A conversation with Sina Yamani, Founder of Action Model

“AI is inevitable, but ownership is a choice. If automation is the future, then the people building it should share in the upside.”

Guest Bio

Sina Yamani is the founder of Action Model, a community-led AI automation ecosystem focused on training models using real-world user journey data.

With an engineering background and decades of programming experience, Sina previously built and exited Yoello, a UK-based fintech startup that developed QR codes to bypass Visa and MasterCard. Yoello scaled to millions of users within a year, powered by strong community alignment.

Today, Sina is focused on what he sees as the next phase of AI: automation. Through Action Model, he aims to enable individuals to participate in training and owning AI systems that will increasingly automate computer-based work across industries.

Key Takeaways

• AI is moving from chat to execution. The next stage is automation—AI that performs tasks, not just generates text.
• Action data is fundamentally different from language data. Automation requires real user journey mapping, not scraped internet text.
• Onboarding AI employees is the biggest bottleneck. Training automation systems mirrors training human employees.
• Community ownership is central to Action Model. Contributors earn proportional stakes based on measurable participation.
• 2026 marks the beginning of large-scale automation adoption.

Main Discussion Questions

What is Action Model, and how does it differ from traditional AI platforms?

Sina explained that while most AI systems today are built on text-based large language models, Action Model is focused on training AI to perform real-world actions. Traditional LLMs are trained on scraped internet text—articles, documentation, forums, and social media. They are excellent at explaining how to do something.

However, automation requires a different type of training data: actual user behavior. Through a browser extension, Action Model collects anonymized user journey data—what buttons were clicked, in what order, and how workflows unfold on real websites. This enables AI to replicate tasks with far greater accuracy and reduced hallucination rates compared to purely text-trained systems.

Why is action-based training data difficult to obtain?

Unlike text, action data cannot simply be scraped from the internet. Automation requires detailed knowledge of user interactions: button sequences, mouse coordinates, DOM elements, and conditional workflows.

Sina emphasized that without thousands of real user journeys mapped across platforms, automation systems remain brittle and unreliable. Enterprises demand near-perfect accuracy—around a 99% success rate—before trusting AI with operational tasks. This makes high-quality action data both scarce and strategically valuable.

Why did Action Model grow so quickly after launch?

Sina attributed rapid growth to two primary factors: mission alignment and invite-only distribution.

The platform is community-owned, and contributors earn proportional rewards based on their participation. This creates strong alignment between growth and individual incentives. Additionally, Action Model operates on an invite-only system, encouraging organic, network-driven expansion.

Within weeks, the platform scaled to over 180,000 users without traditional marketing. Sina believes this growth will accelerate as automation anxiety increases and more individuals seek ways to participate in the AI economy.

How does Action Model protect community ownership in a startup environment?

Action Model quantifies different types of contributions—such as running the browser extension, completing targeted training tasks, or building workflows. Rewards are distributed pro rata based on measurable input.

Long term, the structure is designed to evolve into a foundation and DAO model, where governance decisions are voted on by the community. Sina acknowledged the tension between startup acquisition models and decentralization, but emphasized transparency, structured reward systems, and community governance as key protective mechanisms.

What happens to businesses in 2026 as automation accelerates?

Sina’s view is clear: 2026 marks the year automation moves from experimentation to integration.

Most businesses have not yet adopted automation tools, largely due to complexity and onboarding friction. Traditional Robotic Process Automation (RPA) systems are brittle and inflexible. The next generation of AI employees will be context-aware, capable of error correction, and able to operate continuously in cloud environments.

Sina predicts that up to one billion computer-based roles globally are at risk over the next few years, as businesses prioritize efficiency and cost reduction. He argues that individuals must prepare—either by building automation systems, contributing to them, or transitioning into roles that are less susceptible to automation.

Final Thoughts

Sina Yamani’s message is direct: automation is inevitable.

The shift from chat-based AI to action-based AI will fundamentally reshape work. The question is not whether automation will happen, but who will benefit from it.

Through Action Model, Sina proposes a model where individuals contribute to training automation systems and earn proportional ownership in the ecosystem. In a world where AI infrastructure risks being controlled by a handful of corporations, he argues that community participation and transparency are the only scalable alternatives.

The automation wave is coming. The only choice is whether to ignore it—or surf it.