Can Workers Own The Ai Replacing Them? Action Model Tests A Radical Model Of Automation Ownership

What Happened

Action Model, today, has launched an invite-only Chrome extension that allows users to train an AI system by sharing real browser activity like clicks, navigation paths, typing, and task flows. The platform calls it a Large Action Model (LAM), capable of learning how to perform digital work, not just generate content. In return, contributors receive points that may convert into $LAM governance tokens, intended to represent participation rights in how the system evolves.

Market Context

Action Model’s extension collects user-approved behavioral data to train the AI. Tasks like submitting payroll, managing CRM entries, or running basic operations can be recorded once and repeated by the model. Contributors may publish automations to a public marketplace, where usage can be tracked and rewarded under the platform’s incentive model.

With Action Model, users do not need to write code or manage integrations. They simply record how they complete a task. The AI learns from those real user flows and becomes capable of repeating them independently.

This makes Action Model flexible enough to capture edge cases and undocumented workflows that traditional systems cannot reach.

Other projects that rewarded social engagement or posts were recently banned from major platforms after generating large volumes of AI spam, reply bots, and fake interactions. In response, API access was pulled and token ecosystems collapsed under the weight of low-quality activity.

Why It Matters

AI companies are racing to automate everything, from writing code, generating images, scheduling ads, summarizing meetings, and more. But as these systems improve, their impact on human labor becomes harder to ignore. Some experts now warn that generative AI could trigger a wave of massive job displacement that will hit faster and deeper than most economies are prepared for.

To avoid the problems that have plagued earlier crypto reward systems, Action Model uses behavioral analysis to verify real user input. The system looks for structure, timing, variation, and decision-making signals — things that bots or click farms cannot easily fake.

Details

Rather than resisting the future, one crypto native platform is betting on a different approach. If automation is inevitable, then ownership should be as well.

“If AI is going to replace digital labour then workers should own the machines doing the replacing,” says Action Model founder Sina Yamani.

Training the AI that Does the Work

Unlike chatbot models that generate content, LAM’s are designed to operate software directly. The idea is simple: if a human can do a digital task with a mouse and keyboard, a trained AI agent should be able to do it too.

“The last few years were chatbots. Now it’s automation,” says Yamani. “There are around one billion people employed to use a computer. If a company is offered a tool that performs the same work continuously at a fraction of the cost, they will use it.”

The rise of agentic AI systems has been widely documented across the industry, as models increasingly move from content generation to autonomous task execution. These systems, as outlined in this explainer, collect and act on real user data, learning how to navigate digital environments autonomously.

The platform has already attracted over 40,000 users through waitlists, referral systems, and partner communities. Access remains invite-only to maintain contributor quality and reward early participants.

How Is This Different From Existing Automation Tools?

Most existing automation tools rely on APIs or rigid integrations. But much of real-world digital work happens in legacy systems, internal dashboards, and tools that were never designed to be automated.

“Zapier automates software. We automate work,” says Yamani. “Only about 2 percent of the internet is accessible via APIs. The other 98 percent still requires human interaction.”

What About Privacy?

All training is opt-in, and users are in control of what data is shared. Sensitive sites like email, healthcare, or banking are blocked by default. Users can pause training, block specific domains, or delete contributions entirely.

“The first principle is simple. We don’t need your data. We just need patterns,” Yamani says. “Training data is processed locally and anonymized before it contributes to the model.”

Deleted data is permanently removed and cannot be recovered, even by the company. Contributions are aggregated with data from other users, using k-anonymity to prevent individual reidentification. A dashboard allows contributors to view and manage their training history and rewards at any time.

“While Big Tech collects this kind of data without real consent, we are transparent, user-controlled, and rewarding the people who actually train the AI,” says Yamani.

So Can Bots Game the System?

“Mindless clicking is almost useless,” says Yamani. “Real workflows include intent, pauses, corrections, retries, and decisions. You cannot fake that at scale.”