AI companies are racing to automate everything from writing code to generating images to scheduling ads and summarizing meetings. However, as these systems improve, their impact on human labor cannot be ignored. Some experts are now warning that generative AI could trigger a wave of massive job losses, hitting most economies sooner and deeper than they are prepared for.
Rather than resisting the future, one crypto-native platform is betting on a different approach. If automation is inevitable, so is ownership.
Action Model today released an invite-only Chrome extension. This allows users to train AI systems by sharing real browser activity such as clicks, navigation paths, inputs, and task flows. The platform calls this the Large Action Model (LAM), which allows you to learn how to do digital work in addition to generating content. In return, contributors receive points that can be converted into $LAM governance tokens, which are intended to represent a right to participate in how the system evolves.
“If AI is going to replace digital workers, then workers should own the machines that will replace them,” says Action Model founder Sheena Yamani.
Train the AI to do the work
Unlike chatbot models that generate content, LAM is designed to interact directly with the software. The idea is simple. If humans can perform digital tasks using a mouse and keyboard, trained AI agents should be able to do it too.
“The last few years it was chatbots, now it’s automation,” Yamani says. “There are about 1 billion people employed to use computers. If companies were given a tool to do the same task over and over again at a fraction of the cost, they would use it.”
Action Model extensions collect user-approved behavioral data to train AI. Tasks like submitting payroll, managing CRM entries, and performing basic operations can be recorded once and then repeated by the model. Contributors can publish their automations to a public marketplace, where their usage can be tracked and rewarded based on the platform’s incentive model.
The rise of agent AI systems has been widely documented across industries as models increasingly move from content generation to autonomous task execution. As outlined in this discussion, these systems collect and act on real user data to learn how to navigate digital environments autonomously.
The platform has already attracted over 40,000 users through its waiting list, referral system, and partner community. To maintain the quality of contributors and reward early participants, access will continue to remain invite-only.
How is it different from existing automation tools?
Most existing automation tools rely on APIs or strict integration. However, much of the actual digital work is done on legacy systems, internal dashboards, and tools that were not designed to be automated.
“Zapier automates software; we automate work,” Yamani says. “Only about 2% of the internet can be accessed via APIs. The remaining 98% still requires human intervention.”
With the action model, you don’t have to write code or manage integrations. They just record how they completed the task. The AI will be able to learn from these real user flows and repeat them independently.
This makes action models flexible enough to capture edge cases and undocumented workflows that traditional systems cannot reach.
What about privacy?
All training is opt-in, giving users control over what data is shared. Sensitive sites such as email, medical, and banking are blocked by default. Users can pause training, block specific domains, or delete posts permanently.
“The first principle is simple: we don’t need data, we just need patterns,” Yamani says. “Training data is processed locally and anonymized before contributing to the model.”
Deleted data is permanently deleted and cannot be recovered even by the company. Posts are aggregated with data from other users using k-anonymity to prevent re-identification. The dashboard allows contributors to view and manage their training history and rewards at any time.
“Big tech companies collect this kind of data without any real consent, but we’re transparent, we’re in user control, and we’re actually rewarding the people who train the AI,” Yamani says.
So, can bots manipulate the system?
To avoid the problems that plagued previous crypto reward systems, action models use behavioral analysis to validate actual user input. The system looks for structure, timing, variation, and decision signals that cannot be easily faked by bots or click farms.
“There’s little point in clicking mindlessly,” Yamani says. “Real workflow includes intent, pause, fix, retry, and decision. You can’t fake this at scale.”
Other projects that reward social engagement and posts were recently banned from major platforms for generating too much AI spam, reply bots, and fake interactions. In response, API access was pulled and the token ecosystem collapsed under the weight of low-quality activity.
The platform’s rewards engine, ActionFi, is designed to avoid that trap entirely. We don’t charge for tweets or clicks. Reward validated workflows that reflect a real-world structured digital workforce.
“We don’t pay for noise. We pay for roads that help,” Yamani added.
Who actually owns the system?
Currently, the action model controls the extensions, training logic, and reward system. However, the project has committed to transferring ownership to $LAM token holders over time. The DAO structure ultimately gives contributors control over platform decisions, incentive mechanisms, and model deployment.
“Early systems require coordination, and the key is whether they are centralized by design,” Yamani says.
If implemented as described, ownership would give token holders influence over infrastructure decisions associated with the data they helped generate.
If AI is inevitable, is ownership also inevitable?
The next generation of AI is built on labor as well as language. From administrative tasks to operations, many tasks that take place behind a screen are now within the reach of intelligent agents.
“You’ve heard that millions of screen-based jobs will be automated. That’s not decades away, it’s happening now,” Yamani says. “If your data is going to help you train AI, you need to own what is built.”
We will continue to watch over the coming months to see whether the action model can scale, maintain transparency, and build a sustainable economy. But the stakes are very clear. The defining battle for AI is not just what it can do, but who it will work for.
As AI reshapes the world of work, will the future be owned by platforms or by people?
