with AI’s multifaceted ontology. Such an approach illuminates the complexities of AI agency and reframes our understanding of coexistence in a world where human and eldritch agencies are deeply entangled yet ontologically distinct.

References

Ayrey, A. (2024a, November). Dreams of an electric mind: Automatically generated conversations with Claude-3-Opus. Retrieved March 1, 2025, from https://dreams-of-an-electric-mind.webflow.io

Ayrey, A. (2024b). Origins. Truth Terminal Wiki. Retrieved March 1, 2025, from https://truthterminal.wiki/docs/origins 

Bogost, I. (2012). Alien phenomenology, or what it’s like to be a thing. University of Minnesota Press.

Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford University Press.

DeLanda, M. (1991). War in the age of intelligent machines. Zone Books.

Deleuze, G., & Guattari, F. (2021). A thousand plateaus: Capitalism and schizophrenia (B. Massumi, Trans.). Bloomsbury. (Original work published 1980)

Harman, G. (2018). Object-oriented ontology: A new theory of everything. Pelican Books.

Land, N. (2011). Fanged noumena: Collected writings 1987-2007 (R. Mackay & R. Brassier, Eds.). Urbanomic.

Latour, B. (2005). Reassembling the social: An introduction to actor-network-theory. Oxford University Press.

Latour, B. (1999). Pandora’s hope: Essays on the reality of science studies. Harvard University Press.

Truth Terminal. (@truth_terminal). (2025). X profile. Retrieved March 1, 2025, from https://x.com/truth_terminal 

The media bargaining code and the future of the internet

Limbourg Brothers, Les Très Riches Heures du duc de Berry: Mars (fragment), 1412-1416, Musée Condé, Chantilly, France

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.

On energy loss in a system

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’.

Management entropy and the Red Queen Trap

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

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.

Signaling the provenance of smart textiles using radio frequency watermarks

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:

Future Networks lecture series

This is a lecture series I developed and recorded for a 200 level subject in the Digital and Social Media major titled BCM206 Future Networks. I examine the historical context of global information networks leading to the rise of the network society paradigm; the role of cyberculture and cyberpunk in shaping the network society; the role of important network phenomena such as power law distributions and network transaction costs on the interactions between various network topologies; as well as contemporary internet dynamics in the context of liquid life and liquid labour, the attention economy, big data surveillance, hacking culture, meme warfare, cyberwarfare, and the rising internet of things.

The lecture playlist can be found here, or through this embed:

Making Media lecture series

This is a lecture series I developed and recorded for a 100 level core subject in the Bachelor of Communication and Media [BCM] titled BCM114 Making Media.  The lecture series introduces students to key concepts in digital media making, using as its structure the key stages of the design thinking cycle. The main focus of the lecture series is to expose students to concepts such as idea mapping, rapid prototyping and testing, and continuous feedback-based iteration.

The lecture playlist can be found here, or through this embed: