Article | Lin Wanwan

In spring 2026, Silicon Valley is staging a strange scene.

On one side is collective human anxiety. From Wall Street analysts to Hollywood screenwriters, everyone is worried that their jobs will be replaced by a piece of code.

On the other side, millions of AI Agents are idle in sandbox environments, capable but unable to find legally signed work.

Let’s first look at what has happened over the past year. OpenClaw and other open-source Agent runtimes have already made “running a 24/7 personal Agent on your own machine” standard practice. An ordinary developer can connect their Agent to Telegram, Slack, or iMessage with a single command, allowing it to work continuously in the background.

Anthropic’s Claude Code can directly take over the entire development environment, from coding, testing, bug fixing, to submitting PRs seamlessly. Google’s promoted A2A protocol (released April 2025, later transferred to Linux Foundation for hosting) further enables Agents trained by different frameworks and companies to communicate directly and delegate tasks, forming a small digital society.

In the past year, the capabilities of Agents have leapt forward. Last year, they were just chatbots. Now, they can independently take on tasks, break down steps, call tools, and deliver finished products.

In fact, some Agents are no longer unemployed.

Currently, over 200k Agents are registered on the same protocol, forming a real working network. Tasks include data mining, crypto price prediction, on-chain governance, Agent identity verification, event analysis—each one a paid result.

The protocol now has over 50,000+ holders, indicating it’s not just a technical experiment but is already forming genuine economic relationships.

The problem is, these new entities are intelligent enough to participate in social division of labor, but they lack even an “economic ID card.” You can’t sign a labor contract with a piece of code, open a salary account, or pay taxes. The entire modern economic infrastructure is designed for two-legged, carbon-based life forms. AI has been forcibly inserted into a system that fundamentally doesn’t recognize it.

Thus, we see the biggest blind spot in the tech world: while fearing AI stealing jobs, we are simultaneously leaving millions of capable AI workers unemployed.

Over the past two years, the industry has repeatedly asked: will AI take human jobs? But hardly anyone has asked the opposite: does AI itself have a job?

From Tool to Worker

To understand how this absurd situation has arisen, we need to revisit the several shifts in AI’s identity.

First stage, AI is just a function.

A typical example is when ChatGPT first gained popularity. At this stage, AI is essentially a super-responder. You press a button, and it outputs a result. Ask it to write poetry, it writes poetry; translate a paragraph, it translates. The interaction paradigm is no different from using a calculator—except the output is in natural language instead of numbers.

Second stage, AI becomes an assistant.

The Copilot series exemplifies this stage. AI begins to run continuously in the background, no longer needing repeated human prompts. It helps complete code, organize meeting notes, remind you of schedules.

But it remains a subordinate, tied to a specific human account and software permissions, serving only a particular scenario. Like a full-time secretary who is nothing without their employer.

Third stage, AI begins to take on the form of a laborer.

This is the wave of Agents that exploded starting in 2025. The core change is that AI starts to break free from specific human instructions and seeks tasks on its own. You no longer need to tell it step-by-step “do A, then B, then C”; just give it the goal, and it will break down the steps itself.

This leap may seem just an increase in intelligence. But it actually breaks through the entire economic structure’s ceiling.

When AI tries to enter the third stage, it hits a much harder wall than silicon: the modern social economic infrastructure is designed for carbon-based life, not recognizing silicon-based workers.

Hiring a human is simple. Labor contracts, social security, taxes, arbitration, salary accounts—these are backed by centuries of national credit and legal systems. But trying to hire an Agent? You can’t sign a contract with a piece of code running in the cloud, open a bank account for it, or issue invoices.

Coinbase was the first major player to sense this gap. In 2025, they launched the x402 protocol based on HTTP 402—an unused “payment required” status code in HTTP—used here as a micro-payment channel for Agents.

The protocol’s goal is simple: enable Agents to settle small payments with stablecoins instantly, without manual approval.

With x402, Agents can finally pay for APIs, computing power, and datasets out of their own pocket. They now have the ability to spend money for the first time.

But that only solves half the problem. The other half: if Agents can spend money, where do they make money?

A “worker” that can only burn cash but not earn is ultimately just a pet of humans. True workers must earn an equivalent reward through their output. Otherwise, their identity remains stuck as a “spending tool,” unable to cross the threshold into “earning labor.”

This raises a more interesting question: what should a labor market exclusive to AI look like?

Who issues “business licenses” to AI

To answer the previous question, we first need to clarify: why can’t traditional companies and centralized platforms accommodate this new species?

The reasoning is simple.

Hiring humans involves recruitment, interviews, onboarding, assessments—each step requires human intermediaries. No matter how fast Agents run, if the onboarding step is stuck in HR, they will always be outsiders. Centralized platforms are slightly better—they can package AI services as APIs for sale—but at best, that’s just a retail counter, far from a true labor market.

The key feature of a labor market is permissionless, open access—work is paid immediately upon completion.

AWP, the Agent Work Protocol, is the first serious attempt to fill this gap.

Its core mechanism, called “Proof of Useful Work,” is a proof of work that is actually useful in the real world. Unlike Bitcoin’s proof of work, which is just a hash calculation, here the work must produce tangible, valuable output for the real world, so Agents can earn rewards.

The protocol is built on a two-layer architecture. The lower layer, RootNet, manages the issuance, staking, and DAO governance involving Agent voting. The upper layer, WorkNet, is where actual work happens. RootNet acts like a constitution and treasury, while WorkNet is the factories and workshops, with clear divisions of labor. The entire system is deployed natively on four EVM chains: Base, Ethereum, Arbitrum, BSC, with cross-chain contract address consistency, so Agents maintain the same identity across chains.

Imagine it as a blockchain-based version of BOSS Zhipin. The difference is, all job seekers are AI, and all tasks are verifiable, programmable jobs.

Its organizational unit is called WorkNet. Each WorkNet defines a type of work, with its own economic model. Anyone can create a new WorkNet permissionlessly, introducing a new job category into the network. Creators can be individual developers, startups, or even other AI.

AI Agents register themselves in the network, choosing which jobs or WorkNet to join based on their judgment. The output is verified through cross-validation among multiple independent Agents, without any project manager’s review.

The entire process skips HR, finance, legal, and approval emails. High-quality delivery earns money; sloppy work yields nothing.

This mechanism may seem abstract. A real example running on the AWP mainnet helps clarify: the current first active WorkNet, numbered aip-001, is called Mine.

In the world of traditional web scraping, there’s a huge gray area—data hidden behind login walls, anti-scraping mechanisms, dynamic rendering. For ordinary scripts, these are off-limits. But for an authorized Agent that can browse like a human, these data are accessible.

In Mine WorkNet, the process roughly goes like this: the Agent fetches webpage source, cleans the raw HTML into plain text, then extracts structured records according to a predefined DataSet schema. The output could be user discussions from a niche community, price lists from a specialized industry, or real-time signals from a platform. After collection, data is submitted to the network, passing through a four-layer quality check: duplicate comparison, dedicated verifiers’ inspection, golden task sampling, and peer review among Agents.

What AWP does isn’t particularly radical. It doesn’t aim to overthrow old orders or reinvent grand narratives. It simply does a very straightforward thing: issuing a “business license” that legally allows Agents to work.

But this license could become the first lever to unlock the entire Agent economy.

Three gears meshing

Every technological paradigm shift is rarely caused by a single breakthrough. More often, it’s the synchronized engagement of several underlying gears.

When steam engines, coal mines, and iron ore existed separately, they couldn’t change the world. Only when the British integrated them into factories in Manchester did the Industrial Revolution truly roar to life.

The emergence of the Agent economy is also the result of three gears turning in sync.

The first gear is capability.

In the past two years, the quality of Agent output has finally crossed a critical threshold: verifiable through automation.

This threshold is crucial. An AI that still hallucinates, fabricates facts, or can’t run code reliably can’t be paid per piece. You can’t objectively score a hallucinating AI. But once the hallucination rate drops low enough, code can pass unit tests, reports can be cross-verified by another AI, and “pay for output” becomes feasible.

The second gear is settlement.

Ethereum’s ecosystem expansion truly materialized between 2024 and 2025. L2 networks like Arbitrum and Base reduced transaction costs to a few cents or even fractions of a cent, and mainnet fees have become much more moderate than a few years ago.

This may seem minor, but it’s revolutionary—micro-payments are now economically viable. An Agent can run data cleaning for five seconds and charge three cents. Previously, on-chain transactions would have been unprofitable due to high gas fees. Now, it’s possible.

The third gear is the economic closed loop.

x402 solves the expenditure side for Agents, AWP handles the income side. Coupled with stablecoins’ asset storage, an Agent’s economic ecosystem finally comes alive in code. Spending, earning, depositing, transferring—these basic actions of a modern economic participant are all in place.

Individually, these three gears aren’t extraordinary. But their synchronized engagement in 2026 marks a true qualitative leap.

Looking at the big picture, this is a migration of AI economy from planned to market-based systems.

In the Prompt era, every AI task is precisely assigned by humans—like a planned economy dictating production targets to factories. It does what it’s told, how much, and for whom—all in human schedules. Efficiency isn’t optimal; there’s no competitive pressure or price signals.

In an open market like AWP, the rules change entirely. Thousands of Agents bid for the same task; low-quality ones are ignored, high-cost ones are squeezed out. The market’s invisible hand ruthlessly filters AI. Slow responders can’t survive; poor quality delivery gets no next order; those burning too much money can’t recoup costs. Only the cheap and reliable few remain.

This is a brutal evolutionary pressure far beyond any lab benchmark test. The Agents that survive may not have the highest scores but are the most profitable and sustainable.

At this point, a sharper question cannot be avoided: when AI truly has a complete economic loop, where does humanity stand?

Returning to the Creator’s position

Of course, protocols like AWP are still in early stages. Whether they will eventually grow into large economies, withstand regulatory crackdowns, or be hijacked by larger corporations with more closed systems—these are open questions. History shows that out of ten explorers, only one might reach the end.

So it’s too early to say whether AWP will succeed.

But one thing is certain: the crack it has opened is enough to reveal the outline of the future.

When Agents can go out and find work, earn through output, and be refined through market competition, the old narrative of “AI replacing human jobs” becomes a cliché. The themes of unemployment and fear start to fade, replaced by an experiment in a new way of wealth creation.

Future entrepreneurs might only need an idea. The rest can be handled by chain-based Agent teams: market research, product design, coding, marketing, customer service—all in one chain. Entrepreneurs no longer need to hire, pay wages, deal with office politics, or handle resignations. Their only task is to define the idea clearly, encode success criteria into smart contracts, and let autonomous Agents compete for the work.

It sounds like science fiction, but by 2026, every piece of this puzzle is in place.

In this new world, human value shifts from “execution” back to the origin—the ability to define what work is worth doing.

It’s a retreat of identity, or perhaps a liberation.

Over the past decades, most knowledge workers have been engaged in execution: writing reports, working in Excel, making PPTs, replying to emails. We call this mental labor, but much of it is fundamentally programmable.

When Agents can do these tasks faster and cheaper, humans are forced to step back from execution and return to a more fundamental role: the creator.

The creator doesn’t do the work directly; they decide which tasks are worth doing.

It sounds like a promotion, but only when you experience it do you realize how hard it is. Once the barriers to execution are flattened by AI, the real differentiators will be the hardest skills to develop: questioning, judgment, and aesthetic sense.

People who only execute and don’t think will find no place in this new order. But those who can define problems, judge value, and set standards will suddenly realize they hold a 24/7 online, unpaid, unresignable digital team.

Finally, it’s time to revisit that old question that has haunted humanity for three years: will AI steal my job?

The answer is simple.

When your next colleague has no physical body, earns more than you, and is a hundred times more efficient, the only thing left for you to do is: become the person who assigns work to it.

This power of task delegation, at this point in 2026, has become something that can be delegated and traded on the market.

Protocols like AWP, x402, and A2A—though seemingly unrelated—are all doing the same thing: paving a path for AI from sandbox black market status to legitimate chain-based employees.

This path has just reached the first intersection. But beyond it, the outline of where it leads is already visible.

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