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129 lines
5.1 KiB
TeX
129 lines
5.1 KiB
TeX
\section{Conclusion}
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\label{sec:conclusion}
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% ============================================================================
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The AI cloud industry is built on a broken trust model. Users send their most
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sensitive data to providers who promise---but cannot prove---that they will not
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read, store, or misuse it. This trust model fails under adversarial conditions
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(breaches, subpoenas, rogue employees) and is incompatible with regulated
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industries that require cryptographic guarantees.
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Hanzo AI Cloud replaces trust with proof:
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\begin{enumerate}[leftmargin=1.4em]
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\item \textbf{Confidential inference}: CKKS FHE ensures the provider
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evaluates models on encrypted inputs. The provider never sees the
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plaintext. This is a mathematical guarantee, not a policy.
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\item \textbf{Agent sovereignty}: Each agent's vault, memory, and identity
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are cryptographically isolated. The platform cannot access agent state
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without the agent's keys.
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\item \textbf{Verifiable serving}: Every inference result carries a
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receipt anchored to the Lux A-Chain, proving provenance. Regulators,
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auditors, and users can verify exactly which model produced which output.
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\item \textbf{Decentralized provision}: No single provider controls the
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infrastructure. Stake-weighted selection and on-chain SLA enforcement
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align provider incentives with user outcomes.
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\item \textbf{Edge-to-cloud continuum}: The same model format, the same
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attestation, the same CRDT sync protocol work from a phone to a data
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center. Inference happens where it makes sense.
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\end{enumerate}
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The era of ``trust us, we won't look at your data'' is ending. The era of
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``we cannot look at your data'' is beginning. Hanzo AI Cloud is the
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infrastructure for the latter.
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\paragraph{Settlement on the Hanzo AI Chain (AIVM).}
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The confidential-inference, agent-hosting, and model-serving primitives
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described in this paper settle on the Hanzo AI Chain. The AI Chain is
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the Lux unified A-Chain (LP-134) re-framed as the AI Virtual Machine,
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secured by Quasar 3.0 three-lane post-quantum certificates (LP-105),
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executed on QuasarGPU (LP-132), and scheduled per QuasarSTM lanes
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(LP-010). It mints \textbf{AI}, the native coin of the Hanzo Network,
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and hosts the on-chain HMM registry, agent registry, and three composable
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compute markets (inference, training, GPU pool). Confidential inference
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is delegated to F-Chain (LP-013, LP-067) through the \texttt{F\_CALL}
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precompile; threshold custody for model weights is delegated to M-Chain.
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See the companion paper \emph{Hanzo AI Chain (AIVM)}
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(\texttt{\textasciitilde/work/hanzo/papers/hanzo-ai-chain/}); activation
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is 25 December 2025 alongside Lux Quasar 3.0.
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% ============================================================================
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% REFERENCES
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% ============================================================================
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\begin{thebibliography}{99}
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\bibitem{hanzocandle}
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Z.~Kelling, D.~Wei, and M.~Chen.
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\newblock Hanzo Candle: A {Rust} {ML} framework for high-performance inference.
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\newblock \emph{Hanzo AI Research}, August 2024.
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\bibitem{hanzofhe}
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Z.~Kelling.
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\newblock Hanzo {FHE} Inference: Fully homomorphic encryption for confidential
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{AI} inference.
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\newblock \emph{Hanzo AI Research}, April 2026.
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\bibitem{hanzojin}
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Z.~Kelling.
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\newblock Jin: Unified multimodal {AI} architecture for vision-language, audio,
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and cross-modal understanding.
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\newblock \emph{Hanzo AI Research}, April 2023.
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\bibitem{hanzoedge}
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Z.~Kelling.
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\newblock Hanzo Edge: On-device {AI} inference for mobile and {IoT} deployments.
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\newblock \emph{Hanzo AI Research}, July 2024.
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\bibitem{hanzobaseruntime}
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Z.~Kelling.
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\newblock Hanzo Base: The single-binary runtime for sovereign applications.
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\newblock \emph{Hanzo AI Research}, April 2026.
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\bibitem{luxwhitepaper}
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{Lux Industries Inc}.
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\newblock Lux: A scalable multi-consensus blockchain with post-quantum
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cryptography.
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\newblock \emph{Lux Network Technical Report}, 2024.
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\bibitem{hanzoplatform}
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M.~Chen, D.~Wei, and Z.~Kelling.
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\newblock Hanzo Platform: {PaaS} for {AI}-native application deployment.
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\newblock \emph{Hanzo AI Research}, December 2021.
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\bibitem{ckks2017}
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J.~H.~Cheon, A.~Kim, M.~Kim, and Y.~Song.
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\newblock Homomorphic encryption for arithmetic of approximate numbers.
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\newblock In \emph{ASIACRYPT}, Springer, 2017.
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\bibitem{openaiapi}
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{OpenAI}.
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\newblock {API} data usage policies.
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\newblock \url{https://openai.com/policies/api-data-usage}, 2024.
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\bibitem{gentry2009}
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C.~Gentry.
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\newblock Fully homomorphic encryption using ideal lattices.
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\newblock In \emph{STOC}, ACM, 2009.
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\bibitem{brakerski2014}
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Z.~Brakerski, C.~Gentry, and V.~Vaikuntanathan.
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\newblock ({Leveled}) Fully homomorphic encryption without bootstrapping.
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\newblock \emph{ACM Transactions on Computation Theory}, 6(3):1--36, 2014.
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\bibitem{gilad2016}
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R.~Gilad-Bachrach, N.~Dowlin, K.~Laine, K.~Lauter, M.~Naehrig, and
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J.~Wernsing.
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\newblock {CryptoNets}: Applying neural networks to encrypted data with high
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throughput and accuracy.
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\newblock In \emph{ICML}, 2016.
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\bibitem{luxagent}
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Z.~Kelling.
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\newblock Lux Agent Sovereignty: Decentralized identity and capability
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management for autonomous agents.
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\newblock \emph{Lux Network Technical Report}, 2025.
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\end{thebibliography}
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