Have you ever wondered, "Has the speed of AI model development slowed down?" (Problem) However, experts in the field unanimously state that we are not in a plateau, but at the beginning of a new S-curve called 'Inference Scaling'. (Solution) Based on the latest analysis from the Singularity community and insights from experts, we deeply analyze the drastic changes that will unfold within the next 1-2 years. (Evidence)

From Training to Inference: A Paradigm Shift

Until now, AI development has focused primarily on increasing the scale of 'Pre-training'. More data, more GPUs, and longer training times were the keys to performance improvement. But now, the rules of the game are changing.

The Misconception that "Model Development is Over"

Many people talk about the limits of AI, seeing that no groundbreaking model has emerged since GPT 5.2. But this is seeing only the tip of the iceberg. As OpenAI's o1 model recently demonstrated, it has been proven that performance can be dramatically improved by increasing the 'Test-time Compute'—the time the model spends thinking before answering.

A user in the community quotes a related video, stating:

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"Inference is in a very early stage, and there will be a sharp rising curve within 1-2 years."

This implies that the competition is shifting from simply increasing model parameters to how deeply we can make the model think to solve problems.

Why is 'Inference Scaling' Important?

The changes brought by inference scaling are not just about "slightly higher accuracy."

1. Solution to Data Scarcity

High-quality text data on the internet is running out. However, the 'Chain of Thought' data generated during the inference process can be created infinitely. Through this, models can learn on their own (Self-play) and become smarter. The way AlphaGo reached a god-like level by playing Go against itself without human records is now being applied to LLMs (Large Language Models).

2. Evolution from System 1 to System 2

• System 1 (Intuition): Current chatbots that react instantly to questions (e.g., GPT 5.2).
• System 2 (Deliberation): A method of finding answers by breaking down complex problems, planning, and verifying (e.g., OpenAI o1).

The next 1-2 years will be a period where this 'System 2' thinking capability evolves dramatically. This will produce results that surpass human experts in fields requiring logical reasoning, such as coding, mathematics, and scientific research.

Common Mistake: "Is Bigger Model Always Better?"

Many developers and companies are still stuck in the stereotype that "a bigger model is unconditionally better."

Failure Case: Unconditional Fine-tuning

Company A attempted to fine-tune a massive model from scratch to inject specific domain knowledge. They spent hundreds of thousands of dollars, but the result was worse than attaching well-designed prompt engineering and RAG (Retrieval-Augmented Generation) to a base model.

Why did it fail? The strength of the latest models lies in 'reasoning capability,' not 'knowledge memorization.' Forcing knowledge injection damaged the model's general reasoning ability (Catastrophic Forgetting).

The Correct Approach: Securing Inference Time

Instead, you should ask the model to "think slowly and answer" or introduce an 'Agentic Workflow' that handles complex problems in multiple steps. Even a small model can produce better results than a large model's short answer if given enough inference time.

What Should Developers Prepare for in the Next 1-2 Years?

👉 Action Plan (Do It Now)

1. Deepen Prompt Engineering: Master 'Chain of Thought' prompting, designing the model's thinking process rather than just giving simple instructions.
2. Adopt Agent Frameworks: Use LangChain, LangGraph, etc., to create loops where the model uses tools and verifies itself.
3. Redesign Cost Structure: Shift your perspective from 'cost per token' to 'cost per problem solved.' While API costs increase with longer inference times, the value gained from solving complex problems is much greater.

FAQ: Questions About Inference Scaling

Q1. Won't chatbots be too slow if inference time increases?

A. Correct. It may not be suitable for real-time conversational services. However, for asynchronous tasks (Async Task) where 'accuracy' is more important than 'instant answers'—such as coding, legal review, and report writing—a wait of a few minutes holds sufficient value. The User Experience (UX) should also change to show progress, like "Analyzing... Planning..." instead of just "Answering...".

Q2. Will open-source models follow this trend?

A. Yes, the open-source community, including DeepSeek and Llama, is already releasing models with enhanced reasoning capabilities. Even in local environments with hardware constraints, the combination of 'Small Model + Long Inference Time' will demonstrate powerful performance.

Q3. What is the status of GPT 5.2?

A. GPT 5.2 and GPT 5.2.2 have already been released, and they have evolved into forms where "reasoning capability" is internalized, rather than just having "lots of training data."

The development of AI is not over. Rather, we have just entered the era of 'Thinking AI.' Are you ready to ride this sharp rising curve?