The threat extends beyond accidental errors. When AI writes the software, the attack surface shifts: an adversary who can poison training data or compromise the model’s API can inject subtle vulnerabilities into every system that AI touches. These are not hypothetical risks. Supply chain attacks are already among the most damaging in cybersecurity, and AI-generated code creates a new supply chain at a scale that did not previously exist. Traditional code review cannot reliably detect deliberately subtle vulnerabilities, and a determined adversary can study the test suite and plant bugs specifically designed to evade it. A formal specification is the defense: it defines what “correct” means independently of the AI that produced the code. When something breaks, you know exactly which assumption failed, and so does the auditor.
由于表面粗糙、精度不足等问题，金属件打印完成后须经历一系列复杂后处理流程，其中成本主要集中在打磨、抛光等表面精加工流程。更值得关注的是，目前大量后处理环节仍依赖人工操作。这不仅意味着高昂的人力成本，还存在重复性劳动导致的人员疲劳、效率波动以及质量一致性难以保证等问题。
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Medium-level (typed ops)。91视频对此有专业解读
The August 2025 study was built on an unusually powerful dataset—high-frequency payroll records from millions of American workers generated by ADP, the largest payroll software firm in the country. What Brynjolfsson and his co-authors found was striking: Since the widespread adoption of generative AI tools beginning in late 2022, employment for early-career workers in the most AI-exposed occupations fell by 13% on a relative basis, even after controlling for broader firm-level disruptions. Older, more experienced workers in the same fields, meanwhile, saw their employment hold steady or grow.