Deep Research: How "Slow Thinking" is Transforming AI

Introduction

In recent years, artificial intelligence (AI) has rapidly permeated various professional domains, becoming an indispensable tool for knowledge workers. As more professionals seek to deepen their analytical capabilities and gain strategic insights, AI-powered features are evolving beyond simple task automation. One breakthrough example is Deep Research—a capability that has quickly become a defining milestone in the AI era. By enabling machines to think more like human analysts, this tool is not only enhancing productivity but also reshaping how people interact with and extract value from AI.

The Emergence and Evolution of Deep Research

In February 2025, OpenAI introduced the Deep Research feature within ChatGPT. This tool enables AI agents to autonomously gather information, perform reasoning, and synthesize data, culminating in high-quality research reports. Initially exclusive to Pro users at $200 per month, Deep Research quickly garnered acclaim for its output quality, comparable to that of graduate-level research. Recognizing the demand, OpenAI expanded access to all paying users, offering ten queries per month.

Other AI leaders have introduced similar functionalities. Google integrated a Deep Research capability into its Gemini model in late 2024, though its performance was inconsistent. xAI, founded by Elon Musk, launched Grok-3 in February 2025, offering "deep search" without usage limits until server capacity is reached. Perplexity also offers a similar function, granting free members five Deep Research tasks per day.

Deep Research and the Concept of "Slow Thinking"

Nobel laureate Daniel Kahneman, in his book Thinking, Fast and Slow, delineates two modes of thought: System 1, which is fast, intuitive, and prone to errors, and System 2, which is slow, deliberate, and analytical. Deep Research aligns with System 2, mirroring human structured, logical reasoning over time.

Traditional AI development focused on scaling laws, linking model performance to parameters, training data, and computational power—all of which are costly. The new frontier lies in inference-time computation, allowing models to think step-by-step during output generation. This shift from rapid responses to strategic slow thinking marks a pivotal evolution in AI.

Applications Across Industries

Deep Research represents a methodological shift in AI problem-solving. By emulating human inquiry—from problem identification to iterative analysis—Deep Research facilitates deeper insights and practical applications.

Accessing multiple data sources and cross-verifying information reduces inaccuracies and enhances report reliability. Consequently, Deep Research has the potential to disrupt various industries: in human resources, it can enhance background verification processes; in venture capital, it can provide detailed evaluations of startup teams; and in finance, it may revolutionize KYC (Know Your Customer) and credit assessment workflows.

Conclusion

Intelligence invariably comes at a cost—traditionally through computational power, and now, perhaps, through time. As AI matures, our willingness to await more accurate, in-depth answers could unlock its true potential. Embracing thoughtful reflection in the age of automation may lead to unprecedented advancements.

Keywords: Deep Research, AI, Slow Thinking, OpenAI, ChatGPT

References

• Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
• The Guardian. (2025, February 3). OpenAI launches 'deep research' tool that it says can match research analyst. https://www.theguardian.com/technology/2025/feb/03/openai-deep-research-agent-chatgpt-deepseek
• 經理人月刊. (2024, March 21). Deep research 是什麼？花時間蒐集資料、推理精練、緩解幻覺，是屬於 AI 的「慢思考」. https://www.managertoday.com.tw/columns/view/70076

繁體中文摘要

近年來，人工智慧（AI）迅速滲透至各大專業領域，成為知識型工作者的重要工具。隨著應用愈趨多元，用戶對 AI 的期待也越來越高，然而仍有不少人對於付費使用 AI 持保留態度，遲遲未見到足以改變生產力的關鍵功能。直到 2025 年，OpenAI 在 ChatGPT 中推出名為 深度研究（Deep Research） 的功能，引起業界高度關注，成為許多知識工作者首次主動訂閱 AI 工具的誘因。

深度研究 模擬人類進行高階思辨任務的方式，透過多階段的任務分解、資料蒐集與整合推理，產出品質極高的研究報告，甚至被譽為可達碩博士等級。這項功能的核心思維呼應了諾貝爾經濟學獎得主丹尼爾·卡尼曼（Daniel Kahneman）所提出的《快思慢想》理論，其中強調「系統二」——即 慢想（Slow Thinking），是一種經過邏輯推理、深思熟慮且較為正確的思維模式。AI 模型過去主要仰賴大量參數與資料堆疊換取快速反應，但如今的發展趨勢已逐漸轉向讓 AI 在推理階段中投入更多「時間」，模擬人類的反覆驗證與邏輯分析過程，這不僅提升了答案的精確度，也大幅降低了幻覺錯誤的發生率。

AI 的「慢想」能力，不僅僅是效率的轉變，更是一種智能品質的提昇。透過這種方式，深度研究已開始應用於多個專業場景，包括人資背景查核、創投評估創業團隊、銀行授信徵信流程等。這種 AI 與人類認知策略融合的模式，將會重塑知識型產業的工作流程與決策品質。與其要求 AI 即刻給出答案，現代用戶反而更願意等待 AI「慢慢想」，以換取真正值得參考的洞見。我們或許也該放慢腳步，與 AI 一起，重新學會深度思考的價值。