AI smart‑contract code generators refers to the use of code‑specialized generative models and surrounding toolchains to author, complete, translate and review on‑chain logic (smart contracts, wallet integrations, and on‑chain automation). This topic covers the capabilities and limits of models tuned for code, the operational controls needed to keep generated logic safe, and the test and governance practices required before deployment. Relevance (2026): specialized code models and integrated developer assistants are now widely used in blockchain development, making it easier and faster to produce contract code — but also increasing risk of subtle bugs, insecure patterns, gas inefficiencies, and integration errors. Concurrent trends include smaller edge‑ready models for private inference, vendor integrations into IDEs, and multi‑model routing architectures that change how teams manage provenance and security. Key tools and roles: Code Llama and Stable Code are examples of code‑specialized LLMs optimized for generation, completion and fill‑in‑the‑middle tasks; Amazon CodeWhisperer (rolling into Amazon Q Developer) delivers inline suggestions in developer workflows; AskCodi provides an OpenAI‑compatible API layer and model routing for hosting or hybrid deployment; Claude offers conversational developer assistance and analysis; and vendor pages on AI‑integrated wallet development illustrate how teams are coupling ML with on‑chain wallet and dApp work. Practical focus areas: secure pipelines (private or on‑prem inference, model routing, provenance), governance (approval workflows, policy checks), automated verification (static analysis, fuzzing, property‑based and formal tests), and staged simulation (testnets, gas profiling, oracle modeling). Evaluating generators requires measuring correctness, security coverage, reproducibility, and the ability to embed human review gates before any on‑chain deployment.