Can artificial intelligence replace workers? The business model proves a radical ownership model of automation

Artificial intelligence companies are racing to automate everything from writing code, generating images, scheduling ads, summarizing meetings, and more. As these systems improve, their impact on human work is difficult to ignore. Some experts are now warning that generative AI could lead to a huge wave of… Offset the tasks They will access it more quickly and more deeply than most economies can prepare for.

Instead of resisting the future, one of the original cryptocurrency platforms is taking a different approach. If automation is inevitable, then ownership must be too.

The fire Action Model Today, an invite-only Chrome extension allows users to train an artificial intelligence system by sharing real browsing activity such as clicks, breadcrumbs, typing and activity streams. The platform calls this the Large Action Model (LAM), and it is able to learn how to take digital action, not just generate content. In return, contributors receive points that can be turned into $LAM governance tokens, designed to represent the rights to participate in the evolution of the system.

The founder of the Action Model, Sina Yamani, said that workers should own the machines that do the replacing if AI is going to replace digital work.

Sponsored

Sponsored

Train the AI ​​that does the work

Unlike chat models that generate content, LAM is designed to run software directly. The idea is simple: if a human can perform a digital task using a mouse and keyboard, then a trained AI agent should also be able to perform it.

Yamani said that the last few years have been devoted to chatbots, and now the focus is on automation. There are about a billion people who use computers. If you show a company a tool that still does the same job at a fraction of the cost, they will use it.

The Action Model extension collects behavioral data with user consent to train the AI. Tasks such as submitting payroll, managing CRM entries, or performing basic processes can be captured once and repeated from the form. Contributors can implement automation in a public marketplace where usage can be tracked and rewarded under the platform’s incentive model.

The industry has seen the rise of widely documented proxy AI systems, with models increasingly moving from generating content to performing tasks autonomously. And I said These explanations These systems collect and process real user data, and learn to navigate digital environments autonomously.

The platform has already attracted more than 40,000 users via waiting lists, referral systems and partner communities. Access still requires an invitation to retain quality contributors and reward early entrants.

How is this different from existing automation tools?

Most current automation tools rely on rigid application interfaces or integrations. But most of the real digital work happens in traditional systems, internal control panels and tools that were never designed to be automated.

Zapier automates software, while we automate business, Yamani said. Only 2% of the Internet can be accessed via application interfaces. The other 98% still requires human interaction.

Sponsored

Sponsored

The action model allows users to not write code or manage integrations. All a user has to do is record how they complete a task. The AI ​​learns from these real user flows and is able to replicate them independently.

This made the Action Model flexible enough to capture exceptional cases and undocumented workflows that traditional systems cannot capture.

What about privacy?

Make all training optional, and control what data users share. Block sensitive sites like email, healthcare or banking sites by default. Users can pause training, block specific domains, or remove contributions entirely.

Yamane said the first principle is simple: we don’t need your data, we just need the models. Process the training data locally and anonymize it before contributing to the model.

The data is permanently deleted and cannot be recovered, even by the company. Combine contributions with other users’ data, using k-anonymity to prevent individuals from being tracked or re-identified. Use an admin panel that allows contributors to view and manage their training history and rewards at any time.

Yamane said that while the big tech companies collect this kind of data without real consent, we are transparent, users control their data, and we reward the people who actually train the AI.

Sponsored

Sponsored

Can bots game the system?

Avoiding the problems that have plagued previous cryptocurrency reward systems, the Action Model uses behavioral analytics to verify real user input. The system looks for structure, timing, variety and decision signals – things that bots or click farmers can’t easily fake.

Yamane said that random clicking is almost useless, and that a real workflow involves intentions, pauses, corrections, repeated attempts and decisions. And you can’t fake that on a massive scale.

Have other projects reward social interaction or posts have been made Recently blocked Of the major platforms after generating huge amounts of spam messages generated by artificial intelligence, bots and fake interactions. As a result, API access was withdrawn, and the token ecosystem collapsed under the pressure of low-quality activity.

The platform’s ActionFi rewards engine is designed to avoid this pitfall altogether. Do not pay money for tweets or clicks. Reward documented workflows that reflect real, organized digital work.

We don’t pay for noise, we pay for useful paths,” added Yamane.

Sponsored

Sponsored

Who really owns the system?

Today the Action Model dominates addition, training logic and reward systems. But the project has committed to gradually transfer ownership of the system to $LAM token holders over time. The DAO architecture will then allow contributors to participate in platform decisions, incentive mechanisms and model implementation.

Yamane stated that early systems needed coordination, and what really mattered was whether the design was centralized or not.

Implement the system as described, ownership will give token holders influence over infrastructural decisions related to the data they helped produce.

If AI is deterministic, can property be too?

The next generation of AI is built not only on language, but also on action. Artificial intelligence has expanded its scope to include many activities that are behind the screens, from office work to operational processes.

Yamane said you’ve heard that millions of screen-based jobs will be automated. This is not a matter of decades to come – it is happening now. If your data helps train the AI, you should own what’s built.

Watch carefully in the coming months to see if Action’s model can scale, maintain transparency, and build a sustainable economy. But his bet is pretty clear. The crucial conflict in AI is not only about what it can do, but also about who it serves.

As AI changes the world of work, ask the question: does the future belong to platforms or people?