Core Argument: OpenClaw’s flaws are public knowledge, but it’s being pushed faster than it’s being fixed. Not because it’s good, but because it represents a path that must start running—from “can talk” to “can do.” The costs are real: unemployment, privacy erosion, organizational restructuring. But only by seeing this path clearly can you make a real choice.

1. Pushed Despite Flaws: This Is a Route, Not an Isolated Case

OpenClaw’s weaknesses are obvious: bloated, long chains of execution, expensive to run. A single complex task can burn hundreds of thousands of tokens, with costs ticking by the second. More ironically, the more it tries to “help you do things,” the more it needs your data—your documents, emails, meeting records, codebases. Privacy risk isn’t “possible”—it’s “inevitable.”

Yet it still receives strong support. The signal behind this is clear: AI must move from “can talk” to “can do,” and must hit the road even with flaws.

1.1 The Historical Inevitability from “Can Talk” to “Can Do”

Looking back at technology history, every productivity leap follows a similar pattern: steam engines didn’t need to be perfect to be adopted—they just needed to be stronger than horses; the internet didn’t need to be secure to spread—it just needed to be faster than postal mail; OpenClaw doesn’t need to be elegant—it just needs to prove AI can execute complex multi-step tasks.

ChatGPT represented AI’s “talking era”—it can answer questions, generate content, but stops at conversation. Claude Computer Use, OpenAI Operator, and OpenClaw represent the “action era”—they can operate interfaces, call tools, and complete closed-loop tasks.

This isn’t just a technical upgrade—it’s a business model upgrade. From SaaS (Software as a Service) to RaaS (Results as a Service). Whoever runs it first defines the business rules for the next decade.

1.2 The Chinese Logic of “Pilot First, Scale Later”

This aligns closely with China’s rollout path: private enterprises lead pilots, and once effective solutions emerge, they’re replicated, innovated upon, and advanced together across regions. The essence in four characters: run first, talk later.

The Shenzhen model, Hangzhou’s digital governance, Hefei’s industrial investment—all moved from local pilots to national replication. OpenClaw sits at the “pilot” position in this chain. Its success or failure determines the direction of subsequent investment and policy support.

Flaws, privacy, cost—these issues get temporarily deprioritized. Because in most decisions, risks are future and dispersed, while benefits are present and concentrated. As long as it can replace labor and make organizations run faster, the push speed will exceed the fix speed.

1.3 The Return of Jevons Paradox

The Jevons Paradox in economics tells us: improving resource use efficiency actually increases total consumption. AI is the same—it makes individual tasks cheaper, so organizations initiate more tasks, ultimately leading to more labor being replaced, not just efficiency gains.

This isn’t the fate of OpenClaw alone—it’s a chosen technological route. It doesn’t need to be perfect; it must hit the road.

2. The Cost: Unemployment Is Visible Pain

The cost is unemployment. This will happen to us, concretely. This is the growing pain of era development—only this time it lands squarely on our shoulders.

2.1 Rough Work Gets Liquidated First

What gets replaced first are jobs that can be standardized and decomposed: organizing, summarizing, filling forms, routine communication, repetitive judgment.

Specific liquidation scenarios are already happening:

• Customer Service: Level 1-2 customer service is being replaced en masse by AI chatbots. Complex issues escalate to humans; simple issues auto-close. A 50-person team can compress to 5—3 senior agents handling edge cases, 2 engineers maintaining the system.

• Content: SEO articles, product descriptions, social media copy—work that once required junior creators is now AI-generated at scale. One operator can replace five from before, but only one of those five will keep their job.

• Administrative: Expense processing, document archiving, meeting minute organization—these tasks are being automated by AI agents. Admin roles won’t disappear, but they’ll shrink from 10 people to 2, and the requirement shifts from “executor” to “process designer.”

People whose work is rough—neither specialized nor broad—lose pricing power. Rough means high quality variance, non-reusable methods, and no ownership of outcomes. When AI arrives, such roles have no reason to exist.

Specialized people have technical barriers; broad people have integration capabilities. Neither specialized nor broad means extremely high replaceability.

2.2 Bosses Become Generalists, Middle Layers Disappear

Meanwhile, many bosses or leaders will become generalists again. AI takes over much of the coordination work of middle layers, allowing managers to directly use tools to complete tasks that once required team collaboration.

A concrete scenario: Previously, a startup needed to hire a product manager for documentation, an operator for campaigns, a designer for assets, and a programmer for simple automation scripts. Now, one founder + an AI toolchain can cover 80% of this work. The remaining 20% goes to contractors or consultants.

Just as the internet’s boom distributed the pressure on all-rounders and created many new professions, the AI wave will reconcentrate many capabilities back into individual hands.

This means: middle-layer coordination roles will shrink, frontline execution roles will compress, and individual productivity for those who master the tools will be amplified.

Organizational hierarchy shifts from pyramid to diamond-shaped:

Traditional Org:     AI-Era Org:
     CEO                 CEO
    /   \               /     \
  VP    VP         Core Experts  Core Experts
  /\    /\             |           |
Mgr  Mgr         Tool Users   Tool Users
|     |
Staff Staff

Middle managers disappear not because coordination isn’t needed, but because AI takes over coordination. Frontline staff shrink not because workload disappears, but because AI + a few experts can complete it.

2.3 Witness the Miracle, or Get Left Behind by It

We will inevitably witness miracles in this development. But whether we can participate in this miracle depends on our choices.

Those who become unemployed get counted as “structural adjustment” numbers; those who participate become part of new forms of social activity. The difference isn’t whether you use AI—it’s where you position yourself.

History doesn’t repeat, but it rhymes. During the Industrial Revolution, artisans were replaced by machines; in the internet era, traditional retail was disrupted by e-commerce; in the AI era, knowledge workers will be reshuffled.

Each time, those who participated in the new form gained 10x returns; those left behind bore 100% of the pain. This isn’t alarmism—it’s the historical pattern of technological revolution.

3. Traditional Job Hunting Is Disappearing

Traditional hiring assumes: stable role templates, clear skill boundaries, fixed organizational structures. AI demolishes all three.

3.1 Roles Become Projects, Hiring Becomes Procurement

Roles are becoming “project-based”: instead of hiring someone to do A, you need someone to turn A into a replicable process—ideally one AI can run for you.

Job description changes are already happening:

Many roles won’t disappear from job boards—they’ll disappear inside organizations: bosses stop hiring and instead buy tools, buy systems, buy automated workflows, then cover the rest with fewer people.

The object of organizational procurement has changed: from “labor” to “execution capability.” Traditional forms of job hunting seem to be becoming relics of a bygone era.

3.2 The Dissolution of Skill Boundaries

Before, you could be a “copywriter” without worrying about design; a “designer” without worrying about code; a “programmer” without worrying about product.

In the AI era, these boundaries dissolve. A content creator needs to use AI for images, AI for writing assistance, AI for data analysis. This doesn’t mean you need to be a generalist—it means you need to string together a complete toolchain.

Skill combinations shift from “single-point specialization” to “toolchain integration”:

Traditional Skill Tree:    AI-Era Skill Chain:
Copywriting ──────→    Writing Intent → AI Tool Selection → Result Optimization
Design ───────────→    Aesthetic Judgment → AI Generation → Human Refinement
Code ─────────────→    Problem Decomposition → AI Coding → Testing & Debugging
Data ─────────────→    Business Understanding → AI Analysis → Decision Recommendations

3.3 New Professions Will Grow from the Ruins

But history tells us technological revolutions never simply destroy jobs. In the internet era, pressure on all-rounders was distributed, creating previously non-existent professions like operations managers, product managers, and growth hackers.

New professions in the AI era are emerging:

• AI System Designer: Not writing code, but designing AI toolchains and automation workflows
• Human-Machine Collaboration Specialist: Skilled at dividing human-machine boundaries and designing collaboration patterns
• AI Output Quality Controller: Can judge AI output quality and establish standards and processes
• Prompt Architect: Designs complex prompt systems and templates
• AI Trainer: Fine-tunes and optimizes AI models for specific domains

The AI era will have new professions, but they won’t wait for those whose “work is rough—neither specialized nor broad.” New professions require: ability to collaborate with systems, judgment at boundaries, and backup capability during anomalies.

4. Choice: Find Your Self-Worth

Faced with these changes, passive waiting is meaningless. You must find your self-worth to participate in new forms of social activity.

4.1 Don’t Compete on Output Speed—Output Is Inflating

The most ironic thing about the AI era: the harder you try to be a “high-output person,” the cheaper you become. Because output is inflating.

When AI can generate ten versions of copy, five designs, and three code implementations in one minute, pure quantity advantages mean nothing.

The Output Inflation Curve:

2020: 1 high-quality article = 1 day of work = high value
2024: 1 high-quality article = 2 hours of work = medium value
2026: 10 high-quality articles = 1 hour of work = low value (oversupply)

When everyone can mass-produce with AI, the unit value of output drops. This is why “diligence” in this era might be a trap—you’re using tactical diligence to cover strategic laziness.

4.2 Move Toward the New Scarcity

What will truly become expensive are capabilities AI cannot easily replace:

4.3 Become an Organizer, Not the Organized

Position yourself alongside these four things, and you’re no longer the replaced—you become the replacer.

Core Capabilities of Organizers:

1. Define Problems: AI excels at solving problems, but not at defining them. Those who can accurately define problems can direct AI to work.

2. Design Processes: Breaking vague goals into AI-executable steps—this is the organizer’s core work.

3. Quality Gatekeeping: AI output needs human judgment and correction. Those who can judge quality control output.

4. Integrate Resources: Combine multiple AI tools, multiple people, multiple processes into one system.

5. Bear Risk: AI cannot be responsible for failure. Those who can bear risk hold decision power.

This isn’t chasing trends—it’s occupying scarcity. Trends pass; scarcity is always valuable.

4.4 An Actionable Framework

30 Days:

• Audit your current work: what’s “rough and standardizable”?
• Pick 1-2 tasks and try automating them with AI tools
• Record time savings and effect changes

90 Days:

• Build your personal AI toolchain
• Delegate at least 30% of your work to AI execution
• Start outputting your methodology (write articles, give talks, mentor newcomers)

180 Days:

• Complete the role transition from “executor” to “designer”
• Be able to design an AI + human collaboration system
• Form reusable expertise in some domain

1 Year:

• Become the person who “can use AI to replace 5 people from before”
• Or become the person who “can direct 5 AI systems”
• Ensure you’re positioned at the upstream of the value chain

5. Privacy and Dependency: The Neglected Second Wave of Costs

Unemployment is the first wave of costs, but more hidden costs are brewing.

5.1 The Surrender of Data Sovereignty

Using systems like OpenClaw means you must hand over your data: your codebase, your documents, your communication records, your business processes.

Three Stages of Data Surrender:

1. Active Surrender: You voluntarily upload data for convenience
2. Passive Surrender: You must share data for collaboration
3. Cannot Withdraw: When you want to leave, data is already deeply embedded in the system

This isn’t alarmism. Think about the cloud services, SaaS tools, and platform accounts you can’t leave anymore—exit costs show how fragile data sovereignty is.

5.2 The Trap of System Dependency

When an organization becomes deeply dependent on an AI system, “capability hollowing” occurs:

• Skills once held by humans are now held by the system
• Judgments once made by humans are now suggested by the system
• Experience once accumulated by humans is now stored by the system

Once the system fails or locks you in, the organization loses capability. This isn’t hypothetical—it’s a risk actively materializing.

5.3 Privacy Isn’t “Possible”—It’s “Inevitable”

Privacy breaches aren’t a matter of “if,” but “when”:

• Internal personnel abusing access
• System vulnerabilities exploited by external attackers
• Data sharing in business partnerships
• Government data requests under regulation

Every path is open. All you can do is assess the risk-reward ratio and make an informed choice.

6. Organizational Response: Not Passive Acceptance, But Active Design

Faced with AI advancement, organizations aren’t limited to passive acceptance. Visionary organizations will proactively design their AI strategy.

6.1 Establish an AI Governance Framework

6.2 Invest in Internal Capability Building

• Training: Enable employees to master AI tools rather than be replaced by AI
• Process Redesign: Redesign workflows to maximize human-machine collaboration
• Knowledge Management: Ensure organizational knowledge doesn’t depend on external systems

6.3 Maintain Exit Capability

Most critically: maintain the ability to leave any single system.

• Data must be exportable
• Processes must be migratable
• Skills must be reusable

This isn’t pessimism—it’s mature technology strategy.

Closing: See It Clearly, Then Make a Choice

OpenClaw has many flaws—that’s fine. What you need to see isn’t whether it’s polished, but that the route it represents has already started: action-oriented AI will be pushed into reality, flaws will be fixed during rollout, and costs will be settled during rollout.

Unemployment will happen. Traditional job hunting will increasingly resemble an institutional relic from a bygone era. Privacy will be surrendered. Dependency will form.

But opportunities to participate in the miracle also exist. If you price yourself on “standardizable output,” you’ll be crushed by AI. If you price yourself on “specialization, breadth, accountability, trust, judgment, relationships,” AI will make you more valuable.

You don’t need to like it, but you must see it clearly. Then make a choice.

The power to choose is still in your hands—at least for now.