AI has a strong encoding capability that creates derivatives from an object without hardcore friction or learning curves, which indicates inefficiency premium may no longer exist in the future, regardless software, SaaS or white-collar jobs.

Wage Premium

In labor market, wages are composed of skills and frictions. Here are lists that the productive payrolls and frictions that enterprises have to pay.

Human Labor Moat

Why labor supply is limited

Most skills require years of accumulation of uncompressed, enumerative experience duo to

• Complexity: aviation pilots, firefighters, surgeons, who operate complex equipments.
• long tail & assurance: soldiers, bookkeepers, infrastructure engineers, who are responsible for rare cases
• Long feedback-cycle: financial, biologics, law, policy, while the results may delay after years.

With time constraints, the labor supply is limited, and the society has been working on labor shortage: education, immigration, prolonged retirement.

Diminishing Returns on skill acquisition & upgradation

The diagram shows the most common case between the skill (the bar) and wage(the line). The skill follows a concave learning curve to capability, while the wage follows a S-shaped line depending on the demand relationship.

• Training: From the begin of career, beginners will take 1-3 years to get workspace training on incorporation into their workspace culture and internal processes, and the wages show underpaid as the internal tools & processes are not transferable.

• Reprice: After the skill accumulated, the wage might soar alias with the job market demand, as the new companies must pay premium to get an experienced labor instantly.

• Plateau: When the skill stabilized, individuals may not continue on investing on their skills for higher pays, as the career opportunities may have stagnated: lack of high-end positions, politicized roles, leaderships, work & family balance, primary accumulation of capital, ageism.

Inefficiency Premium in Organizational Frictions

In corporates organizations, employees are not always working on core productivity, instead they must follow on the rigmarole management framework (like L2C, IPD, Agility) as cogs inside a machine.

Operational Frictions and Modern Process Frameworks

The modern frameworks decompose the complex, abstract problems into standardized, observable, executable process stages, making it possible to help domain isolated employees working togeher, but with more frictions and delays.

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Workflow
 ├─ Financial: Record to Report
 ├─ HR: Recruit to Retire
 ├─ Sourcing: Source to Pay
 ├─ Sales&Service: L2C/LTR
 ├─ R&D: IPD/DevOps/Agile
 └─ Machine–Machine (data pipelines, BI)

For those problems, there has been mitigation on productivity, like digital office tools, ERP SaaS, data lakes or other IT automations in corperates. Most of them are “the Big Excels”.

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Mitigation
 ├─ Human–Machine (ERP, CRM, tickets)
 ├─ Human–Human (meetings, chat, Email)
 └─ Machine–Machine (data pipelines, BI)

These services may leverage on AI for less operational frictions, as plenty of SaaS companies have declared their agentic solutions.

Gate-keeping as Friction

Despite productivity increases on AI-acceleration, decision roles involved are not always applicable for maximizing speed for compliance reasons. As decision rights and knowledge are separated, when forcely applying AI-based processing on the gate-keeping even seemingly works, there could be

• governance backpressure on volumes: uncertainty, traceability, accountability and liability
• Transparency & trust conflict: entrepreneurs prefer the single source of truth powered by AI, but employees may believe they are supervised, as a result, there could be
  • duo record systems - a hiden Excel for true works, and the delay or AI-reshaped data for KPI
  • uncontrolled computing at the edge - junior staff can produce executive-level analysis
• Career incentives: for who preparing from retirement, the only consideration is obedience, while new graduates may focus on innovations.

These kind of intentional frictions may be at a prisoner’s dilemma

• Resistant to AI => Redundancy: bureaucrat, left behind other companies & countries
• Accept AI => Taylorism: KPI dashboards, permanent auditability, accountability, screwed, micro-monitoring and peer pressures.

Capture Apparatus - the Deflationary Encoder

The past innovations

In the book A Thousand Plateaus: Capitalism and Schizophrenia authored by Gilles Deleuze, he describes and predicts a mechanism Capture Apparatus (appareil de capture), which encodes or integrates any flow like land, capital, goods, information and labor into a centralized, stratified, efficiency order.

Here’s how giant platform capitalism minimize frictions and increase certainties while accumulating network effect.

• land distance friction: 2x time-saving or cheaper transportation
  • Amazon & Walmarket: elimate local grocery store with lowest price.
  • Tesla: driving is cheaper and painless without consideration.
  • vessel and canal: China’s products are cheaper even cargo are delivered across the ocean.
• Capital & Trust friction: 50x faster on leverage
  • finacial capital: lend, borrow, transfer money with trustworthiness.
  • trading market: it’s feasible to trading future time with stock, features and derivatives
• information spread friction: 10x faster on AI & Algorithms
  • search engine: redistribute websides with keywords.
  • TicTok: algorithm replace traditional newspapers and reporters.

AI Capture Apparatus

Similar to the Walmarket minimized the supply chain frictions from factories to customers, AI reduces knowledge distance frictions with the two brute force computability

• Connectivity: discover, synthesis, patten match, dissolve the gap among digital objects.
• Cognitive compiler: elaborate with deductive reasoning with scoped inputs.

Based on the definition from Deleuze, the AI models is literally a digital Capture Apparatus representation, which captures cognitive labor and reterritorializes into giant datacenters with brute force low-marginal-cost inference.

Keep in mind that the AI is not defined as a replacement on labor forces, it is an accelerator for frontier experts.

Polarization on Jobs

Verification Bottleneck on biological limitation (Unresolved)

With AI involved in the job, code or document generation may be cheaper and faster, human work may shift into the search and verification, which may be similar to EDA replacement on handcraft circuit in 80s.

Unlike EDA in a scoped mathematical system with format verification, AI can’t guarantee same outputs in nature language. The question arises when it comes to the lag between high throughput AI productivity with human biological limitations.

Which is the right sweat spot for human when trusting or verifying AI artifacts durability?

• the duck test - thousands of blackbox test cases
• disposable artifacts - when verification cost > replacement cost
• ritualization - the worst case like aviation crash

Jobs middle collapse (Facts)

In the last few months, I have almost attempted to transfrom my job into agentic flows and skills in the coding development. Here are something interesting.

• Information Security
  • Static taint analysis: Digging hundreds of XSS/SSRF risks from source code with qwen3.5-27b
  • But comprehensive defensive armor is always required, like supply chain protection over axios
• IT outsourcing
  • For entry & middle level engineers, especially who only understand one language but not capable on others skills like linux, they may be constrained inside their own circle of competence despite working with advanced models.

After all, I believe all patten match or audit jobs may be distilled first.

Long term jobs (Future)

Jobs may shifts towards high compression ratio or abstraction tasks

• steeper learning curve engineer: exception handling, unpredictable, slow feedbacks the complex system.
• MoE knowledge base: learn philosophy, being sophisticated, generalist for problem framing capabilities

One time digital transformation on connectivity on business

• AI empowers domain experts to bypass engineering bottlenecks.
  • software maintenance: software engineers may maintain the AI-generated code by experts.
  • Liability engineering: explainability or liability insurance
• AI infrastructure
  • build, test, maintain, gateway, sandbox, firewall, multi-level agent management.
  • Less expensive models: Replacing repeated works with cheaper models
  • continuous maintenance on training data: security CVE, tax, legal precedents
  • data aggregation and bi-verification among isolated IT platforms, which will make the corperate more transparent.

Despite AI leveraging, friction and uncertainty is not merely unavoidable, but structurally necessary.

• Emotional & ritual Labor: it has happened on Japan even without AI enrollment.

Questions (TBD)

• is a failure experience compulsory?

  • learning swimming without entering water
  • driving a car in an emulator

• prior knowledge limitation

  • can you outsource your thinking?
  • can you verify outdated outputs?
  • can you prove your innocence of reasonable reliance on AI generated artifacts like laws & taxes?