Physical Boundaries of Scaling Laws and New Moore's Law: The Ultimate Deduction of AI Scaling

Industry Observation Cover
Industry Observation Cover

Preface:
OpenAI's Scaling Laws have been the bible of AI development for the past five years: More data, more compute, more parameters equal stronger models.
But by 2025, this bible seems to face challenges.
With the popularization of trillion-parameter models, we hit three walls: Energy Wall, Data Wall, and Cognitive Wall.

Is the road of AI scaling at an end? Or are we brewing the next greater leap? This article deduces the ultimate future of AI from physics, information theory, and economics.

Chapter 1: Energy Wall: When AI Drinks the Grid Dry

Sam Altman once said: "Future currency is compute, and the essence of compute is energy." In 2025, this became a realistic crisis.

1.1 Limit of Joules/Token

  • Status: Training a GPT-5 level model consumes electricity equivalent to a small city's annual usage.
  • Bottleneck: Transformer capacity cannot keep up with GPU deployment speed. Many data centers leave H100s idle because they can't get enough power quota.

1.2 Two-way Rush of Nuclear Fusion and AI

This sounds like sci-fi, but it's happening.

  • Helion Energy: A nuclear fusion company invested by OpenAI, promising to provide unlimited clean energy for data centers by 2028.
  • AI Optimizing Fusion: DeepMind's reinforcement learning algorithm successfully controlled plasma magnetic fields in Tokamak devices, extending fusion reaction time by 30%.
  • Conclusion: AI creates energy crises, AI also solves energy crises. This is the dialectic of technological development.

Chapter 2: Data Wall: When the Internet is "Eaten Empty"

All high-quality text produced in human history (books, papers, code, news) is about 10-20 trillion Tokens.
By end of 2024, state-of-the-art models had already learned this data once.
Data Exhaustion is the biggest panic of 2025.

2.1 Salvation by Synthetic Data

If human data is insufficient, let AI create it.

  • Textbook-level Data Generation: Let a powerful Teacher model (like GPT-5) write high-quality physics problems, coding problems, logic reasoning problems according to the syllabus.
  • Self-Play: Similar to AlphaZero playing Go. Let the model debate with itself, write code and test it itself. Through this internal loop, the model can self-evolve without external new data.

2.2 Dimensionality Reduction of Multimodal Data

Although text is exhausted, video data is nearly infinite.

  • Video-to-Text: Future model training will rely more on videos from YouTube and TikTok. Learning physical laws, human behavior, and social common sense by letting the model "watch" videos has far higher information density than pure text.

Chapter 3: Cognitive Wall: Asymptote Approaching Human Intelligence

Can piling parameters really stack up to AGI (Artificial General Intelligence)?
Academia in 2025 diverged.

3.1 Diminishing Marginal Utility

Experiments show that from 100B to 1T parameters, model capability improves significantly; but from 1T to 10T, the improvement slows down.
This is much like the late stage of Moore's Law in chips. We invested exponentially growing costs but only got linear performance improvements.

3.2 Lack of System 2 Thinking

Current LLMs are essentially System 1 (Fast Thinking): Probability prediction based on intuition.
It lacks System 2 (Slow Thinking): Planning capability based on logic and multi-step deduction.

  • Next Generation Architecture: Not just pure Transformer, but LLM + Search + Planning.
  • Q / AlphaZero Paradigm*: Introducing Search Tree in the inference phase, letting the model "simulate" various possible paths in its "mind" before answering, choosing the optimal solution. This is no longer the scope of Scaling Laws, but a revolution in algorithm paradigm.

Chapter 4: Endgame Deduction: Future Form of Silicon-based Intelligence

Based on the above analysis, we make an ultimate deduction for the AI form in 2030:

  1. Hierarchical Intelligent Network:

    • Top layer: A few Super Intelligences (Nuclear-powered, Trillion parameters, System 2 capable), responsible for scientific discovery and strategic decision-making.
    • Bottom layer: Countless Utility Models (Edge-running, Billion parameters, Extremely low energy), responsible for handling human daily chores.

  2. Human-Machine Symbiotic Evolution:

    • AI won't replace humans but becomes the human exocortex.
    • Breakthroughs in BCI (Brain-Computer Interface) will directly interconnect human brains with cloud AI, achieving million-fold bandwidth increase.

Conclusion

Scaling Laws might have physical boundaries, but the evolution of intelligence has no boundaries.
When we hit a wall, it often means a new door is right next to it.
From the Age of Discovery to the Electrical Age, and now to the Intelligent Age, humanity always finds new ways out through technological explosions amidst anxiety of resource scarcity.
This time will be no exception.

This document is written by the Strategy Planning Group of the Augmunt Institute for Frontier Technology.