Wetware's Default: A Diagnosis of Systemic Myopia under AI-Driven Autonomy

By Ihor Ivliev @ 2025-07-03T23:21 (+1)

Abstract

This article reframes the discourse on artificial intelligence risk, positing that the central crisis is not a potential future machine failure but a demonstrable present human-system failure. We diagnose a "systemic myopia" - a pathology rooted in biological imperatives and amplified by institutional logic - that creates a powerful default trajectory toward short-term optimization at the expense of long-term survival. The analysis models the evolution of the AI development ecosystem as a game-theoretic shift from chaotic competition to an unstable oligopolistic equilibrium, driven by material chokepoints. We argue that AI functions as a critical accelerant to the underlying myopia, primarily by causing a rapid, observable degradation of the collective information commons. This dynamic creates a "Paradox of Governance", where the system is structurally incapable of adopting necessary controls. The paper concludes that meaningful intervention is unlikely to arise from voluntary policy, but may become possible by applying "hard friction" to the system's physical substrate during brief "normative fracture points" created by escalating, system-induced crises.

 

Introduction: Reframing the Crisis

The public and technical discourse surrounding artificial intelligence is captured by a compelling but dangerously narrow narrative: the challenge of controlling a future superintelligence. This paper argues that this framing is a grand misdirection. The true crisis is a pre-existing, demonstrable misalignment of our own civilization - a systemic pathology we term "civilizational myopia". This analysis provides a diagnosis of this condition and the mechanisms by which AI acts as its powerful accelerant. We will first dissect the "Myopic Gradient", the continuous fault-line of short-termism running from human biology to institutional design. Second, we will analyze the game-theoretic logic that governs the AI ecosystem, modeling its evolution into an unstable, crisis-prone equilibrium. Third, we will explore AI's role as an accelerant, focusing on its immediate, observable impact on our collective epistemic capacity. Finally, we will examine the resulting "Governance Impasse" and assess the limited, physically-grounded avenues for intervention that may emerge not from foresight, but from systemic crisis.

 

Part 1: The Diagnosis: The Myopic Gradient as the System’s Default Trajectory

1.1. The Biological Bedrock 

The analysis begins at the individual level. The source code of our predicament is not a flaw in our character, but a feature of our biology. The human brain is a "cognitive miser", an organ shaped by evolution to minimize its own immense metabolic costs. This principle of "metabolic thrift" creates a default preference for low-energy, intuitive heuristics over costly, deliberate reasoning. This baseline is compounded by the ultimate temporal constraint: mortality. As finite beings with finite careers, we are neurologically and rationally programmed to apply a massive discount factor to long-term, abstract risks. This phenomenon, well-documented in behavioral economics as hyperbolic discounting, produces "mortality-weighted myopia". A probabilistic catastrophe decades from now simply cannot compete for cognitive resources against the concrete, immediate pressures of survival and advancement. This is not a moral failing but a biological operating principle.

1.2. Institutional Amplification 

This individual-level bias does not remain contained. It aggregates upward, becoming codified in the logic of our most powerful institutions. Corporate governance structures, optimized for the legible metrics of quarterly earnings reports, are a direct institutional reflection of this short-term preference. Likewise, political systems, driven by the relentless cadence of election cycles, are rationally incentivized to prioritize immediate, visible benefits over long-term, invisible risk mitigation. This is the path of least resistance. The emergent result is a system that allocates resources according to its true, myopic priorities. This dynamic is starkly visible in the United States, the world’s leading AI investor. According to the 2025 AI Index Report, U.S. private AI investment reached $109.1 billion in 2024. In the same year, a surge in funding for the ethics of AI in medicine - a key area of public interest and safety - saw the National Institutes of Health (NIH) allocate $276 million. Even with this significant and crucial investment in ethical considerations, the resulting resource skew remains profound, with a ratio of nearly 400:1 favoring private capability investment over this targeted public interest oversight.

This national imbalance is mirrored globally. Worldwide corporate AI investment stood at a record $252.3 billion, dwarfing all identifiable public funding for safety and ethics. This immense financial momentum toward acceleration - contrasted with comparatively minuscule and sometimes precarious public interest funding, evidenced by events like the U.K. government's withdrawal of over £1 billion in promised AI infrastructure and research funding - paints an undeniable picture. Capital and talent are overwhelmingly channeled toward capability development, not foundational restraint.

1.3. The Default, Not Destiny 

This myopic gradient constitutes the system's powerful default trajectory, but it is not an iron law of history. The existence of rare but successful counter-examples proves that long-term governance is possible. The 750-year adaptive planning of the Dutch water boards or the legally mandated, inter-generational contracts of Norway's sovereign wealth fund are not miracles - they are feats of institutional engineering. They demonstrate that long-termism requires deliberate, legally entrenched, and robustly funded structures designed explicitly to provide "friction" against the powerful pull of the myopic default. Absent these exceptional designs, the default prevails.

 

Part 2: The System's Logic: From Chaotic Competition to Unstable Equilibrium

2.1. The Micro-Level Conflict: The Dual-Function of Corporate Safety 

To understand the system’s evolution, one must first look inside its primary actors: the frontier AI labs. Their public posture on safety is best understood as serving a "dual function". On one hand, these organizations contain teams genuinely dedicated to technical risk mitigation, driven by founder ideology or a rational fear of catastrophic accidents. This is evidenced by their detailed research papers and ethics-team blog posts. On the other hand, these same organizations have business and legal units dedicated to strategic market shaping - lobbying for favorable regulations, managing liability, and erecting "regulatory moats" to disadvantage competitors. This function is evidenced by their investor presentations, which invariably highlight capability breakthroughs, not restraint. The core diagnostic claim is that the system's short-term incentive landscape ensures that the market-shaping function consistently dominates the risk-mitigation function in determining the corporation’s final, observable behavior.

2.2. The Macro-Level Game: An Unstable Oligopolistic Equilibrium 

This internal conflict scales up to the ecosystem level, where the game itself transforms. The initial phase of AI development resembled a multi-player Prisoner's Dilemma: a chaotic race with many actors, where the rational move for all was to defect from restraint and accelerate development. However, two material factors fundamentally altered the game's structure: the emergence of extreme hardware chokepoints, with a single firm controlling over 75% of the AI accelerator market, and the immense, multi-hundred-billion-dollar capital requirements for frontier model training. These barriers to entry filtered the field from many players to a few, creating an oligopoly. For this oligopoly, the game changes.

2.3. Oscillation, Not Stasis 

The emergent system does not settle into a stable cartel but rather an unstable equilibrium, oscillating between two poles.

 

Part 3: The Accelerant: Epistemic Degradation and Hypothesized Cognitive Risk

3.1. The Observable Crisis: The Degradation of the Information Commons 

AI’s role as an accelerant to systemic myopia is most immediate and demonstrable in its effect on our collective epistemology. Frontier models are, fundamentally, "plausibility engines", optimized to generate text that is stylistically coherent and authoritative-sounding, irrespective of its factual grounding. This capability collapses the economics of information warfare. The cost to generate a flood of plausible falsehoods - fabricated reports, synthetic social media campaigns, deepfakes - drops to near zero. The cognitive, temporal, and financial cost for society to rigorously verify and debunk this deluge remains high. This fundamental economic imbalance guarantees a continuous degradation of the information commons, eroding the very possibility of a shared, reality-based discourse. This is not a future risk - it is an observable structural change, evidenced by the documented surge in AI-generated disinformation.

3.2. The Plausible Hypothesis: Cognitive Skill Atrophy 

This direct epistemic crisis is coupled with a plausible, longer-term hypothesis regarding individual cognition. The consistent offloading of core mental tasks - planning, summarizing, navigation, critical analysis - to AI assistants creates a risk of skill atrophy. This "use it or lose it" effect, supported by analogies from other technologies like the GPS and its documented impact on innate navigational abilities, warrants urgent empirical study. While this remains a hypothesis, it points toward a terrifying feedback loop: a populace whose information environment is being systematically polluted is simultaneously being equipped with tools that may degrade its capacity for the critical reasoning required to navigate that pollution.

 

Part 4: The Governance Impasse: A System Programmed to Reject Its Cure

4.1. The Governance Stack 

The failure to govern this crisis can be analyzed via a three-layer "Governance Stack". At the top is the Strategic layer (the geopolitical game), below it is the Policy layer (treaties, laws, regulations), and at the foundation is the Physical layer (the material substrate of compute, hardware, and data centers). Any effective governance at the top two layers is entirely dependent on exerting credible control over the foundational physical layer.

4.2. The Paradox of Governance 

It is precisely at this foundational layer that the system's resistance is strongest. This creates the "Paradox of Governance": the system's most powerful actors, who are the primary beneficiaries of the myopic, accelerant-driven status quo, are structurally incapable of implementing the "hard levers" of physical control that would be necessary to alter the trajectory, as doing so would directly undermine their market power and strategic advantage. The system, as an emergent property of its misaligned incentives, is programmed to reject its own cure.

4.3. The Mechanisms of Inertia 

This impasse is maintained by two key dynamics. The first is the "Clockspeed Mismatch", where our linear, incremental, human-speed governance systems are fundamentally outpaced by the non-linear, exponential progress of the technology. The second is the "Warning Shot Dilemma", a terrifying strategic paradox wherein the political will for decisive action may only be achievable after a limited catastrophe occurs, with no guarantee that such a "warning shot" will be survivable, correctly interpreted, or arrive in time.

 

Part 5: Prognosis and Avenues for Intervention

5.1. The High-Probability Trajectory & The Mechanism for Change 

If nothing fundamental changes, the high-probability forecast is a trajectory toward a "slow-burn dystopia punctuated by reality-shock crises". Voluntary, foresight-driven change is unlikely due to the governance paradox. However, this does not make change impossible. The key counter-deterministic concept is the "Normative Fracture Point". As the system's default trajectory generates escalating negative externalities - economic shocks, social dislocation, catastrophic accidents - it builds immense systemic stress. At a critical threshold, this stress can precipitate a "fracture": a rapid collapse in the legitimacy of the status quo, creating a brief political window where the "Paradox of Governance" is temporarily suspended.

5.2. The Realist Intervention: Applying "Hard Friction" 

Strategic efforts should therefore be redirected from crafting ideal policies for a rational world to preparing to act decisively within these rare and chaotic windows of opportunity. During a normative fracture, when the normal rules of politics are suspended, the only interventions likely to be effective and durable are those that apply "hard friction" directly to the system's physical and economic structure. These are not policy "wishes" but material constraints.

5.3. Falsifiable Metrics and Levers 

A realist approach requires tracking non-performative metrics and preparing concrete levers. The most critical metric to monitor is "compute-breakout time": the estimated time required for a determined actor to assemble an illicit, frontier-scale AI training cluster from grey-market or stolen hardware. If this time shrinks below our cycles of inspection and verification, all governance becomes theater. During a fracture event, prepared actors must be ready to implement hard-friction levers, such as mandating tamper-evident identifiers in all frontier-class hardware or enforcing standards of verifiable digital provenance that make it economically cheaper to produce truth than to forge it.

 

Conclusion: The Final Mismatch

This analysis presents a sobering diagnosis. The uncontainable engine is not the future AI but our own civilizational operating system, whose ancient, myopic logic is now fused to an engine of exponential speed. This engine compresses the timeline for our existing problems while simultaneously degrading the epistemic capacity of its human pilots. The trajectory is set because it is the path of least resistance. The central challenge is therefore not a technical problem of AI alignment, but a systemic problem of managing the collision between our slow-moving biology and the consequences of its own digital creation. Meaningful change is unlikely to be born of foresight - it will depend on our ability to recognize and exploit the rare fractures that appear in the system under the weight of its own self-generated crises, applying material friction to its core before the cracks seal over.

 

Paper: Warped Wetware