$AIDA Explained: AI You Can Own 🧠

The Spaces featured Terry from Idas outlining a privacy-first AI platform, upcoming product milestones, and policy engagement. He announced a new website, refreshed app UI, and a transparent three-year roadmap covering closed beta, open beta, and GA. Core principles are privacy and ownership: users control their data, models, and AIs within single-tenant, encrypted enclaves where Idas cannot access plaintext—even under subpoena—while enabling consent-based sharing and monetization. Terry described converging AI and blockchain with a token that validates identity, purchases inference/compute, and attests to compute integrity, evolving from a meme launch on Bonk to utility, function, and governance. A marketplace will let developers monetize agents, tools, and connectors (e.g., a Goodwill agent that automates skills and job-resume workflows). On policy, Terry will attend a Congressional session on crypto policy to advocate privacy-aware compliance and avoid guidance that paints crypto innovators as bad actors. Government adoption will emphasize AI-readiness consulting and operational efficiency, citing CMS claims processing as a flagship use case. Addressing learning under privacy, he outlined L0–L3 approaches: RAG/memory, local adaptive fine-tuning (e.g., LoRA), federated privacy-preserving aggregation, and offline global refresh. Idas differentiates on end-to-end privacy versus big hosted and purely local models, and will publish videos, UI clips, and blogs answering remaining questions.

Idas Ideas Twitter Spaces Recap and Analysis

Participants and Roles

• Moderator. Name not provided. Opened and closed the session.
• Terry. From Idas Ideas. Primary speaker providing updates, roadmap context, technical architecture, tokenomics, and Q and A responses. Referenced colleague Nick as sharing a government background.

Announcements and Operational Updates

• New website and UI. Team is working on a new ideas.io website and a new app UI. Short demo clips are planned to be shared soon.
• Public roadmap. A transparent roadmap covering closed beta, open beta, and general availability will be published. Expect visibility into phases, progress, successes, and setbacks. The roadmap spans three years, combining long term goals with tactical project activities.
• Upcoming policy engagement. On September 15, Terry will travel to Washington DC to attend a Congressional session on crypto policy and compliance, followed by a dinner with Members of Congress and White House staffers. Objective is to help shape the narrative and policy so guidance does not presume crypto participants are bad actors. A debrief will be shared after the meetings.
• Future Spaces topics. Prior session touched hardware. A future session will dive into software, data, people, and the architecture.

Core Vision and Principles

• Privacy and ownership as bedrock. Users should own their data, the models that operate on their data, and the AI that uses those models. Access to data must be under the user’s control, including the ability to grant access on their terms and to monetize when desired.
• Convergence of AI and blockchain. The platform merges AI capabilities with blockchain based assurances to enhance trust, transparency, and veracity while enforcing privacy via encryption.

Architecture and Security Model

• Single tenant enclave. Each user is deployed as a tenant within their own enclave, a secure environment provisioned on shared infrastructure. Think of it as a bespoke package of hardware, software, data, and people deployed for the user’s identity.
• End to end encryption. Data is encrypted at rest, during extraction, and during processing and movement. Only the user holds the keys to the enclave.
• Zero visibility by operator. Idas Ideas cannot see a user’s data. Even under subpoena, the operator cannot access it. Future functionality will enable controlled sharing of tenancy, models, and data between users.
• Blockchain aligned guarantees. Transparency and immutability principles are combined with encryption to secure transactions and data, reinforcing trust in the environment’s veracity.

Product Status, Agents, and Token Rationale

• AI first deployment. Agents are already in use by government, commercial entities, and individuals.
• Token purpose. The token validates identity and validates compute used for inference. It is also the unit for purchasing inference and compute. The design targets proof of value so only the rightful user can access their AI and its responses within their enclave.
• Tokenomics flywheel. A visual infographic of the tokenomics flywheel is forthcoming. The project began with a meme style launch and is moving toward utility, function, and governance. Team expressed excitement about being on Bonk.

Marketplace and Developer Opportunities

• Agent marketplace. Users will be able to create AI agents and publish them in a marketplace for others to use, enabling creators to earn.
• Tools and connectors. Developers can build connectors, tools, and agentic workflows. When tools are used inside agents, toolmakers get paid. This supports an ecosystem of reusable components.
• Example integration. With Goodwill, an agent ingests email information, engages users on skills and upskilling, and connects into organizational systems to assist with resumes and job placement. Connectors of this type are opportunities for developers to build and monetize.

Government Policy Coordination and Adoption Strategy

• Policy engagement. Terry and Nick have deep government experience in waste, fraud, and abuse investigations, compliance, counterterrorism, and criminal exploitation units. The team is actively engaged with policymakers to help define compliance frameworks in real time rather than passively receiving guidance later.
• Agency consultations. Active discussions with Defense Logistics Agency, Space Force, Air Force, Transportation, Federal Aviation Administration, Homeland Security Citizenship and Immigration Services, and Health and Human Services Centers for Medicare and Medicaid Services.
• AI ready focus. Success in government requires more than delivering tools. The team helps agencies become AI ready by aligning policy, process, and organizational readiness.
• CMS use case. Human reviewers assess claims across numerous programs and face a large backlog. Work is repeatable and suitable for AI. The team proposes secure internal deployment that protects health records while improving operational efficiency and productivity, reducing backlog, and enabling more effective public spending.

Roadmap, Testing, and Incentives

• Phased rollout. Closed beta, open beta, and general availability are planned. The detailed 3 year roadmap will be shared publicly.
• Beta programs and rewards. There will be referral mechanisms and rewards for beta testers who report issues or bring volume. Bug bounty programs will be implemented for developers and individuals during the rollout.

Learning Without Centralized Data Collection

• Challenge. Big labs improve models via centralized user interaction data. Idas Ideas prioritizes privacy and does not centralize user data.
• Multi level learning framework. Four levels L0 to L3 include retrieval and memory based approaches where training is not required, local adaptive fine tuning via LoRA and PEFT on models, federated learning with privacy preserving aggregation, and a global refresh offline process. More technical detail will be provided in blogs and future sessions.

Differentiation Versus Other AI Options

• Privacy as the defining feature. Large model providers and local desktop setups often lack true privacy guarantees. Idas Ideas differentiates by providing single tenant enclaves with end to end encryption and user held keys. Token mechanisms, including zero knowledge proofs discussed by the team, help validate identity, access to compute, and the trustworthiness of transactions.

Real World Use Cases and Sectors

• Core need. Individuals and enterprises want AI without surrendering personal information, trade secrets, or sensitive and classified material. A private environment with user control addresses this need.
• Sectors of interest. Media and entertainment, retail, government, and broader commercial applications. Specific sector deep dives will be shared in future posts and sessions.

Q and A Highlights

• How will Idas Ideas revolutionize and scale privacy while coordinating with the US government. By staying tightly engaged with policymakers, attending Congressional discussions, and co shaping compliance metrics from the outset, rather than receiving and reacting to guidance after the fact.
• How will the tech be used in government. Focus on making agencies AI ready, deploying secure internal AI that protects sensitive data, and driving operational efficiency. Active engagement with DLA, Space Force, Air Force, DOT, FAA, DHS USCIS, and HHS CMS. CMS claims processing and backlog reduction highlighted as a flagship opportunity.
• Can developers build on top of the AI. Yes. The platform will open for creation of agentic workflows, connectors, and tools. A marketplace will reward toolmakers when their components are used.
• Will there be referral or rewards for beta testers. Yes. Expect phased programs, bug bounties, and rewards during beta through GA.
• Timeline from closed beta to open beta to full release. Planned and will be published in the public roadmap. The roadmap spans three years.
• If the system does not collect centralized data, how do agents improve. A layered learning approach L0 to L3 combines retrieval, memory, local adaptive fine tuning via LoRA and PEFT, federated learning with privacy preserving aggregation, and an offline global refresh. More detail to come.
• What separates this from other AI. Privacy first design with single tenant encrypted enclaves, user held keys, and token gated identity and compute validation.
• Real world use case. Private AI for individuals and organizations that need to protect personal info, trade secrets, and government sensitive data, with planned use across media and entertainment, retail, government, and commercial domains.

Key Takeaways and Highlights

• Privacy and ownership are foundational. Users own data, models, and AI. The operator cannot access user data; keys remain with users.
• Enclave architecture with end to end encryption. Single tenant deployments, encrypted at rest, during extraction, and during processing and movement.
• AI plus blockchain. Identity and compute validation via token, with a proof of value approach and plans for zero knowledge proofs in the trust pipeline.
• Ecosystem and monetization. Agent marketplace, developer tools and connectors, and coin for service economy; creators get paid when their tools are used.
• Government traction and policy voice. Active consultations across multiple agencies and planned attendance at a Congressional session on crypto policy and compliance on September 15, with follow up updates promised.
• Roadmap transparency. Closed beta to open beta to GA, backed by a three year roadmap; rewards and bug bounties for testers and developers.
• Learning under privacy constraints. RAG and memory, local fine tuning, federated learning, and offline global refresh to improve agents without centralizing sensitive data.

Next Steps and Follow Ups

• Publish new website and share UI demo clips.
• Release the detailed three year roadmap with beta and GA timelines.
• Share the tokenomics flywheel infographic.
• Post longer form answers and technical blogs on learning methods, token validation, and architecture.
• Provide a debrief after the September 15 Congressional session and policy meetings.
• Host a follow on Spaces focused on software, data, people, and the architecture.

Closing

The moderator thanked Terry and attendees. Additional questions will be addressed in future Spaces and through written posts.