尽管人工智慧在数学难题和软体编程等领域取得了突破性进展，引发了对其将迅速解决所有难题甚至掌控一切的乐观或恐慌预期，但这种指数级增长的信念建立在「只要具备足够智力即可解决所有问题」的未经证实假设之上。

智力的力量高度依赖于反馈的速度与清晰度；在数学、代码和气象预测等具备即时验证机制的领域，模型能持续修正与精进，然而在生物学和癌症研究等领域，因需要漫长且真实的活体实验反馈，纯粹的智力优势无法跨越现实世界的验证限制。

如同曼哈顿计划中科学家仅占整体资源的一小部分，智力并无法取代实体基础设施、供应链或实验数据，根据阿姆达尔定律，单纯加速思考只会将瓶颈转移至其他实体限制，因此大众应理性看待人工智慧的极端预测，转而聚焦于现实中的关键瓶颈。

While artificial intelligence has achieved breakthrough successes in areas like complex mathematics and software engineering, sparking widespread utopian or apocalyptic forecasts, this vision of exponential growth relies heavily on the unverified assumption that intelligence alone is sufficient to solve every challenge.

The effectiveness of intelligence fundamentally depends on rapid and precise feedback; while domains like coding and math thrive on immediate validation, fields such as cancer research require multi-year real-world biological experiments, creating physical latency that sheer computational intelligence cannot bypass.

Much like the Manhattan Project where theoretical physicists represented only a fraction of the total effort, pure intellect cannot substitute for physical infrastructure, supply chains, or empirical data; accelerating computation merely shifts bottlenecks to physical constraints, meaning society should temper extreme AI projections and focus on real-world limitations.