As AI coding agents grow more capable, developers tend to trust them more — but this growing trust may not be warranted. The risks haven't disappeared; they've scaled up. More capable agents connected to more tools mean higher potential damage when things go wrong. Neural networks are fundamentally black boxes, jailbreaking is an inherent LLM limitation rather than a fixable bug, and inference optimizations can subtly alter model behavior. Open-weight models from foreign entities introduce additional supply chain concerns. The takeaway: don't blindly merge AI-generated code, sandbox your agents properly, and treat AI safety as an engineering discipline rather than an assumption.
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