作者回顾2000年代初期的基因体学革命，指出当前人工智慧在生命科学领域的蓬勃发展与当时的时代精神高度相似，同样伴随著颠覆性技术、大胆的商业愿景、学术界的自我审视，以及法规追赶科技速度的伦理挑战。尽管AI投入的资金规模更为庞大，但AlphaFold在蛋白质结构预测上的突破，证明了深层神经网路处理非线性复杂生物系统的变革性能力，让过去被视为不可能的难题得以被攻克。
然而，科技的迅速进展也伴随著伦理与过度炒作的风险。作者提醒，正如当年基因体学面临隐私、基因专利和优生学风险一样，伦理与监管必须及时跟上现实。目前部分AI领袖宣称五年内治愈所有人类疾病的言论过于狂妄，忽视了许多疾病定义的不完备、人类大脑结构的极端复杂性，以及繁复的临床实证过程，过度夸大的期待一旦落空，将会重创整个科研领域的信誉。（关键数字： five years）
面对浩瀚未知的生命机制，科学界应当保持谦逊，摒弃不切实际的夸张宣传。虽然大型语言模型无法凭空创造新的科学发现，但结合精准的数据集，专用领域AI（如AlphaFold、ChromBPNet等）正实质性地破解生物学的核心难题；未来应透过人类科学家与AI的协同合作，踏实深耕生命本质的探索，共同建构一个更明智且健康的未来。
Reflecting on the genomics revolution of the early 2000s, the author observes a striking parallel in the contemporary surge of artificial intelligence within biological sciences, marked by transformative capabilities, bold commercial ventures, academic soul-searching, and regulatory lag behind scientific progress. Although the scale of capital investment in AI is significantly larger, breakthroughs like AlphaFold's protein structure prediction demonstrate the genuine power of deep neural networks in tackling non-linear, complex biological systems and solving once-impossible challenges.
Nevertheless, this rapid progress raises critical ethical concerns and risks associated with excessive hype. Just as early genomics had to confront genomic privacy, patenting issues, and eugenic risks through responsive frameworks, modern AI requires robust ethical guardrails. The author cautions against hubristic claims by certain AI leaders promising to cure all human diseases within five years, stressing that many conditions remain ill-defined, involve the immense complexity of the human brain, and demand rigorous, labor-intensive clinical validation that cannot be bypassed.
Ultimately, science must remain humble in the face of profound biological mysteries rather than relying on outlandish headlines. While large language models cannot conjure new discoveries out of thin air, specialized domain models such as AlphaFold and ChromBPNet, when paired with high-quality datasets and human expertise, are genuinely unlocking biology's toughest problems; progress lies in rigorous collaborative inquiry to build an informed and healthier future.