1 Generative AI's Act o1: The Dawn of Agent-Based Reasoning

Sequoia Capital,’ the American venture capital firm, recently published an article titled "Generative AI's Act o1: The Reasoning Era Begins." While the piece is extensive, I'll distill its essence for our readers. The central thesis is striking: we have entered the age of reasoning. Just as the mobile era saw the rise of TikTok, Uber, and Meta, there exists a vacuum in the AI application space—one that agents, powered by reasoning capabilities, are poised to fill. I chose to highlight this piece because the domain of "reasoning" has become tangible with GPT-4-o1. (While I've framed the title from a user's perspective as "From Generation to Reasoning," in the context of AI business, "From Learning to Reasoning" might be more precise.)

In the midst of Sequoia's analysis emerges a reference to Kahneman's seminal work "Thinking, Fast and Slow," which introduces the concepts of System 1 and System 2. While a complete reading of this 727-page opus would be ideal, its considerable volume warrants a concise examination of these two fundamental systems.

System 1: Operates on intuition and past experience, handling automatic, rapid responses. While efficient for routine tasks, its reliance on heuristics can lead to errors.
System 2: This system engages in slower, deliberate processing, conducting logical analysis and problem-solving. It manages complex decision-making that requires careful consideration.

For optimal human judgment and decision-making, both systems must work in harmony. Examining Generative AI's evolution through this lens, we see that it primarily operated in a System 1-like manner, producing outputs based on learned patterns—generating images, translating text, summarizing content, creating videos, or interpreting photos. These processes mirror System 1's input-output immediacy. However, with the advent of GPT-o1, we've entered the domain of "reasoning"—a System 2-like capability requiring "thinking about thinking." To demonstrate this evolution, let's examine two test cases. First, consider this query: "How many 'ㅣ' vowels appear in 'Amorepacific Digital Strategy Unit'?" The AI's response is remarkable for its "17 seconds of thinking" approach.

Note that the legacy GPT-4 model provides an incorrect answer when given the same query.
Let's examine one more example. Here's a geography question from the 2024 Korean College Scholastic Ability Test.

While still impressive, the current GPT-4o model incorrectly selects answer 5. It erroneously validates the statement ⓒ, asserting that "Countries (A)(Italy) and (C)(Germany) share a border."

In contrast, the new GPT4-o1 takes 9 seconds to analyze and provide its answer.

This demonstration highlights a crucial aspect of AI's reasoning capabilities: its ability to engage in "deep thinking and creative problem-solving." Sequoia's key observation becomes clear - we're entering an era of "reasoning warfare," where industry giants like OpenAI, Anthropic, Google, and Meta will compete to advance these reasoning capabilities.

This evolution ties into our previous discussion of "Service-as-a-Software" - a paradigm shift in software delivery. Rather than selling software over the internet (the traditional "Software-as-a-Service" model), this “new approach monetizes the actual problem-solving process.”

The concept of "Service-as-a-Software"—essentially selling solutions rather than products—points to a dramatic “market expansion.” While the traditional software market was valued at 650B, the potential market for AI-enabled service solutions reached an estimated 10T.

Sequoia illustrates this transformation through several pioneering Agent applications:

At the end of the analysis, the figure below emerges. The upper-right quadrant of their framework, labeled "AI Apps," promises to be a hotbed of innovation. More fundamentally, this evolution of reasoning and action capabilities marks the dawn of AGI (Artificial General Intelligence), bringing this analysis to its natural conclusion.

Interpreting this framework from my perspective: The left column (Infrastructure/Security/Data/Developer) represents a hierarchical structure. The top "APPS" section, particularly the central "Mobile" zone, shows elements familiar to our current digital landscape. We can anticipate parallel developments in the AI sector as we've witnessed the mobile app industry flourish atop diverse infrastructure. This framework invites us to imagine the possibilities emerging in the AI application space.

Responding to reader requests for practical tools, I'll now share our summary prompt.

2 Summary Prompt: A Practical Tool

Note: This prompt is offered as a starting point, not a definitive solution. Please modify it according to your needs.

My journey with summarization began the day GPT 3.5 launched. The motivation was twofold: the impossibility of consuming vast amounts of information and videos and an impatience with articles that bury their conclusions.

Additionally, I found myself frustrated with ChatGPT's repetitive sentence endings like "해볼게요 (I’ll do it)" and "있습니다 (there is)." This prompt represents a year of refinement based on these requirements (though I suspect there's always room for improvement).

It would be invaluable if we curate and institutionalize the most effective prompts used within our organization. I hope the countless hours of trial and error by Amorepacific team members continue after individual efforts but become shared assets that others can build upon, leading to ever-improving results.

With this aspiration—and hoping readers will share even better approaches—I'll share this prompt as my final contribution as an in-house columnist.