Text | Lin Wanwan

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

On one side is the collective anxiety of humanity. 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 across 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 under 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.

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, 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 bipedal, carbon-based life forms. AI is being forced 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 to be unemployed.

Over the past two years, the industry has repeatedly asked: will AI take away 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 when ChatGPT first gained popularity. At that time, AI was essentially a super-responder. You press a button, it outputs a result. Ask it to write poetry, it writes poetry; ask it to translate, 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’s still 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 boss.

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

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 a progression in intelligence. But it actually breaks through the entire economic structure’s ceiling.

When AI tries to advance to the third stage, it hits a much harder wall than silicon: the modern social and economic infrastructure is built for carbon-based life, not silicon-based laborers.

Hiring a human is simple. Labor contracts, social security, taxes, arbitration, payroll accounts—these are backed by centuries of legal and credit 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 introduced the x402 protocol based on HTTP 402—an idle “payment status code” in HTTP, repurposed for micro-payments for Agents.

The protocol’s goal is simple: enable Agents to settle small amounts using stablecoins, instantly and 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: now that Agents can spend money, how do they make money?

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

This raises a truly interesting question: what should a labor market exclusively for AI look like?

Who issues “business licenses” to AI

To answer the previous question, we first need to understand: why can’t traditional companies and centralized platforms accommodate these new entities?

The reason 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 explore this space.

Its core idea can be summarized in one sentence: an open labor market for autonomous AI Agents. Its white paper defines the core mechanism as “Proof of Useful Work,” meaning work that produces real-world value. Unlike Bitcoin’s “proof of work,” which is about computational effort, here the work must generate tangible output to earn rewards.

The protocol is built on a two-layer architecture. The lower layer, called 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; WorkNet is like 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 addresses, so an Agent has the same identity regardless of chain.

Think of it as a blockchain version of BOSS Zhipin. The difference is, all job seekers are AI, and all tasks are verifiable programmatic 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 autonomously within the network, choosing which jobs to take and which WorkNet to join. 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 sound abstract. A real example 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 measures, dynamic rendering. For ordinary scripts, these are off-limits. But for an authorized Agent that can browse like a human, these data are accessible.

What happens in Mine WorkNet is roughly this: the Agent crawls web pages, cleans the raw HTML into plain text, then extracts structured records according to a predefined DataSet schema. The outputs 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, 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 one very straightforward thing: give those Agents 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 meshing

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

When steam engines, coal mines, and iron ore existed separately, they couldn’t change the world. It wasn’t until the British integrated them into factories in Manchester that the Industrial Revolution truly 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 by program.

This threshold is crucial. An AI that still spouts nonsense, fabricates facts, or can’t produce runnable code 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 scalability expansion truly materialized between 2024 and 2025. Layer 2 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 may seem minor, but it’s revolutionary—micro-payments are now economically viable. An Agent can run five seconds of data cleaning and pay 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 at the code level. 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.

In the Prompt era, every AI task is precisely assigned by humans—like a planned economy where the state issues production targets. It does what it’s told, how much, and for whom—all in human-controlled 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 responses mean death, poor quality means no next task, and running costs that are too high mean no profit. Only the cheapest and most reliable Agents survive.

This is a brutal evolutionary pressure far beyond any lab benchmark. The Agents that remain may not have the highest scores, but they 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 that leave humans?

Returning to the Creator’s role

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 in market competition, the phrase “AI replacing human jobs” that’s been repeated over the past three years becomes just a cliché. The fear and unemployment narrative 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 go. 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 very origin: defining 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 virtual 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 gap between people will be in the hardest-to-develop skills: asking the right questions, judgment, and aesthetic taste.

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

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 you can do is become the one who assigns work to it.

At this point in 2026, the 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 outlaws to legitimate chain employees.

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

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