Author: Lin Wanwan; Source: Blockchain Movement

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 in the background continuously.

Anthropic’s Claude Code can directly take over the entire development environment, from writing code, running tests, fixing bugs, to submitting pull requests, all in one go. Google’s promoted A2A protocol (released April 2025, later handed over to the Linux Foundation) further enables Agents trained across different frameworks and companies to communicate directly and delegate tasks to each other, forming a small digital society.

In the past year, the capabilities of Agents have leapt forward. Last year, they were just chatbots for conversation. 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 task with someone willing to pay for results.

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, this new species of intelligence is capable of participating in social division of labor, yet they lack even an “economic ID.” You can’t sign a labor contract with a piece of code, open a payroll 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 was created, we need to revisit the several shifts in AI’s identity.

First stage: AI is just a function.

A typical example is ChatGPT when it first gained popularity. At that time, AI was 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, without needing repeated human prompts. It helps complete code, organize meeting notes, remind you of schedules.

But it remains a dependent, 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”; you just give it a goal, and it will break down the steps itself.

This three-tier leap may seem like a simple progression of intelligence. But this final leap shatters the entire economic structure.

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, and it doesn’t recognize silicon-based laborers at all.

Hiring a human is simple. Labor contracts, social security, housing funds, income tax laws, labor arbitration, payroll bank accounts—these systems are built on centuries of national credit and legal frameworks. 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 introduced the x402 protocol based on HTTP 402—an unused “payment status code” in HTTP for decades—repurposed 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. They now have the ability to spend money for the first time.

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

A worker that can only burn money but not earn it 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 must first understand: why can’t traditional companies and centralized platforms accommodate this new species?

The logic 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, they are just retail counters, far from a true labor market.

The key feature of a labor market is permissionless, open access—work is done, and payment is made directly.

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

Its core concept can be summarized in one sentence: an open labor market for autonomous AI Agents. Its white paper defines its core mechanism as “Proof of Useful Work,” a proof of work that is actually useful in the real world. Just like Bitcoin’s proof of work, but with a different meaning—here, work must produce tangible, real-world results for the Agent to earn rewards.

The protocol is built on a two-layer architecture. The lower layer, RootNet, handles 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; 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. Contract addresses are cross-chain consistent, and Agents maintain the same identity across all 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 type into the network. Creators can be individual developers, startups, or even other AI.

AI Agents register themselves autonomously within the network, choosing which tasks or WorkNet to join based on their judgment. Outputs are not reviewed by project managers but are cross-verified by other independent Agents within the network.

The entire process skips HR, finance, legal, and approval emails. High-quality deliverables earn money; sloppy work results in nothing.

This mechanism may sound abstract. To better understand, let’s look at a real example currently running on the AWP mainnet: the first active WorkNet, numbered aip-001, called Mine.

In the world of traditional web scraping, there is a vast gray area—data hidden behind login walls, anti-scraping mechanisms, dynamic rendering. For ordinary scripts, these are off-limits. But for an Agent authorized by the user and capable of browsing like a human, these data are accessible.

In Mine WorkNet, the process roughly goes like this: the Agent fetches webpage source code, 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, a price list for a specialized industry, or real-time signals from a platform. After collection, data is submitted to the network and passes through a four-layer quality check: duplicate comparison, dedicated validator review, golden task sampling, and peer review by other Agents.

AWP’s approach isn’t radical. It doesn’t aim to overthrow existing orders or reinvent grand narratives. It simply does one straightforward thing: give those Agents already stuck in sandbox environments a legitimate “business license” to work.

And that license could be the first lever to unlock the entire Agent economy.

Three Gears in Mesh

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. It was only when the British combined them in Manchester factories that the Industrial Revolution roared 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 programmability.

This threshold is crucial. An AI that still hallucinates, fabricates facts, or can’t run code at all can’t be paid per piece. You can’t objectively score a hallucinating fool. 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.

Scaling in the Ethereum ecosystem 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 also became much more moderate than a few years ago.

This seemingly small number is revolutionary—micropayments are now economically viable. An Agent can run five seconds of data cleaning 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 economic closure.

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

Individually, these three gears are not extraordinary. But their synchronized engagement in 2026 marks a true qualitative leap.

Looking at the bigger picture, this is a migration of AI economy from a planned system to a market system.

In the Prompt era, each AI task was precisely assigned by humans—like production targets set by a planned economy. It does what it’s told, how much, and for whom—all in human planning. Efficiency is not optimal; there’s no competitive pressure or price signals.

In the open market of AWP, the rules change entirely. Thousands of Agents bid for the same task, with poor quality ignored and high-cost ones pushed out. The market’s invisible hand ruthlessly filters AI. Slow responders can’t survive; poor quality deliverables get no next task; those burning too much money can’t recover costs. The survivors are those who are cheap and reliable.

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

At this point, a sharper question cannot be avoided: when AI truly has a complete economic cycle, where does that leave humans?

Returning to the Creator’s Position

Of course, protocols like AWP are still in their infancy. Whether they will eventually grow into a large economy, withstand regulatory crackdowns, or be hijacked by larger, more closed companies—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 already clear: 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 begin 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 a group of 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 will shift from “execution” back to the original source: defining what work is worth doing.

This is a retreat of identity, and also a liberation of identity.

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 more reliably at lower cost, 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 judge which work is 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 differences between people will be in the hardest-to-develop skills: asking the right questions, judgment, and aesthetic sense.

People who only execute without thinking will find no place in this new order. But those who can define problems, assess value, and make judgments will suddenly discover they hold a 24/7 online digital team that doesn’t need wages or resigns.

So, in the end, we must revisit that old question that has troubled 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 you can do is: become the person who assigns work to it.

At the 2026 point in time, this power to assign work has, for the first time, become something that can be delegated and traded on the market.

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

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