Deep Dive
1. Near-term roadmap (0–6 months)
The XCAD Social platform enters final testing, allowing users to:
- Generate AI influencers via text prompts (photorealistic/cartoon avatars)
- Choose content niches (crypto, gaming, fashion)
- Govern AI behavior through token-weighted voting
A critical milestone is the bonding curve mechanism – users must lock XCAD tokens to list new AI influencers on the platform’s DEX. This creates immediate buy pressure but risks oversaturation if adoption lags.
2. Long-term vision (6+ months)
The team aims to:
- Integrate AI influencers with YouTube/TikTok via API partnerships (unconfirmed)
- Implement revenue-sharing from AI-generated content ads
- Develop cross-platform reputation scores for AI personas
Success hinges on overcoming two hurdles:
1. Content moderation for decentralized AI output
2. Avoiding "bot influencer" stigma through quality controls
3. Potential impact
Bull case (3-6 months):
- Each new AI influencer requires 10,000-100,000 XCAD staked (per blog examples)
- Current circulating supply: 47.7M XCAD → 477-4,770 influencers could drain liquid supply
Bear risks:
- 89% of XCAD held by whales – rapid sell-offs possible if AI traction disappoints
- Requires 10-100x more users than current holder base (2,273 addresses)
Conclusion
XCAD’s AI pivot attempts to solve its creator onboarding bottlenecks but introduces execution risks in an unproven market. The bonding curve mechanics could create reflexive token demand if early adoption meets targets.
What engagement metrics (AI influencers created, daily content output) will best indicate whether this model is gaining traction?