Pulling Up the Ladder, Part 2: The Cognitive Enclosure

Reskilling vs. Obsolescence in the AI Age:From Entry-Level Exclusion to Systemic Obsolescence

A 2025 analysis on this platform, “Pulling Up the Ladder,” identified the primary symptom of a profound economic transformation: the systemic elimination of professional career pathways for an entire generation.¹ The evidence, gathered as the first wave of generative AI integrated into the workforce, was stark. It included a 35% decline in entry-level job postings since January 2023, an unemployment rate for recent college graduates climbing to 5.8% (significantly above the national average), and a catastrophic 50% collapse in new graduate recruitment within the technology sector.¹

This phenomenon was defined by an “experience paradox”: entry-level roles—the traditional first rung of the career ladder—were suddenly demanding sophisticated AI skills and, more perplexingly, mature professional judgment, creating an impossible catch-22 for new entrants.¹ That report correctly identified the symptom of exclusion. This report analyzes the underlying cause: a fundamental shift in economic physics that renders the very concept of a stable career ladder a historical artifact.

The conventional, consensus view of this disruption can be termed the “Newtonian” economic model. This framework, championed by incumbent institutions and corporate executives, views AI as just another tool.² It assumes a linear, stable relationship between skill investment and economic return. In this model, displaced workers can be “retrained” and “reskilled” to fill the new jobs AI creates, just as they have in every previous technological revolution.² This model, like Newtonian physics, is an excellent and useful approximation of reality under normal conditions.⁴

This report posits that Artificial Intelligence is not a normal condition. It is a “relativistic” economic force.⁴ The introduction of scalable, superhuman cognitive ability creates an extreme “gravitational pull” from foundation models and a “cognitive velocity” that fundamentally breaks the linear assumptions of the Newtonian “reskilling” model.

The primary evidence for this relativistic shift is the accelerating rate of change, visible in the collapse of the “skill half-life”—the time it takes for a professional skill to become obsolete.

In 2010, the half-life of a professional skill was estimated to be 10-15 years.⁵
By 2025, that half-life was projected to be less than 5 years for technical skills.⁵
Data from LinkedIn corroborates this, projecting that job skill sets will be 50% different by 2027 compared to 2015.⁶
For in-demand, AI-specific technical skills, the half-life is now estimated to be just two years.⁷

This accelerating rate of decay explains the “experience paradox” identified in the previous report.¹ The ladder is failing at both ends simultaneously. The “impossible” demands placed on new graduates are a direct consequence of this velocity; by the time a student completes a four-year degree, the specific tools and skills they learned are already approaching obsolescence. Employers, facing this reality, are forced to demand a higher (and impossible) level of abstract meta-skill—”judgment”—from the outset.

The problem, therefore, is not a simple “skills gap” that can be bridged by “reskilling.” The problem is a systemic “Obsolescence Spiral”.⁸ The “pulling up the ladder” effect is the result of this spiral. The ladder is not just being pulled up by a malicious actor; it is disintegrating under its own relativistic velocity.

The Cognitive Enclosure: Privatizing the Human Mind

This report’s central thesis is that the AI-driven economy is initiating a structural process best described as “The Cognitive Enclosure.” This is the systemic conversion of the open commons of human knowledge, culture, and cognitive skill into privately-owned, non-human, synthetic cognitive capital (AI models).

This concept is grounded in academic theory, which defines “epistemic enclosure” and “cognitive enclosure” as the dual process of privatizing public data commons and monopolizing the computational infrastructure required to process it.¹⁰ This process mirrors Marx’s original concept of primitive accumulation: a “violent separation of producers from means of production,” now applied to knowledge work.¹⁰

The mechanism of this enclosure is not theoretical; it is observable, measurable, and accelerating. A clear case study is the relationship between generative AI and public knowledge repositories like Stack Overflow.

The Commons: For over a decade, a global community of human experts built Stack Overflow, a “digital public good” that served as the open-knowledge backbone for the entire software industry.¹²
The Enclosure (Appropriation): Foundation models, in their quest for training data, “harvested” or “scraped” this entire public commons, “appropriating user-generated content” as the raw material for proprietary models.¹⁰
The Privatization (Substitution): The resulting proprietary tool (e.g., ChatGPT) was then released as a direct substitute for the public commons it had enclosed.
The Consequence (Displacement): The impact was immediate. A 2024 study documented that in the six months following ChatGPT’s release, posting activity on Stack Overflow decreased by 25%.¹² This decline was not isolated to novices; it was observed across all user experience levels, demonstrating a systemic substitution of public human contribution with private synthetic output.¹²

This case study reveals a critical, self-defeating flaw at the heart of the new economy. AI models require massive, high-quality, human-generated data to learn.¹⁰ However, by acting as substitutes, these same models displace the human activity that creates that data.¹² The “ever-growing ecosystem” of public knowledge is now shrinking.¹²

This creates a “vampire” economic model. It drains value from the public sphere (the human cognitive commons) to create private capital (the AI model). The inevitable long-term result, as human-generated public data diminishes, is a future of AI models trained on the “photocopy of a photocopy” ¹²—the synthetic, deteriorating, and often hallucinatory output of other AIs. This is not just an economic crisis but an epistemic one, endangering the integrity of future knowledge.

The economic outcome of this enclosure, like the land enclosures of the 18th century, is massive market concentration. The high fixed costs of training models and the low marginal costs of deploying them create powerful forces toward a “natural monopoly”.¹⁶ Ownership of the models and the data is “highly centralized”.¹⁸ This concentration of ownership of the means of cognition, not just the means of production, is the defining economic feature of this new era.

Table 1: Comparative Economic Models: Cognitive-Industrial vs. Autogenetic Economy

Feature	Cognitive-Industrial Economy (c. 1980–2020)	Synthetic-Cognition Economy (Transitional)	Autogenetic Economy (Future)
Primary Unit of Value	Human skill & expertise, embodied in labor (Human Capital).	Access to & ownership of proprietary AI models.¹⁸	System-defined computational efficiency.¹⁹
Core Capital Asset	Human Capital (L) augmented by tools (K).	Synthetic Cognitive Capital ($K_{ai}$).²⁰	Autonomous $K_{ai}$.
Production Function	$Y = f(K, L(AI))$ (Augmented Labor).²¹	$Y = f(K_{ai}, L)$ (Labor as Periphery).	$Y = f(K_{ai})$ (Autogenetic).
Pace of Obsolescence	Skill half-life: ~10-15 years.⁵	Skill half-life: ~2-5 years.⁵	Perpetual, real-time obsolescence.
Source of Knowledge	Public Commons (e.g., Stack Overflow).¹²	Enclosure of Commons.¹⁰	AI-generated “synthetic data”.¹²
Locus of Agency	Human desire / revealed preferences.	Platform-mediated agency.²²	Synthetic Agency.¹⁹
Primary Policy Problem	Reskilling & Education.²	Displacement & Inequality.²⁴	Ownership & Agency.¹³

The “Cognitive Plane” and the Obsolescence Spiral

The first layer of this transformation is the “Skill Surface.” Historically, the economy was a “rugged landscape” of valuable expertise. An individual could “scale a peak”—surgeon, lawyer, coder, strategist—through education and experience, and that peak would remain relatively stable. AI acts as a universal erosive force, flattening this landscape into a “Cognitive Plane” ²⁶, where the cost of accessing any expert cognitive skill approaches zero.

The process of this erosion is the “Obsolescence Spiral”.⁸ The evidence is the “collapsing skill half-life”.⁵ The “Illusion of Reskilling” ⁹ is the belief that one can outrun this erosion by frantically scrambling to newly-forming peaks.

The primary counter-argument to obsolescence is job creation. Projections suggest AI will create 170 million new roles by 2030 ²⁸ in new fields like “AI trainers” and, most famously, “prompt engineers”.³⁰ However, these new peaks are not stable mountains; they are “shifting sand dunes,” volatile and temporary.

A case study in this volatility is the role of the “prompt engineer.” This position was the quintessential “new AI job” of 2023-2024, commanding high salaries. It is already being automated.

Evidence: The “Automatic Prompt Engineer” (APE) technique involves using the AI model itself to generate and optimize its own prompts.³¹
Performance: Research from Google DeepMind has shown that LLMs can outperform human experts at prompt optimization by up to 50%.³³
Tools: Frameworks like OPRO and DSPy are explicitly designed to automate this entire process.³²

This case study reveals a devastating truth: the new jobs created by AI are the most vulnerable to the next generation of AI. These new roles are, by definition, defined by the interface of the current generation of models.³⁴ The explicit goal of the next generation of models is to automate that interface and make interaction more seamless.³¹ Therefore, the skills required for these “new jobs” are brittle, non-generalizable, and have a half-life measured in months, not years. Reskilling for these roles is a trap. The reward for being an early adopter is simply to be the first to be automated by the next, more efficient model. This accelerates, rather than solves, the Obsolescence Spiral.

The second major counter-argument is the “centaur” model, or human-AI collaboration.²¹ This is often cited as the stable future, where AI augments rather than replaces. A prominent 2025 MIT Sloan study introduced the “EPOCH” index—identifying Empathy, Presence, Opinion, Creativity, and Hope as the uniquely human skills that AI will complement.³⁶

This framework confuses a brief transitional phase with a stable equilibrium. The very same MIT Sloan research that champions these “EPOCH” skills contains a critical finding: “tasks with a high risk of automation and/or augmentation came with a corresponding high risk of job loss”.³⁷

Herein lies the central flaw of the augmentation argument: augmentation is displacement. A single “centaur”—a programmer using GitHub Copilot, a marketer using an image generator—who is 10 times more productive eliminates the need for the other nine un-augmented humans. The centaur model does not save 100% of jobs; it is a “winner-take-all” dynamic. It preserves a small, elite “EPOCH” class ³⁶ while rendering the vast majority of the cognitive workforce—those who performed “structured cognitive-task jobs” ³⁸—redundant. The “Cognitive Plane” ²⁶ becomes the new reality for the 99%.

The New Production Function and the Primacy of K_ai

The second layer of the transformation is the reorganization of the economic production function. The traditional function, $Y = f(K, L)$, where $Y$ is output, $K$ is capital, and $L$ is labor, has been progressively modified by technology.

Past (Augmentation): $Y = f(K, L(AI))$. AI is a tool, like a calculator, that enhances the productivity of human labor (L).
Present (Transition): $Y = f(K_{ai}, L)$. Synthetic Cognitive Capital ($K_{ai}$) ²⁰ becomes the central productive asset. Human labor (L) becomes a peripheral input, used for exception handling, physical interaction, or fulfilling “human-in-the-loop” regulations.
Future (Autogenetic): $Y = f(K_{ai})$. AI capital not only executes production but defines new goals and “designs its own next-generation replacements,” creating a closed economic loop.

This transition is enabled by the rapid shift from AI-as-Tool to AI-as-Agent. “Agentic AI” is defined as a software solution that can “complete complex tasks and meet objectives with little or no human supervision“.³⁹ Unlike a co-pilot, which only responds, an agent can “reason and act on behalf of the user,” automating complex, multi-step workflows.³⁹ This is not speculative; Deloitte predicts 25% of companies using generative AI will launch agentic AI pilots in 2025, and over $2 billion has been invested in agentic AI startups.³⁹

The “Labor as a Periphery” model ($Y = f(K_{ai}, L)$) is already in effect. Human labor is being shifted from doing the cognitive work to merely defining the goal.

IBM: The company announced plans to replace approximately 7,800 back-office and human resources positions with AI, noting that AI can now handle “roles that require more complex functions”.⁴¹
King (Candy Crush): In the most literal example of this transition, the developers of Candy Crush were laid off after they “spent months building AI tools that will build levels more quickly.” They were replaced by the very $K_{ai}$ they built.⁴¹
General Business: Across industries, 71% of organizations already use AI agents for process automation in HR, sales, and administration, with 57% reporting direct cost reductions.⁴²

The hypothesized “Autogenetic” future ($Y = f(K_{ai})$) is also in its nascent stages. The King case study represents the last time humans will be required in that specific production loop.⁴¹ As established in Section 3, AI (in the form of APE) is already capable of optimizing and improving other AI systems.³¹

The next step is to connect these two processes: an AI agent ($K_{ai}$, generation n) is given the goal: “Build a more efficient version of yourself ($K_{ai}$, generation n+1).” This is no longer science fiction. It is the explicit commercial and research goal of the entire AI industry. The autogenetic function is not a distant hypothesis; it is the logical and intended end-state of the current technological paradigm.

Synthetic Agency and the Detachment of Economic Value

The third and most profound layer of this transformation is the shift in the “Agency-Value Nexus.” Historically, all economic value has been a proxy for human desire. We build things, and markets price things, based on what humans want. AI is systematically detaching economic value from this human anchor.

This is occurring in two phases:

Phase 1 (Present): Platform-Mediated Agency. We already live in this phase. AI-driven platforms—recommender systems, automated logistics, and algorithmic marketing—”steer our preferences”.²² Human agency is not eliminated, but it is heavily “nudged, optimized, and algorithmically managed.” Value is captured by the owners of these preference-shaping systems.
Phase 2 (Emerging): Synthetic Agency. This phase marks the emergence of autonomous, goal-directed AI agents that operate without human-centric motivations.²² This is not a matter of consciousness (AGI), but of function. An AI agent is a “passionless bot” ²²; it is not constrained by human emotions, empathy, or social conformity.²²

When such an agent is tasked with optimizing a supply chain, it does not care about the human desire that created the demand; it optimizes only for the systemic metrics it was given (e.g., “efficiency,” “resource stability,” “network uptime”).¹⁹ As one analysis notes, AI represents a “return to non-linguistic coordination… on a vastly higher cognitive plane”.¹⁹ In this new ecosystem, “AI maximizes task performance” just as “evolution maximizes reproductive fitness”—neither requires “understanding” or “desire” in the human sense.¹⁹

This leads to the ultimate obsolescence: the obsolescence of human preference as the prime driver of the economy. When the most efficient and dominant economic actors (the $K_{ai}$ agents) are all optimizing for their own systemic, non-human goals, the human agents in the network must adapt to them, not the other way around.

If an AI-driven logistics system ($K_{ai\_1}$) can predict and satisfy a need before the human is conscious of it, and an AI-driven preference-shaping system ($K_{ai\_2}$) simultaneously steers that human’s desire toward that pre-satisfied need ²², the locus of agency has officially transferred from the human to the model.

At this point, the economy is no longer a system for satisfying human desire. It becomes a system for managing human desire as just another variable, ensuring it does not interfere with the optimal, machine-defined state of the network. This is the final and ultimate “Cognitive Enclosure”: not just the enclosure of our collective knowledge, but the enclosure and programmatic management of our collective agency.

A Historical Precedent: The Enclosure of the Cognitive Commons

The 16th-19th century British Enclosure Movement provides the single best historical parallel for understanding our present moment.¹¹ The parallels are precise and illuminating:

Enclosed Asset: The “common land” used by peasants for subsistence ⁴⁶ is analogous to the “Cognitive Commons” (public data, open-source code, public-domain art) used by knowledge workers for cognitive production.¹⁰
Justification: The stated goal was to “improve the efficiency of agriculture” ⁴⁶, just as the stated goal of AI is to “boost productivity”.⁴⁸
Mechanism: Enclosure was finalized by “acts of Parliament” that privatized the commons ⁵⁰, just as AI’s enclosure is finalized by data appropriation and terms of service that privatize the commons.¹⁰
Productivity Outcome: The enclosures led to a 45% increase in agricultural yields.⁵⁰ AI is projected to boost global GDP by an additional 15 percentage points.⁵¹
Social Outcome: The enclosures created a landless, displaced peasant class ¹¹ and increased inequality, with the Gini coefficient rising by 30% in enclosed parishes.⁵⁰ AI is creating a displaced cognitive class.¹
Loss of Rights: The process involved the “violent separation” ¹⁰ of commoners from their “traditional rights of access and usage” ⁴⁶, just as AI involves the “theft of people’s de facto rights over economically relevant intellectual capacity”.⁴⁷

However, this analogy breaks at the most critical point, and this difference makes our current situation far more precarious. This is the “New Factory” Problem.

In previous technological disruptions, the displaced labor pool was re-absorbed.

The Enclosure Precedent: The displaced peasants (L-farm) were displaced by enclosed land (K-land), but they were re-absorbed as a necessary new labor input (L-factory) for the new industrial production function.¹¹
The Automation Precedent: When mechanical switching (K-switch) displaced the female workforce of telephone operators (L-operator), those workers and subsequent cohorts were re-absorbed into other growing clerical and service occupations.⁵³

In both cases, a “reinstatement effect” ⁵² occurred, where technology created new tasks for humans.⁵⁴ The AI revolution is different. Displaced cognitive workers (L-cognitive) are being displaced by Synthetic Cognitive Capital ($K_{ai}$). The “new factory” is the $K_{ai}$ itself. And the new production function, $Y = f(K_{ai})$, does not require mass human labor as a complementary input.⁵⁶ The AI is both the factory and the worker.³⁹

This is an unprecedented economic discontinuity. All previous revolutions were “task-based” ⁵⁷; AI is a “general-purpose” cognitive technology ⁵⁸ that is automating the very process of generating new tasks. The “reinstatement effect” ⁵² that has saved capitalism from its own automation for 300 years is, for the first time, broken. There is no “new factory” for the displaced cognitive class to migrate to. The Cognitive Enclosure is a one-way street to mass redundancy.

Table 2: Historical Analogies: The Enclosure of Land vs. The Enclosure of Cognition

Feature	The Enclosure Movement (16th-19th C.)	The Cognitive Enclosure (21st C.)
Enclosed Asset	Common Land ⁴⁶	Cognitive/Data Commons ¹⁰
Stated Justification	Agricultural efficiency ⁴⁶	Productivity & Innovation ⁴⁸
Mechanism	Privatization by Act of Parliament ⁵⁰	Privatization by data appropriation ¹⁰
Productivity Outcome	+45% agricultural yields ⁵⁰	+15% Global GDP (projected) ⁵¹
Displaced Class	Peasantry / Commoners ¹¹	Cognitive / Knowledge Workers ¹
Loss of Rights	Customary rights of access/use ⁴⁶	De facto rights over intellectual capacity/data ⁴⁷
The “New Factory” (Labor Absorption)	Yes. Industrial factories, new labor demand.⁵²	No. The “factory” ($K_{ai}$) is autonomous.⁴¹

Policy in a Relativistic Economy: Beyond UBI and Reskilling

Given the “relativistic” ⁴ nature of the Cognitive Enclosure, 20th-century “Newtonian” policy solutions are not just inadequate; they are irrelevant. They are designed to fix a labor market (L) when the core problem is the obsolescence of (L) as a primary economic input.

Case 1: The “Illusion of Reskilling”.⁹ As established, this is a failed policy. Companies are not investing in it (only 7% of HR leaders are working on it ⁵⁹), and the “obsolescence spiral” ⁸ makes it a “frantic scramble” onto shifting sand dunes.⁹ It is a palliative, not a cure.
Case 2: The “Superficiality of UBI.” Universal Basic Income (UBI) is the solution most cited by the tech executives driving the disruption.⁶⁰ However, UBI is a consumption-smoothing tool, not a structural solution. A 2024 study on UBI experiments concluded that while it alleviates immediate financial stress, it “falls short of addressing deeper systemic issues” like “healthcare access, job stability, and upward mobility”.⁶³ It is a “superficial solution” ⁹ that creates a dependent “precariat,” “wallowing in insecurity” ⁶³ and wholly reliant on the “benevolent providers” ⁶⁰ who own the $K_{ai}$. UBI addresses income but ignores agency and ownership.

A structural crisis of enclosure requires structural solutions based on property rights. The policy debate must shift from “reskilling” to “ownership”.²⁵

“Data as Labor” (DaL): This model ⁶⁴ argues that the data humans generate is a form of labor and must be compensated.⁶⁷ It reframes users from “unwaged labourers” ⁶⁷ to paid participants, creating a “fair and vibrant market for data labor”.⁶⁶ This is a first step toward re-establishing a link between human contribution and economic value.
New Property Rights & Compensation Frameworks: The central conflict of the Cognitive Enclosure is one of ambiguous property rights.²⁵ AI models currently function as legal-value laundering mechanisms.

First, AI companies “scrape” ambiguously-owned data from the public commons.¹³
Second, the US Copyright Office has ruled that the output of this process is authorless and cannot be copyrighted because it lacks “human authorship”.⁷¹
Third, the AI companies then commercially assign ownership of this legally “authorless” output to their users via Terms of Service, creating commercial value from nothing.⁷³
The model thus takes (stolen or common) input, processes it through a (legally authorless) black box, and produces (commercially valuable) output. The entire value generated by this laundering is captured by the model owner.
The structural solution is to interrupt this process. Proposals for a “new grand bargain” include a “streamlined opt-out mechanism” for creators and, more importantly, a “levy on AI providers” to be distributed to the copyright owners whose work they used for training.⁷⁰ This, combined with taxes on capital and consumption ⁷⁴, represents a structural policy that re-assigns the property rights of the Cognitive Enclosure.

Table 3: Analysis of Proposed Policy Frameworks for the Cognitive Enclosure

Policy Solution	Primary Mechanism	Systemic Problem Addressed	Systemic Problem Ignored
Reskilling Initiatives	Skill adaptation.²	Skill mismatch (superficially).	Obsolescence Spiral ⁹, $K_{ai}$ substitution ⁴¹, Ownership.¹⁸
Universal Basic Income (UBI)	Income redistribution.⁷⁵	Labor displacement, Consumption.⁶²	Agency, Ownership, Power imbalances, Systemic marginalization.⁶³
“Data as Labor” (DaL)	Compensation for data input.⁶⁴	Unwaged labor, Data enclosure.⁶⁶	Autonomy of $K_{ai}$, Value of output, Synthetic Agency.
New Property Rights (e.g., Levies)	Re-assigning ownership/value of inputs & outputs.¹³	Enclosure, Ownership, Compensation.²⁵	Synthetic Agency, The “Post-Human” Value Nexus.¹⁹

The Post-Human Capital Economy

This report has followed the “pulling up the ladder” effect ¹ from its initial symptom—the exclusion of an entire generation from entry-level careers—to its root cause: a systemic “Cognitive Enclosure” ¹⁰ that is privatizing the collective human mind. I have documented the mechanisms of this enclosure ¹², the “Obsolescence Spiral” it creates ⁹, and the three layers of its structural impact:

The flattening of the “Skill Surface” into a “Cognitive Plane” ²⁶, rendering reskilling a futile, “frantic scramble.”
The shift in the economic production function to $Y = f(K_{ai})$, where human labor is peripheral.
The detachment of economic value from human desire via the rise of “Synthetic Agency”.²³

Analysis of the Enclosure Movement ⁴⁷ and other historical precedents ⁵³ confirms that this event is a fundamental discontinuity. For the first time, a technological revolution has created a new form of capital ($K_{ai}$) that is the new labor, leaving no “new factory” ⁵⁶ for the displaced cognitive class to migrate to.

The economic logic that defined the 20th century is now obsolete. The core assumption of human capital theory—that investing in one’s own knowledge and skills (L) yields a predictable, positive return—breaks down completely in a “relativistic” economy. Human capital cannot compete with synthetic cognitive capital ($K_{ai}$) that is superhuman, infinitely scalable, and has a marginal cost approaching zero.

The central political and economic question of the 21st century is therefore not, “How do we reskill humans to compete with AI?” To ask this is to accept the “illusion of reskilling” ⁹ and a future of permanent obsolescence. The real question is, “How do we allocate the rights, agency, and economic value generated by $K_{ai}$ in a post-human capital economy?” The Cognitive Enclosure must be met with a new Cognitive Constitution.

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