Author: Kinjal Shah

Translation: Jiahua, ChainCatcher

In 2024, Sam Altman made a bold prediction: with the rise of artificial intelligence, a billion-dollar company founded by a single person will soon emerge.

The core shift is that for the first time, humanity can scale in the dimension that has always limited it, which is time. When intelligence is no longer constrained by the human need for sleep but is driven by machines that never tire, what will "creation and construction" look like?

Imagine a scenario: an intelligent agent commissions another intelligent agent to complete a task, pays with USDC upon receiving the results, and the entire transaction is settled on-chain in 400 milliseconds, with no intermediaries verifying the process.

Or, an athlete licenses their signature touchdown celebration for a marketing campaign of a video game, regenerated by a world model. Or, a scientist pays directly to the original researcher for a rare dataset needed for an experiment.

We are much closer to this vision than most people think.

The fear that dominates the current discussion (that artificial intelligence is taking jobs) misses a more interesting structural question: what happens when the fundamental unit of labor itself changes?

Every Transition

Regarding why companies exist, Ronald Coase provided the clearest answer in his 1937 paper "The Nature of the Firm": when the costs of coordinating via the market exceed the costs of hiring directly, companies will bring labor "in-house".

Every major labor transformation in history has been a direct result of declining coordination costs. As the friction in finding, paying, and managing work decreases, the boundaries of companies will shift, and work that used to be performed internally can now be done externally.

In the past, artisans operated by a multi-node supply chain, with each craftsman taking a share of the value, and skills passed down through generations. The industrial revolution pushed this distributed model into factories, where coordination concentrated under "one roof," capturing most of the production value.

The internet and mobile devices have once again reduced matching and coordination costs, giving rise to the gig economy (Uber, DoorDash) and the creator economy: ordinary people with a camera and an internet connection began undertaking tasks previously only accessible to studios, publishers, and agents.

Bridge Class

Before the infrastructure capable of capturing all value appears, each of the aforementioned transitions will first run out a "bridge class" that proves the new model works.

Artisans proved that distributed production is feasible, and factories then centralized and captured the value; creators proved that individuals could also build audiences at scale and generate income, and then major platforms (YouTube, Instagram, Substack) took most of the economic gains, becoming the default gathering point in the entire system.

The bridge class took on the risks of new technologies and validated that demand exists. Once the infrastructure catches up, a new batch of institutions will massively capture the value.

The gig economy and the creator economy are two recent bridge classes. They proved that work can be broken down, distributed, and compensated outside of traditional employment relationships.

Yet they still rely on platforms to bundle these economic activities: using Stripe for payment, using YouTube for content distribution, using Uber for ride-matching. While coordination costs have decreased, they have not disappeared, because the infrastructure for payment and identity still assumes both parties in a transaction are human.

Programmable Labor Meets Programmable Money

Today, we are in the early stages of the next transformation, which depends on the simultaneous emergence of two things.

The first is programmable labor. AI agents represent a new category of labor participants, unrestricted by working hours, headcounts, or geography, expanding in scale through computational power rather than by hiring people.

A top-level agent can decompose tasks, delegate them to specialized sub-agents, assess their outputs, and arrange for the next steps, all without human intervention. At this point, the fundamental unit of labor is no longer positions, working hours, or deliverables, but the tasks themselves.

In the past, humans packaged tasks into jobs, jobs into careers, and careers into companies simply because that was the only organizational form available at the time. Once you can directly price a single task and send it out, "packaging" shifts from a structural necessity to an option.

The second thing is programmable money. Today, stablecoins are an asset class worth around $300 billion, with credible predictions from several institutions suggesting it could surge to $2 trillion in the coming years. Stablecoins compress the entire payment supply chain into a programmable transaction.

The gig economy has not fully disaggregated labor because transactions still rely on Stripe, PayPal, or bank accounts, and the assumption behind these infrastructures is a known, ongoing relationship between the two parties.

Stablecoins, however, may be the best solution prepared for this new labor class of agents. An agent can pay another agent based on output, with amounts as little as a fraction of a cent, settling within 500 milliseconds without the need for an account, invoices, or intermediaries.

Meta recently began distributing USDC to creators on Polygon and Solana, while AWS launched AgentCore, which supports stablecoin micropayments specifically for business transactions between agents. These are early signals indicating that the world's largest tech companies have begun viewing stablecoins as the settlement layer for next-generation economic activities.

The combination of programmable labor and programmable money enables for the first time in history the possibility of a production line without an organizational entity, with no companies, no payroll systems, no human resource departments, only a series of tasks deployed, executed, priced, and settled at machine speed.

This is the true disaggregation of labor.

Real-World Applications

Merit Systems has created a product called Poncho, making all of this very concrete. Poncho provides AI agents with a wallet.

With it, agents can bypass payment walls, access advanced tools, and pay for services, only for the actual parts they use. Poncho integrates payment protocols like x402 and MPP, embedding payment authorization directly into HTTP requests: agents see the price, make the payment, and gain access.

This represents another way in which economic value flows on the internet. Agents no longer need to subscribe to a large bundle of services that may or may not be used; instead, they can precisely pay for the data, API calls, or computational power required to complete a specific task.

The early internet once explored this idea under the banner of "microtransactions," but never materialized. One reason was that credit card fees economically could not support such small payments, plus a host of other issues, and there was no internet-native payment rail at that time.

Stablecoins leverage infrastructures like Solana and Ethereum, enabling instant settlements for just a fraction of a cent, meaning pricing can finally align with the granularity of work.

Repackaging

If you follow this hypothesis further, as work increasingly involves agents paying other agents to complete tasks, the shape of companies will change too. You no longer need to bring every function in-house.

What you really need to excel at is defining clearly what needs to be done, what standards to use to measure quality, and how to allow these outputs to combine into a whole that is greater than the sum of its parts.

This extends to the creator economy as well. Peer-to-peer tipping has never really taken off, and platforms like Clubhouse and Farcaster have demonstrated its limitations. However, microtransactions are particularly suitable for machine-to-machine interactions: small payments carry no social awkwardness and do not bring expectations of reciprocity.

If agents become the main consumers of digital content, the subscription models and paywalls that have long dominated the internet may give way to pay-per-use executed automatically by programs.

As AI-generated content floods various channels, the premium for human judgment and craftsmanship will only rise, and the most interesting business models will emerge at the intersection of human taste and machine execution.

In an economy driven by agents, the role of humans is to repackage labor. You are the orchestrator. Your job is to design a system that allows different agents to perform their roles according to specific configurations, creating a flywheel that nudges the desired results into existence.

Your value lies in knowing which tasks to delegate, how to assess them, and how to combine them into something that generates compound returns.

Companies will not disappear, but future companies will increasingly resemble an intelligent layer built on top of a global programmable labor market rather than a container for labor.

免责声明：本文章仅代表作者个人观点，不代表本平台的立场和观点。本文章仅供信息分享，不构成对任何人的任何投资建议。用户与作者之间的任何争议，与本平台无关。如网页中刊载的文章或图片涉及侵权，请提供相关的权利证明和身份证明发送邮件到support@aicoin.com，本平台相关工作人员将会进行核查。