The future of AI demands not just decentralized compute, but secure and programmable compute. Today, we're excited to announce our partnership with Lilypad, a pioneering serverless compute network that's revolutionizing how we think about AI infrastructure.

Redefining Decentralized AI Infrastructure

Lilypad has built an impressive marketplace for distributed GPU compute, making AI more accessible and efficient through decentralized infrastructure. However, as AI models and data become more valuable, the need for encrypted compute and secure model deployment becomes critical.

Why Gateway's Shared Private State Matters

This is where Gateway's technology becomes transformative. Through our MPC-based shared private state, AI models and sensitive data can remain encrypted while being fully operational and composable on-chain. Traditional privacy protocols require complex key management or hardware dependencies, Gateway uses information-theoretic security to deliver performant privacy. All the privacy, none of the overhead. Gateway provides the greatest flexibility and creativity for builders to create diverse applications. 

How this changes DeAI?

The combination of Lilypad's compute marketplace and Gateway's encrypted state enables… 

1. Private Model Training: AI developers can now train models on encrypted datasets without exposing sensitive information
2. Secure Model Deployment: Deploy AI models that remain encrypted while being fully operational
3. Confidential Compute Jobs: Process sensitive data while maintaining privacy throughout the computation

Unlocking new possibilities! 

Gateway’s shared private state can enable a variety of applications to be built with Lilypad’s compute infrastructure, especially for enterprise adoption.

1. Federated Healthcare AI

Medical institutions can now collaborate on AI model training while keeping patient data encrypted. Hospitals share compute resources through Lilypad's marketplace while Gateway's shared private state ensures patient records remain secure yet usable for training. This enables development of more accurate diagnostic models without compromising patient privacy.

2. Secure Financial Model Training

Financial institutions can train AI models on encrypted transaction data across organizations. Through Lilypad's distributed compute network and Gateway's encrypted state, banks can develop fraud detection and risk assessment models using collective data while maintaining complete confidentiality of customer information.

3. Private Enterprise AI Development

Companies can leverage external compute power for AI development without exposing proprietary data or models. Engineering teams access Lilypad's GPU marketplace while Gateway ensures their training data and model architecture remain encrypted. This enables faster development cycles without security trade-offs.

Building the Future of DeAI

Together, Gateway and Lilypad are creating infrastructure that makes AI not just decentralized, but truly encrypted, secure,  and programmable. This isn't about adding privacy features - it's about enabling a new paradigm where sensitive AI models and data can be securely utilized at scale.

As Lilypad continues to democratize compute access and revolutionize AI deployment, Gateway's technology ensures that this revolution happens with privacy and security built in from the ground up.

The future of DeAI requires both powerful compute and programmable privacy. With Lilypad and Gateway, that future is being built today.