Juza💧🐬 🦭/acc 📘
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@5ChariotStars
Crypto Enthusiast since 2021｜ SUI BagHolder 💧｜
WaterX Genesis Campaign powered by @GalxeQuest Complete light quests and claim your WaterX Genesis #GalxeOAT: app.galxe.com/quest/WaterX/G… 🔹 May 4th 14:00 UTC - May 11th 14:00 UTC 🔹 WaterX Genesis #GalxeOAT + @Galxe Space points 🔹 Refer to earn more point 🔹 Any form of cheating or malicious behavior will result in disqualification
zkSummit14 is happening in Rome on May 7th! Once again, we bring together the researchers, cryptographers, and builders in ZK for a day to catchup on the most cutting edge ideas in our space. We are about to sell out, so apply & get your ticket asap! zksummit.com
Trust Is Not Enough: Data Must Be Verifiable in the Age of AI Enterprise adoption of AI is accelerating. From automating customer support with chatbots to streamlining workflows through document summarization, AI-driven efficiency gains are already reshaping how businesses operate. But the way we use AI is fundamentally shifting. We are moving from “AI that answers questions” to “AI that reasons over data and acts autonomously” — in other words, toward AI agents. Chatbot-style use cases only scale so far. The real challenge now is how deeply AI can be embedded into actual business operations. What AI-Era Systems Demand from Data What does it actually mean to integrate AI into operations? Simply feeding Slack messages or PDF documents into an AI system is not sufficient for sustained, reliable execution. These formats were designed for human consumption, not machine-driven action. They need to be transformed into structured formats that AI systems can process. But structure alone is not enough. If AI is expected to execute tasks autonomously, without human oversight, the data it relies on must satisfy three critical properties: - Existence — the data can be proven to exist - Integrity — the data has not been tampered with - Availability — the data remains continuously accessible Traditionally, these properties have been guaranteed by trusting the data provider or storage system. @WalrusProtocol takes a different approach: instead of relying on trust in a specific entity, it makes these guarantees cryptographically provable and independently verifiable. Why Verifiability Becomes Necessary In multi-agent environments or systems involving multiple independent parties, trust cannot be assumed to hold across all parties and systems. In high-stakes domains, “trusted data” is not enough. Only verifiable data enables reliable long-term operation. The reason is structural: AI systems operate at high speed and frequency. Even small inconsistencies in data can cascade through automated decisions, amplifying errors and leading to significant losses. That said, verifiability does not prevent AI from making mistakes. Models can still misinterpret data and take incorrect actions. Safeguards and guardrails are necessary, but they are inherently limited. Errors and unexpected situations are inevitable. In systems involving critical decisions, what matters is not just avoiding mistakes, but having the ability to verify and reconstruct what happened after an incident. Because ultimately, responsibility does not lie with the AI — it lies with the humans and organizations deploying it. That requires reproducibility of the decision process. Verifiability as the Foundation for Accountability AI is especially well-suited for domains like finance, healthcare, and law, where large volumes of data must be processed. At the same time, these domains require auditability and accountability when something goes wrong. AI cannot take responsibility. If verification depends on mutable logs, trusted cloud infrastructure, or centralized databases, accountability becomes impossible to establish. In a world where AI systems make decisions and take actions, it becomes a prerequisite that the responsible party can reproduce and verify those decisions. Traditional models that rely on trusting a single system or organization are insufficient to guarantee this level of reproducibility and verifiability. That is why data must be verifiable. @WalrusProtocol provides the cryptographic foundation to make that possible.
