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: aircraft 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 worked 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 Firctions 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.
1 | Workflow |
For those problems, there has been mitigation on productivity, like digital office tools, SaaS, data lakes or other IT automations in corperates. Most of them are “the Big Excels”.
1 | Mitigation |
These services may leverage on AI for less operational frictions, as plenty of SaaS companies have declared their agentic solutions.
Bureaucratic Frictions
Inside an organizational workflow, the key friction is never just the meetings. On AI-accelerated productivity, the decision volume explode is the key bottleneck
governance backpressure: uncertainty, traceability, accountability and liability
Trust gap: the source of truth may be delayed, bypassed or reshape data among employees and AI
- duo record systems - a hiden Excel for true works, then reshape with AI and input the official page
- uncontrolled computing at the edge - junior staff can produce executive-level analysis
Career incentives: for who preparing from retirement, the only consideration is stability, while new graduates may focus on innovations.
These kind of intentional frictions may resistant to AI service, the more transparency, the more responsibility.
Imbalacned ROI on staffs
Before AI, the large part of our labor cost is for meetings, documentation, or compliance regardless of job position, but now the corporates pay the same while we take more acountability with expertizes.
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 platforms 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: synthesis, patten match, even interprets unstructured data into other artifacts with ontology abstraction.
- Decompression: elaborate with deductive reasoning with limited 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.
Moreover, AI is not just a bridge which pipes flows in and out, it becomes a producer itself. We call this as agentic, the RPEL flow in nature language.
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,
- prior knowledge limitation - multi-AI supervisied verification? can you outsource your thinking?
Collapsed entry-level Jobs (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 defense is still 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 jobs may be distilled first to resolve search inefficiency with less compensation.
Long term jobs (Future)
Personal may concentrate on
- steeper learning curve: exception handling, unpredictable, slow feedbacks the complex system, which both have high compression ratio
- MoE knowledge base: learn philosophy, being sophisticated
One time digital transformation
connectivity on business
- AI-powered data integration and bi-verification between isolated IT platforms, which will make the corperate more transparent.
- Agentic workflow on the AI synthesized data.
AI infrastructure: build. Test, maintain ,gateway, sandbox, firewall, multi-level agent management.
Questions (TBD)
- is a failure experience compulsory - learning swimming without entering water, driving a car