Can Workers Own the AI Replacing Them? Action Model Tests a Radical Model of Automation Ownership 

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.

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. 

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. 

“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.”

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. 

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.”

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.

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? 

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.

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

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.

ActionFi, the platform’s reward engine, is designed to avoid that trap entirely. It does not pay for tweets or clicks. It rewards verified workflows that reflect real, structured digital labor.

“We don’t pay for noise. We pay for useful paths,” Yamani adds.

Who Actually Owns the System? 

Today, Action Model controls the extension, training logic, and reward systems. But the project has committed to transitioning ownership to $LAM token holders over time. A DAO structure will eventually allow contributors to govern platform decisions, incentive mechanisms, and model deployment.

“Early systems need coordination. What matters is whether they are centralized by design,” Yamani says.

If implemented as described, ownership would give token holders influence over infrastructure decisions tied to the data they helped generate. 

If AI Is Inevitable, Can Ownership be Too? 

The next generation of AI is being built not just on language, but on labor. From office work to operations, many tasks that happen behind a screen are now within reach of intelligent agents. 

“You’ve heard that millions of screen-based jobs will be automated. That isn’t decades away — it’s happening now,” Yamani says. “If your data helps train AI, you should own what gets built.”

Whether Action Model can scale, stay transparent, and build a sustainable economy remains something we will keep an eye on closely in months to come. But its bet is very clear. The defining struggle of AI is not just about what it can do, it’s about who it works for. 

As AI reshapes the world of work, will the future be owned by platforms, or by the people?