The Three AI Scaling Laws: From Pre-Training to Test-Time

Understanding the Evolution of AI Scaling

AI development is transitioning through three distinct scaling paradigms, each with different resource requirements and capability profiles.

Pre-Training Scaling (System 1 – Fast Thinking)

The original scaling law: more data, more compute, more parameters = better models.

Characteristics:

• Pattern recognition
• Intuitive responses
• Human-in-the-loop

Current status: Hitting diminishing returns (0.5-1% improvement per doubling).

Post-Training Scaling (Beginning System 2 – Reasoning)

The current frontier: Better training signals through reinforcement learning.

Key components:

• F – Fine-tuning
• R – RLHF (Reinforcement Learning from Human Feedback)
• A – Architecture improvements

Capabilities:

• Logical analysis
• Step-by-step problem solving
• Evidence evaluation

“We are here” – The industry is actively investing billions in this phase.

Test-Time Scaling (System 2 – Deep Thinking)

The emerging frontier: Inference compute for complex reasoning.

Key components:

• I – Iteration
• M – Multi-step reasoning
• V – Verification

Target capabilities:

• Complex problems
• Autonomous tasks
• Human-looped-in AI
• PhD-level research

“Getting there fast” – This is where frontier AI is heading.

The Strategic Implication

Understanding which scaling law dominates determines where to invest. Pre-training returns are diminishing. Post-training (RL) is the current high-ROI frontier. Test-time scaling is the emerging opportunity.

This is part of a comprehensive analysis. Read the full analysis on The Business Engineer.