Biju Dominic is a writer. He says that AI makes people less curious. People are still smart, but they stop asking questions.

Biju Dominic 是一位作家。他認為 AI 讓人們變得不再好奇。人們雖然依然聰明，但他們不再提出問題。

In the past, people worked hard to find information. This hard work made them want to learn more. Now, AI gives answers very fast. People do not need to work, so they lose the drive to learn.

過去人們需要努力尋找資訊。這種努力讓他們更渴望學習。現在 AI 能夠快速提供答案，人們不再需要費力，因此失去了學習的動力。

Young people use AI a lot. They do not practice how to find information. They think the AI knows everything. This is a problem for their minds.

年輕人大量使用 AI。他們不再練習如何尋找資訊，並認為 AI 无所不知。這對他們的心智發展來說是一個問題。

AI uses old data to guess answers. Humans are different. Humans can think of new and strange ideas. The most important human skill is asking a good question. AI cannot do this.

AI 是利用舊數據來推測答案。人類則不同，人類能思考出新穎且奇特想法。人類最重要的技能在於提出一個好問題，而 AI 無法做到這一點。

People might stop learning. They are not less intelligent. They are just not curious anymore.

人們可能會停止學習。這並非因為他們不再聰明，而僅僅是因為他們不再好奇。

Biju Dominic, an author and executive at Fractal Analytics, argues that the rapid growth of Generative AI is causing a decline in human curiosity, rather than a loss of actual mental ability.

Fractal Analytics 的作者兼高層 Biju Dominic 認為，生成式 AI 的快速成長導致人類好奇心下降，而非實際心智能力的喪失。

This issue is explained through the 'Librarian's Paradox,' which suggests that when information is too easy to find, people lose the motivation to learn. In the past, gaining knowledge required effort, which encouraged deeper intellectual engagement. However, Generative AI has removed this effort, making the cost of finding answers almost zero. This leads to 'cognitive satiety,' a state where the drive to learn disappears because information is available everywhere and without effort.

這個問題透過「圖書館員悖論」來解釋，該悖論指出當資訊太容易找到時，人們會失去學習的動力。過去，獲取知識需要付出努力，這鼓勵了更深層的智力參與。然而，生成式 AI 消除了這種努力，使尋找答案的成本幾乎為零。這導致了「認知飽和」，即資訊隨處可得且無需努力，使得學習的驅動力消失。

Dominic emphasizes that this trend is especially clear among young people, who may struggle to find information manually because they rely on automated answers. He distinguishes between the ability to think and the desire to ask questions, noting that the latter is weakening. To fix this, he suggests that we must intentionally cultivate a feeling of 'not knowing' to encourage deeper exploration.

Dominic 強調這一趨勢在年輕人之中尤為明顯，他們可能因為依賴自動化答案而難以手動尋找資訊。他區分了思考能力與提出問題的慾望，並指出後者正在減弱。為了修正這一點，他建議我們必須刻意培養一種「不知道」的感覺，以鼓勵更深層的探索。

Furthermore, the report highlights a major difference between how machines and humans work. Generative AI functions by predicting outcomes based on existing data. In contrast, human innovation comes from exploring unlikely and creative ideas. Dominic argues that the true value of human intelligence is not in providing answers, but in asking critical questions—a skill that AI cannot currently replicate.

此外，該報告強調了機器與人類運作方式的主要區別。生成式 AI 透過預測基於現有數據的結果來運作。相反，人類的創新來自於探索不尋常且具創造性的想法。Dominic 認為人類智能的真正價值不在於提供答案，而是在於提出批判性問題——這是 AI 目前無法複製的技能。

The current trend suggests a risk that humans may stop seeking knowledge, not because they lack intelligence, but because their natural curiosity is being eroded.

目前的趨勢顯示，人類可能會停止追求知識，並非因為缺乏智能，而是因為天生的好奇心正在被侵蝕。

Biju Dominic, an author and executive at Fractal Analytics, posits that the proliferation of Generative AI is precipitating a decline in human curiosity rather than a loss of innate cognitive ability.

Fractal Analytics 的作者兼高階主管 Biju Dominic 認為，生成式 AI 的普及導致的是人類好奇心的下降，而非天生認知能力的喪失。

The phenomenon is articulated through the 'Librarian's Paradox,' wherein the unrestricted accessibility of information correlates with a decrease in the motivation to acquire it. Historically, the acquisition of knowledge necessitated a degree of effort—or 'friction'—which served as a catalyst for intellectual engagement. The advent of Generative AI has effectively neutralized this friction, reducing the economic and temporal cost of obtaining answers to near zero. This state, termed 'cognitive satiety,' suggests that when information is omnipresent and effortless to retrieve, the drive to possess such knowledge diminishes.

此現象透過「圖書管理員悖論」來闡述，即資訊的獲取越不受限制，獲取資訊的動力反而越低。在歷史上，獲取知識需要一定程度的努力——或稱作「摩擦力」——而這正是激發理智參與的催化劑。生成式 AI 的出現有效地抵消了這種摩擦力，將獲取答案的經濟與時間成本降低至趨近於零。這種狀態被稱為「認知飽足感」，意指當資訊無處不在且獲取毫不費力時，追求掌握該知識的驅動力便會減弱。

Dominic asserts that this trend is particularly evident in younger cohorts, who may exhibit diminished proficiency in information retrieval due to a reliance on the perceived ubiquity of automated answers. He distinguishes between the capacity for cognition and the drive for inquiry, arguing that the latter is currently atrophying. To counteract this, Dominic references Stuart Firestein's 'culture of ignorance,' suggesting that the intentional cultivation of a sense of intellectual deficiency is necessary to stimulate deeper inquiry.

Dominic 主張這一趨勢在年輕群體中尤為明顯，由於依賴於自動化答案的普及，他們在資訊檢索方面的能力可能會有所下降。他區分了「認知能力」與「探究驅動力」，並認為後者目前正在萎縮。為了 counteract 這一現象，Dominic 引用了 Stuart Firestein 的「無知文化」，建議有意識地培養一種理智上的匱乏感，以激發更深層次的探究。

Furthermore, the report highlights a structural divergence between machine processing and human creativity. Generative AI operates as a probabilistic mechanism, predicting outcomes based on historical data distributions. Conversely, human innovation is characterized by the exploration of improbable extremes. Drawing a parallel to David Hilbert's 1900 presentation of 23 unresolved mathematical problems, Dominic argues that the primary value of human intellect lies not in the provision of answers, but in the formulation of critical questions—a function that remains beyond the current capabilities of artificial intelligence.

此外，該報告強調了機器處理與人類創造力之間的結構性分歧。生成式 AI 作為一種概率機制，根據歷史數據分佈來預測結果。相反，人類創新的特徵在於對低概率極端情況的探索。Dominic 以 David Hilbert 於 1900 年提出的 23 個未解決數學問題為類比，認為人類理智的主要價值不在於提供答案，而是在於提出關鍵問題——而這項功能目前仍超出人工智慧的能力範圍。

The current trajectory suggests a risk where humanity may cease the pursuit of knowledge, not due to a lack of intelligence, but due to a systemic erosion of curiosity.

目前的趨勢顯示出一種風險，即人類可能會停止追求知識，這並非因為缺乏智能，而是因為好奇心遭到了系統性的侵蝕。