To close out the year, we hosted an AMA on Discord with our CCO, Arthur to answer some of the most common questions from the Perlix AI community. We covered along of ground, from the beta access, task expansion, contributor rewards, privacy, and how the platform is evolving.
Here’s a recap for anyone who couldn’t join live.
How does Perlix AI combine Web3 and AI in a way that’s meaningfully different from existing platforms?
Most data annotation platforms treat contributors as interchangeable labor and optimize only for speed or cost. Perlix AI is building a system where contributors can develop long-term, compounding reputations based on accuracy, consistency, and trust.
By using Web3 primitives like identity, transparent incentives, and reputation, Perlix aligns incentives around quality rather than volume, enabling high-quality human intelligence to play a more meaningful role in AI training.
A lot of people have been asking about access. How is the team thinking about scale and participation during the beta?
The beta is intentionally gated so the team can closely monitor task quality, prevent spam or farming, and collect clean behavioral data. Limiting access allows the reputation layer and validation systems to mature properly before scaling.
Scale is absolutely the end goal, but the team wants to avoid opening access prematurely. As the system hardens, access will gradually expand, with more opportunities to join in the coming weeks ahead.
How is the team thinking about task scope during the beta, and how will it expand over time?
Right now, the focus is on core task types like perception, audio, identification, and basic annotation across text, image, video, and audio data. These tasks help validate quality and reputation scoring.
Over time, task scope will expand into higher-context and higher-value work, including multi-step reasoning and domain-specific tasks in areas like healthcare, law, and robotics. Access to these tasks will be tied to contributor reputation and demonstrated quality.
Will early beta contributors and active community members receive recognition, roles, or reputation benefits as the platform grows?
Yes. Early contributors play a critical role in shaping the platform, and that participation is being tracked internally. Historical contributions, reputation, and community engagement will translate into benefits as the system matures.
These may include recognition, roles, higher trust tiers, and early access to advanced and high-value tasks. The goal is for early contributors to grow alongside the platform.
How does Perlix AI approach privacy, security, and user trust, especially for sensitive tasks like voice or selfies?
Privacy and trust are non-negotiable at Perlix AI. The platform is designed to minimize the collection of personal data wherever possible, and sensitive information is only requested when it serves a clear, explicitly stated purpose. Contributors are always informed about how their data will be used, where it goes, and why it matters for a specific AI training use case.
The team enforces strict internal access controls, complies with major privacy regulations across the US, EU, and Asia, and does not sell raw personal data. Perlix’s focus is on using structured contributions for clearly defined AI training workflows, with transparency and user trust as core principles throughout the beta.
Have any datasets from the beta been used to train AI models yet? How does Perle handle data ownership and compensation?
Yes. Perlix AI is already working with frontier AI labs, Fortune 500 companies, and government partners, using beta datasets across real-world AI training use cases. These collaborations help validate the platform and ensure the data produced meets high quality and reliability standards.
Feedback from these partners helps refine task design and instructions over time. Perlix AI’ business model focuses on providing structured, high-quality contributions for specific AI training use cases, not selling individual personal data, with compensation and ownership handled at the contribution level.
Looking ahead, what excites the team most about where Perlix AI is headed?
Perlix AI is not just building a data annotation platform, but infrastructure for human intelligence in AI. Beyond annotation, the team plans to support the full AI training pipeline wherever human input is needed.
This includes evaluation, reinforcement learning, preference ranking, and higher-level reasoning tasks. While still early, the team believes this approach will fundamentally change how AI models are trained and improved.
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Huge thanks to the community for the great questions and ongoing engagement. As the platform continues to evolve, contributors will play an even bigger role in shaping what Perlix AI becomes. We’re excited for the new year ahead and can’t wait to share what’s coming next!