Preparing AI for the Quantum Era. Verona x PQabelian, Both approach post-quantum security from different directions.
Quantum computers could eventually change how digital security works today. Some encryption methods that protect our identities, financial records, and transactions may no longer be strong enough when quantum computing advances.
One concern is Harvest Now, Decrypt Later, encrypted data collected today could be stored and decrypted years from now. That's a serious problem for information that needs to remain private for a long time.
Verona focuses on making information trustworthy.
Through its Truth Engine, information can be verified at its source and turned into reusable proofs. AI agents can use those proofs without accessing the personal data behind them. Its Fermi upgrade also introduces post-quantum ML-DSA authentication based on the NIST standard.
Abelian focuses on keeping transactions private.
Its blockchain has used lattice-based cryptography since its first block in April 2022. Users can also choose how much transaction information they reveal, including sharing it with an auditor they trust.
These two roles become particularly relevant as AI agents start handling more than conversations.
Imagine an agent checking someone's identity or financial eligibility before making a payment. It needs reliable proof to make that decision, but it doesn't need access to someone's entire financial history.
Verona helps establish what is true, while Abelian helps keep the transaction private.
That's an important distinction for the future of AI. As agents gain more responsibility to make decisions and move money, protecting the information behind those actions becomes just as important as the actions themselves.
VERONA IN OCTOBER: BUILDING THE VERIFIED AI ECOSYSTEM
October brought several interesting developments around Verona, and the projects involved are building connections across very different parts of the AI landscape. Some focus on how AI agents identify themselves, others on what they can remember, how they manage money, or where they get reliable information to make decisions. Here's a closer look at the collaborations: Brale Provides the regulated stablecoin infrastructure behind verUSD, including 1:1 USD reserve backing and native issuance across supported chains. This gives AI agents a dollar-based asset to pay for verified data and settle transactions. Billions Adds verified agent identity by linking an AI agent to a real, verified human. Verona helps establish whether the facts an agent relies on are true, while Billions helps establish whose agent it is and who stands behind its actions. Zeta Chain & AnumaAI Working with Verona on research into what people want AI to remember and what should remain private. The exploration also looks at how Anuma's encrypted, user-controlled memory can complement Verona's verified context, giving AI a way to use relevant information without turning personal data into something openly accessible. Alphio Brings another use case into financial decision-making. Through risk rules and cross-portfolio context, the integration explores how AI trading agents can work with more meaningful information instead of relying only on market signals. Axis Robotics Takes the conversation beyond software and into Physical AI. With more than 270,000 contributors and 7 million recordings, Axis is building a large foundation of real-world robotics data. Verona's verification layer adds another important dimension: establishing the provenance and trustworthiness of the data used to train intelligent machines. Stockcoin AI Connects AI-powered trading with verified personal context across US, Hong Kong, and Korean stock markets, alongside stock perpetuals and IPO/pre-IPO opportunities. Verona can help prove relevant information, such as allocation eligibility or asset ownership, without requiring users to expose their underlying financial data. Looking across these projects, the interesting part isn't simply the number of collaborations. It's how different pieces are beginning to connect. Money needs reliable settlement. Agents need identity and private memory. Trading systems need trustworthy context. Physical AI needs data with a verifiable origin. These are different problems, but they point toward a common requirement: AI needs more than intelligence to operate reliably. It needs information that can be trusted. Verona making real-world facts verifiable, reusable, and accessible to AI without giving away the private data behind them. #Aİ