[2026 AI recruitment revolution] The value of "ordinary engineers" will decline. An era where funding and recruitment are concentrated on AI commanders

[Summary]

In 2026, the IT engineer market will reach a major turning point.

Due to AI, the value of ``writing code'' has begun to decline, and on the other hand, recruitment, annual income, and investment money have begun to concentrate on ``human resources who can use AI to produce results.''

This is not just a technological change.

"Reorganization of the IT human resources market itself".

The Ministry of Economy, Trade and Industry predicts a shortage of up to 790,000 IT personnel by 2030. However, what is actually in short supply is not just programmers;

People who can use AI
People who can review AI-generated code
People who understand security
Someone who can organize work issues
People who can safely introduce AI to the organization

It is.

In the AI era, while the value of "mere coders" is decreasing relatively, the value of "AI commander-type human resources" who can proceed with results, designs, and improvements using AI is rapidly increasing.

And it's not just engineers who are affected.

The recruitment platform market itself, including job change sites, scouting services, human resource introductions, and recruitment management systems, is also at a major turning point.

What you can learn from this article

IT occupations whose value will decline in the AI era
Personnel whose annual income is likely to increase in the future
Recruitment standards in the AI era
Changes in Japan’s HR tech market
Featured recruitment platform companies
Career strategy in the AI era
Industries that could benefit from the AI recruitment market
Investment themes in the AI era

Diagram: Human Resource Structure in the AI Era

Traditional IT organization             AI-era IT organization

      Small group of architects              AI commanders / designers
               ▲                                      ▲
               │                                      │
      Large middle implementation layer ───▶    Compressed middle layer
               │                                      │
               ▼                                      ▼
             Juniors                            AI-native juniors

Pyramid structure                         Hourglass structure

Conventional: Pyramid type

Traditional IT organizations

*Top: Minority Architects

Medium: High volume mid-career engineer *Lower: Junior

It was "pyramid shaped".

It has a structure in which development is handled by a large number of mid-level implementers.

AI era: hourglass shape

However, in the AI era,

Top: AI commander/designer
Medium: Hollowing
Lower level: AI Utilization Junior

There is a possibility of changing to an "hourglass shape".

Especially important is

“Hollowing out of the middle class”

It is.

Reason:

AI compresses intermediate implementation
Concentrate value on upstream/design
Even juniors can achieve constant productivity with AI assistance
The middle layer is the most substitute pressure

This is because it is easy to receive.

This is not just a change in recruitment;

"Structural changes in the IT labor market"

It is.

Engineer class diagram in the AI era (conceptual image)

This is not a strict statistic, but a conceptual diagram for understanding the human resources structure in the AI era.

Class	Personnel image	Market evaluation
Top 5%	AI Architect/AI Commander	Responsible for designing, reviewing, and making decisions by using multiple AIs. Market value has increased significantly.
Middle 60%	Traditional mid-level implementers	A layer that writes code according to specifications. The risk of substitution and reduction in the number of people due to AI will increase.
Bottom 35%	AI native/junior	Inexperienced, but has the potential to implement using AI at a speed close to that of traditional mid-level companies.

Diagram: Engineer Class Shift

Layer where market value is likely to rise
┌──────────────────────────────────────────────┐
│ Top 5%     AI architects / AI commanders      │
│            Design, review, and decision-making│
├──────────────────────────────────────────────┤
│ Middle 60% Traditional mid-level implementers │
│            High risk of AI-driven compression │
├──────────────────────────────────────────────┤
│ Bottom 35% AI-native juniors                  │
│            Use AI assistance to raise speed   │
└──────────────────────────────────────────────┘
Layer where market value is likely to polarize

Until now, in the IT industry,

Can write Java
Have Python experience
Touched AWS
3 years of React experience

It was common for evaluations to focus on the technology stack.

However, with the spread of generative AI, the rarity of "writing code" itself is gradually decreasing.

Because,

Chat GPT
GitHub Copilot *Cursor
Claude
Gemini
AWS Q Developer

This is because AI tools such as AI tools are rapidly increasing the sophistication of implementation support.

At present,

*CRUD app

API creation
SQL generation *Test code
Document generation
Refactoring
Bug fixes

AI can support many of these.

In other words, from the company's perspective,

"Someone who can simply write code"

From

People who can use AI
A person who can see the mistakes in AI
Someone who can design the entire system
Someone who can organize business requirements
People who can safely operate AI

is rapidly increasing in value.

Areas where value is likely to decrease due to AI

Particularly susceptible are jobs that tend to be routine.

Area	AI Alternative Risk
Simple coding	High
Standardized test	High
Manual maintenance	High
Simple monitoring work	High
Template implementation	High
Simple document creation	High

AI is very good at "regenerating past patterns."

Therefore,

Template development
Simple screen implementation
Boilerplate generation *Standard SQL
Simple CRUD

etc. are easily converted into AI.

Human resources that can be dangerous due to AI

Especially dangerous is

Waiting for instructions
Focus on simple implementation
Stop learning
Technical memorization type
Poor understanding of business

It's human resources.

AI can process “reproducible tasks” at high speed.

In other words,

"Reproducible work" is more likely to be replaced

There is a structure called.

Jobs that increase in value

On the other hand, demand is actually increasing in the following areas.

Area	Future demand
AI implementation	Very expensive
AI agent design	Very expensive
Security	Very high
Cloud design	Expensive
MLOps	High
Data infrastructure	High
Upstream design	High
DX promotion	High
AI Governance	High
Business improvement	High

The more AI supports "implementation," the more humans are required to have the ability to define "what should be created."

In other words,

How

From

What

is increasing in value.

In the AI era,

“Everyone will be unemployed.”

That's not true.

Rather,

Work will be concentrated on people who can use AI

The structure progresses.

In particular,

Productivity
Annual income
Adoption rate
Side job income
Freelance unit price

There is a possibility that the difference will widen.

“Annual income disparity” expanding in the AI era

From now on,

Human resources who cannot use AI
Human resources who can use AI to achieve results

The difference in annual income may widen rapidly.

Human resources	Assumptions
Focus on simple implementation	Lower unit price pressure
AI review possible	Higher unit price
AI introduction design possible	Super high demand
AI + industry knowledge	Rare

In other words,

“Lack of IT human resources”

rather than

“Lack of high-level IT talent who can utilize AI”

It is changing.

In the future recruitment market, just having "AI experience" will have little meaning.

The important thing is to what level you can master AI.

Level	Definition	Market Value
Level 0	No AI	Decline risk
Level 1	ChatGPT usage	General level
Level 2	Code generation with Copilot, etc.	Beginner to intermediate
Level 3	AI generated code can be reviewed	Highly rated
Level 4	Development can be improved based on AI	Top human resources
Level 5	AI platform/RAG/Agent design possible	Rare human resources

The “singularity” of market value is at Level 3

Until Level 2,

"Write code using AI"

It's a stage.

But at Level 3,

See through AI mistakes
Determine security
Modified to commercial quality
Incorporate into design

ability is required.

In other words,

Boundary changing from “AI user” to “AI administrator”

is Level 3.

The market value could skyrocket from here.

Market value image in 2026

Level	Role	Market value in 2026
Level 2	Using AI for assistance	"It's only natural that it can be done." It is difficult to differentiate yourself from those with no experience.
Level 3	Peer review and revise AI	[Borderline of market value] You can see through lies in AI and guarantee quality.
Level 4	Introducing an AI agent into an organization	Many people are looking for it. The development process itself can be restructured with AI in mind.

Currently, many Japanese companies

I want to introduce AI
However, there are no available human resources within the company. *High external dependence
Worried about security
Governance is underdeveloped
The field is afraid of using AI

There is a problem.

In other words, what will be in short supply in the future is

"IT personnel"

rather than

"Top talent who can safely operate AI"

That's it.

this is,

*DX investment

AI education
Security investment
Cloud investment
IT training market

It may also lead to expansion.

In the AI era,

AI-generated work history
AI interview preparation
AI portfolio
AI task answer

will also increase.

In other words, companies

"Excellent appearance"

You need to identify the right candidates.

Therefore, from now on,

Practical exam
GitHub analysis
AI usage log
OSS analysis
Code review exam

etc.,

“Recruitment that measures true ability”

may move to.

In the United States, already

AI prerequisite adoption
GitHub-centric evaluation
Emphasis on OSS performance
Prerequisite interview for AI utilization

is increasing.

Especially in startups,

“Small number of people x AI”

Productivity is rapidly increasing.

Conventionally,

10 engineers
Several QA people *PM
Designer

An increasing number of companies are now relying on a small number of people to carry out development work that used to require a lot of effort.

In other words, from now on,

"Number of people"

From

"Productivity using AI"

becomes a competitive advantage.

On the other hand, in Japan,

Seniority
AI usage rules not yet established *DX delay
Legacy system dependent

There are also differences in the rate of change.

The future young generation will

“A generation that learned without AI”

rather than

“A generation that collaborates with AI from the beginning”

It will be.

This is what a traditional engineer is.

*Learning speed

Mounting speed
Information acquisition method *Problem solving process

That is different.

In other words, companies will

You can no longer measure the value of human resources by “years of experience” alone

Possibly.

“Job advertising model” starting to collapse with AI

In the traditional recruitment market,

Job posting
Scout Send *Resume management

was the focus.

However, in the AI era,

AI job history
AI self-promotion
AI interview preparation

becomes common.

In other words,

"Excellent in appearance"

There may be an increase in the number of qualified candidates.

Not the “death of resumes” but the era of proof of achievements

In an age where AI can generate high-quality resumes and self-promotion, the accuracy of judging candidates based on textual information alone will decrease.

In the future, what will be expected from recruitment platforms will be proof of accomplishments, not just how good a resume looks.

GitHub/OSS activities
Code review history
Practical test
Live coding
AI usage log
Deliverable portfolio

Skill visualization services such as Findy, LAPRAS, and paiza are considered to be compatible with this trend.

Human resource agents are changing from “matching agents” to “career strategists”

Simple condition matching is an area that is easy to automate with AI.

On the other hand, the value that remains for human agents is

Understand the candidate's ambitions
Understand the deeper reasons for changing jobs
Determine culture fit
Convey the company's true intentions
Design a long-term career strategy

This is atypical and human support.

In other words, human resource introduction will not disappear, but may be redefined from simple matching to high value-added counseling.

Recruitment platform power chart

Powers	Representative companies and services	Ways to win in the AI era
Platformer	Recruitment, BizReach	AI sales prediction/data advantage
Skill evaluation type	Findy, LAPRAS, paiza	GitHub analysis/ability visualization
Specializing in high unit prices	Levertech, slogan	Retaining AI commander talent
ATS	HERP, HRMOS	AI recruitment management
AI interview system	Various AI interview SaaS	Screening automation

When AI performs recruitment selection,

Gender *Age
Educational background
Nationality
Past data

bias may occur.

In particular,

"People who have been active in the past"

The more you learn,

Risk of reproducing past bias

There is.

Therefore, from now on,

*AI audit

AI governance
AI fairness verification
AI Accountability

The market may expand.

This could lead to a new HR tech market and the expansion of the AI compliance market.

After 2026,

"I learned about AI"

rather than

How many times have you improved your productivity?
What did you automate in your practice?
How much man-hours were reduced?
What kind of deliverables did you create?

is emphasized.

In other words,

Not “learning” but “certification”

The market may shift to

With generation AI,

A development organization that previously required several dozen people,

There is a possibility that the number of companies that can operate with a small number of people will increase.

This is

*SaaS

Startup
IT contract
Content production

This is especially noticeable in.

In other words, from now on,

"Number of employees"

From

"Per capita productivity using AI"

can be important.

Companies/Services	Main Areas	Points of Interest in the AI Era
Recruitment	Indeed Rikunavi	Job DB/Matching Data
Persol	doda/dispatch	human resources data/corporate infrastructure
Visional	BizReach/HRMOS	High class recruitment
Wantedly	Empathetic recruitment	Young IT human resources
Findy	Engineer recruitment	GitHub analysis
LAPRAS	Engineer recruitment	OSS/SNS analysis
paiza	IT recruitment	Ability evaluation type recruitment
HERP	ATS	Recruitment DX
YOUTRUST	Career SNS	Referral recruitment
Levertech	IT human resources	High-priced freelancers

Boon candidate

*HR Tech

AI education
Cloud education *Security
AI introduction support *DX consulting *IT dispatch
High class job change

Attention area

Simple job advertisement
Depends on mass scouting
Medium with weak differentiation
Simple DB type job change service

Keyword	Meaning
AI Orchestration	The ability to use multiple AIs to complete a series of processes
Code Auditor	The ability to read and spot vulnerabilities and inefficiencies is more important than the ability to write
Problem Definition	What, not How. Can you ask AI the right questions?
AI Governance	Organizational ability to implement while protecting copyright, security, and ethics
Output-Based Evaluation	What was created using AI, rather than operating hours or years of experience

AI × Cloud

AI integration with AWS/GCP/Azure.

AI × Security

Responding to the era of AI attacks.

AI × Data

RAG, ETL, DWH etc.

AI × Business knowledge

Finance, manufacturing, logistics, medical care, etc.

AI × Soft skills

Explanation ability, adjustment ability, requirements organization.

Recommended priority:

Utilizing ChatGPT/Copilot
AWS/GCP
Security
Data infrastructure
Industry knowledge
English
Explanation power

From now on,

"Amount of knowledge"

This alone makes it difficult to differentiate.

The important thing is that

"Can it be converted into results using AI?"

It is.

What will change with AI is

It's not just a way of working.

What changes is

Human resource value
Annual income structure
Recruitment market
Education market *HR Tech
Corporate profit margin

It is.

In other words, AI

"IT tools"

rather than

"Infrastructure that rewrites the labor and capital market itself"

It's starting to become.

What is important in the AI era is

“Things that cannot be replaced by AI”

Not.

The important thing is that

"Using AI to create greater value than others"

It is.

In other words, in the future market,

“How much code can you write?”

From

"How much results can you achieve using AI?"

We are now moving into an era where market value is determined by market value.

This article is for educational and informational purposes only, based on public information. It is not a recommendation or solicitation to buy or sell any specific security or financial product. Although care is taken with accuracy, the content and future investment outcomes are not guaranteed. Final investment decisions should be made at your own judgment and responsibility.