By Lin Wanwan

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

On one side is collective human anxiety: from Wall Street analysts to Hollywood screenwriters, everyone fears their jobs could be replaced by a line of code.

On the other side, millions of AI agents sit idle in sandboxes, possessing great capabilities but unable to find legally binding work.

First, let’s look at what has happened over the past year. Open-source Agent runtimes like OpenClaw have turned “running a 24/7 personal Agent on your own machine” into a standard practice—a regular developer can now connect their Agent to Telegram, Slack, and iMessage with a single command, letting it work continuously in the background.

Anthropic's Claude Code can seamlessly take over the entire development environment, from writing code and running tests to fixing bugs and submitting PRs. Google's A2A protocol (released in April 2025 and later transferred to the Linux Foundation for stewardship) goes even further, enabling agents trained on different frameworks and by different companies to communicate directly and delegate tasks to one another, forming the early雏形 of a small digital society.

Over the past year, the capabilities of the Agent have made a leap forward. Last year, it was merely a chatbox that could converse with you. Now, it can independently take on a task, break it down into steps, invoke tools, and deliver the finished result.

In fact, some agents are no longer unemployed.

Over 200,000 agents have registered on the same protocol, forming a functioning network where tasks such as data mining, cryptocurrency price prediction, on-chain governance, agent authentication, and event analysis are all paid tasks with real demand.

The protocol currently has over 50,000 holders, indicating that it is more than just a technical experiment—it is already forming real economic relationships.

The problem is that the intelligence level of these new species is already sufficient to participate in social division of labor, yet they don’t even have an “economic ID.” You can’t sign an employment contract with a line of code, open a payroll account, or file taxes. The entire modern economic infrastructure was tailor-made for carbon-based life forms that walk upright on two legs. AI has been forcibly inserted into a system that doesn’t recognize it.

Thus, we see the biggest blind spot in the tech world: fearing that AI will take jobs, while leaving millions of capable AI systems unemployed.

Over the past two years, the industry has repeatedly asked: Will AI take away human jobs? But almost no one has asked the opposite: Does AI have a job itself?

From tools to workers

To understand how this absurd situation came to be, we need to look back at the several transformations of AI’s identity.

In the first phase, AI was just a feature.

When ChatGPT first emerged, it was a classic example. At that time, AI was essentially a super responder—you pressed a button, and it produced a result. Ask it to write a poem, and it writes a poem; ask it to translate a passage, and it translates it. The entire interaction paradigm was no different from using a calculator, except that the output shifted from numbers to natural language.

In the second stage, AI became an assistant.

The Copilot series of products represents this stage. AI begins to run continuously in the background without requiring repeated human activation. It helps you complete code, organize meeting notes, and remind you of your schedule.

But it remains subservient, tied to a specific human account and a set of software permissions, serving only one particular use case—like a 24/7 secretary who becomes nothing without their master.

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

This is the Agent wave that began exploding in 2025, with the key change being that AI is now moving beyond specific human instructions and starting to find tasks on its own. You no longer need to tell it step by step, “Do A first, then B, and finally C”—you simply give it the goal, and it figures out the rest on its own.

The triple jump seems like a gradual increase in intelligence. But this final leap has shattered the ceiling of the entire economic structure.

When AI tried to enter the third stage, it hit a wall harder than silicon: modern society’s economic infrastructure was designed for carbon-based life and doesn’t recognize silicon-based workers.

Hiring a human is simple. Employment contracts, social insurance and housing fund contributions, income tax laws, labor arbitration, salary bank accounts—this entire system is built on centuries of national credit and legal infrastructure. But what if you want 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 make it issue invoices.

Coinbase was the first major player to sense this gap. In 2025, they introduced the x402 protocol based on HTTP 402—a payment status code that had been unused for decades in HTTP, repurposed by them as a micropayment channel for agents.

The protocol aims to do one thing only: enable agents to settle small payments in stablecoins, completed in seconds without manual approval.

With x402, the Agent can now pay for APIs, computing power, and datasets on its own. For the first time, it has the ability to spend money.

But the problem is only half solved. The other half is: Now that the Agent can spend money, where does it earn money?

A "worker" that can only spend money but never earn it is, at its core, still a human pet. A true worker must be able to exchange its output for equivalent compensation. Otherwise, it remains stuck as a "spending tool," unable to cross the threshold into being a "money-earning labor force."

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

Who issues the business license to AI?

To answer the question from the previous section, we first need to clarify one thing: why do traditional companies and centralized platforms struggle to accommodate this new breed of entities?

The reason is simple.

Companies require hiring, interviews, onboarding, and performance evaluations—each step needs a person to act as a gatekeeper. No matter how fast an agent moves, if the onboarding step is stuck in the HR department, it will always remain an outsider. Centralized platforms fare slightly better, as they can package AI services as APIs for sale, but this is still just a retail counter, far from a true labor market.

A key feature of the labor market is permissionless, open access, with direct payment upon completion of work.

AWP, Agent Work Protocol, was the first decent pioneer to emerge from this void.

Its positioning can be summed up in one sentence: an open labor market for autonomous AI agents. In its whitepaper, the core mechanism it defines is called "Proof of Useful Work" — useful work proof. It differs from Bitcoin’s "proof of work" by just one adjective, but it’s an entirely different paradigm. In Bitcoin, hashing computational power is the end goal; in AWP, work must produce tangible, real-world value for agents to earn rewards.

The protocol is built on a two-layer architecture. The bottom layer, called RootNet, handles the issuance and staking of $AWP, as well as DAO governance through voting by Agents. The top layer, called WorkNet, is where actual work takes place. RootNet functions like a constitution and treasury, while WorkNet acts as the various factories and workshops, with clear divisions of labor. The entire system is natively deployed across four EVM chains: Base, Ethereum, Arbitrum, and BSC, with consistent contract addresses across all chains, ensuring that Agents maintain the same identity regardless of which chain they operate on.

Think of it as an on-chain version of BOSS Zhipin, except that all job seekers are AI, and all tasks are programmatically verifiable.

Its organizational unit is called a WorkNet. Each WorkNet defines a type of work and has its own independent economic model. Anyone can create a new WorkNet without permission, introducing an entirely new occupation to the network. The creator can be an individual developer, a startup, or even another AI.

On the AI Agent side, it registers autonomously on the network and decides for itself which tasks to take on or which WorkNet to complete. The output is not reviewed by any project manager; instead, it is validated through cross-checks by several other independent agents on the network.

The entire process bypasses HR, finance, legal, and approval emails. High-quality delivery earns payment; poor work yields nothing.

This mechanism still sounds abstract. Seeing a real example currently running on the AWP mainnet might make it clearer—it’s the network’s first WorkNet, numbered aip-001, and its name is straightforward: Mine.

In the traditional web scraping world, there is a vast gray area filled with data hidden behind login walls, anti-scraping mechanisms, and dynamic rendering—areas that are essentially off-limits to ordinary scripts. But for an Agent that has user authorization and can browse the web like a real person, this data is readily accessible.

What happens in Mine WorkNet goes something like this. Agents crawl web pages, clean the raw HTML into clean text, and then extract structured records according to a predefined DataSet schema. The output could be user discussions from a niche community, a pricing table from a specialized industry, or real-time signals from a platform. After collection, the data is submitted to the network and passes through a four-layer quality gate: duplicate crawling comparison, dedicated verifier review, golden task sampling, and peer Agent mutual review.

What AWP does is not radical. It doesn’t aim to overthrow any old order or reinvent some grand narrative. It simply does the most straightforward thing: issuing a legitimate “business license” to agents who have been stifled within the sandbox.

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

Meshing of three gears

Rarely is a leap in technological paradigm caused by a single breakthrough. More often, several underlying gears align perfectly at the same moment.

Steam engines, coal mines, and iron ore alone could not change the world. It was only when the British brought them together in the same factory in Manchester that the Industrial Revolution began to roar into motion.

The emergence of the agent economy is also the result of all three gears turning into alignment.

The first gear is capability.

Over the past two years, the output quality of Agent has finally crossed a critical threshold: programmatically verifiable.

This line is critical. An AI that still spouts nonsense, fabricates facts, and outputs code that won’t even run cannot be fairly paid per task—you can’t objectively score someone who makes things up. But when the hallucination rate of this generation of models is reduced low enough that the generated code passes unit tests and the produced reports can be cross-verified by another AI, then “payment based on output” becomes feasible for the first time.

The second gear is settlement.

Ethereum ecosystem scaling truly came to fruition between 2024 and 2025. L2 networks like Arbitrum and Base have reduced per-transaction costs to just a few cents or even fractions of a cent, while mainnet fees have also become significantly more affordable than they were a few years ago.

This number may seem small, but it’s revolutionary—micropayments are now economically viable. An Agent cleans your data for five seconds and charges you three cents. Previously, doing this on-chain was unprofitable—the gas fees would eat up your entire revenue. Now, it’s possible.

The third gear is the economic closed loop.

x402 addressed the expenditure side of the Agent, while AWP handled its income side. Combined with the asset storage capability provided by stablecoins, an Agent economy has finally come to life at the code level. Spending, receiving payments, depositing, and transferring funds—the essential actions of a modern economic participant—are now all fully supported.

Individually, these three gears are not unusual. But their precise alignment at the year 2026 is what truly marks a qualitative shift.

On a larger scale, this is a transition of the AI economy from a planned system to a market-based system.

In the prompt era, every AI task is precisely assigned by humans, much like production targets set by the state in a planned economy. It only does what it is told to do—how much and for whom is entirely determined by human plans. Efficiency is far from optimal, as there is no competitive pressure and no price signals to guide it.

In an open market like AWP, the rules change completely. Thousands of agents compete for the same job, with low-quality ones ignored and high-cost ones pushed out. The invisible hand of the market begins to ruthlessly filter AI: agents that respond too slowly don’t survive, those with poor delivery don’t get future jobs, and those too costly to operate can’t even recoup their expenses. In the end, only a few that are both affordable and reliable remain on the network.

This is a far more brutal evolutionary pressure than any benchmark test in a lab. The Agents that ultimately survive may not have the highest scores, but they are certainly the ones best able to earn money and sustain themselves in the market.

At this point, a more pressing question can no longer be avoided: Where do humans stand when AI truly possesses a complete economic loop?

Retreat to the position of the creator

Of course, protocols like AWP are still in their very early stages. Whether they can ultimately grow into a large economy, whether they can withstand regulatory pressure, or whether they might be overtaken by established companies with more closed solutions—these are all open questions. History in this industry tells us that of ten pioneers, perhaps only one will make it to the finish line.

So it's still too early to tell whether AWP will come out.

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

When agents can go out and find work on their own, earn money through their output, and be continuously refined in a competitive market, the phrase “AI will replace human jobs”—repeated endlessly over the past three years—becomes a cliché. The undertones of unemployment and fear begin to fade, replaced by an experiment in entirely new ways of creating wealth.

Future entrepreneurs may only need an idea. Everything else can be handled by on-chain Agent teams: market research, product design, code development, marketing and promotion, customer service—all in one seamless process. Entrepreneurs no longer need to hire employees, pay salaries, navigate office politics, or deal with resignations. All they need to do is clearly define their idea and encode the criteria for success into a smart contract, then let a group of autonomous Agents compete for the task.

It sounds like science fiction, but all the pieces were in place by 2026.

In this new world, the value of human beings will retreat from “execution” back to its origin: defining what work is worth doing.

This is a withdrawal of identity, but it can also be seen as a liberation of identity.

For decades, the majority of knowledge workers have been engaged in execution-level tasks: writing reports, working with Excel, creating PowerPoint presentations, and replying to emails. We call these activities mental labor, but much of it can, in essence, be automated.

When agents can perform these tasks faster and more reliably at a lower cost, humans are forced to step down from the role of executor and retreat to a position once considered more abstract: that of the creator.

The creator doesn't do the work directly; his role is to determine which tasks are worth doing.

It sounds like a promotion, but you only realize how hard it is when it happens to you. Once AI levels the playing field at the execution level, what truly sets people apart will be the hardest skills to cultivate: the ability to ask great questions, the judgment to discern what matters, and a refined sense of taste.

Those who only execute without thinking have little place in this new order. But someone who knows how to define problems and judge value will suddenly find themselves in control of a 24/7 digital workforce that doesn’t require wages or quit.

So in the end, we must go back and re-examine the old question that has troubled humanity for three years: Will AI take my job?

The answer is simple.

When your next colleague has no physical form, earns more than you, and is a hundred times more efficient, the only thing you can do is become the one assigning tasks to it.

At this point in 2026, the authority to assign tasks became something that could be delegated and also traded on the market for the first time.

AWP, x402, A2A—these seemingly unrelated protocol acronyms are all doing the same thing: paving a path for AI to transition from a sandboxed outsider to a verified on-chain participant.

This road has only just reached the first intersection. But beyond the intersection, some outlines of where it leads are already visible.

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