Why Open AI is not Enough

Scaling Law

Scaling Laws for LLMs describe the power-law relationships between model performance and key scaling factors;

We are approaching a critical inflection point where traditional scaling laws are showing signs of stagnation, primarily due to data limitations;

We'll exhaust high-quality human-generated text data between 2025-2030, with median estimates pointing to 2028 if current scaling trends continue. The total effective stock is approximately 4×10¹⁴ tokens

OpenAI's internal struggles with "Orion" (originally intended as GPT-5) exemplify this: the performance gains over GPT-4 were significantly smaller than the GPT-3→GPT-4 jump, with some tasks showing no reliable improvement.

Reference:

Business Case: https://www.businessinsider.com/openai-orion-model-scaling-law-silicon-valley-chatgpt-2024-11

Research Reference: https://arxiv.org/pdf/2211.04325

Impact:

Public vs. Private Data Asymmetry

Public data: ~300T tokens of general knowledge

Private business data: Exponentially larger but inaccessible by the model trainers;

Current scaling laws require ~20 tokens per model parameter for optimal training

Required Model Size for Business Reasoning: ~10^15 parameters

Required Training Data: 20 × 10^15 = 2×10^16 tokens

Available High-Quality Data: ~3×10^14 tokens

Data Gap: 67x more data needed than exists

Why can fine-tuning/distillation not solve the problem?

Information-Theoretic Quality Requirements: Fine-tuning requires exponentially higher data quality than pretraining due to the signal extraction problem

Margin-Based Learning Mathematics: Fine-tuning operates in the low-margin regime where small errors have large impacts

High plasticity: Model can learn new patterns but forgets old ones;

High stability: Model retains old knowledge but can't adapt to new patterns;

In summary, the general capabilities of the pre-trained LLMs is necessary to drive the outcomes in the right direction, while grounding it to the business requirements and rules is also a challenge, solve one does not directly solve the other;

Additionally, cost implications, the amount of technical competency required, etc. are additional factors to consider other than the technical implication;

Why does the above problem exist mathematically and in simple language?

The "Memory Interference" Problem

Think of Your Brain Learning a New Language

Imagine your brain has 100 "memory slots" and you've used 90 of them to learn English. Now you want to learn Chinese:

Total brain capacity: 100 slots

Used for English: 90 slots

Available for Chinese: 10 slots

Problem: Chinese needs 50 slots to be useful

Available capacity: Only 10 slots

Result: Either bad Chinese OR forget English

In neural networks, this is exactly what happens:

Model has billions of parameters (like memory slots)

Most are used for general knowledge (like English)

Business knowledge needs many parameters (like Chinese)

Mathematical constraint: You can't exceed total capacity

The "Tug of War" Mathematics

When fine-tuning tries to learn business knowledge:

simpleGeneral knowledge wants: Parameter = Value A

Business knowledge wants: Parameter = Value B

During training:

Step 1: Move toward A (general knowledge improves, business degrades)

Step 2: Move toward B (business improves, general knowledge degrades)

Result: Model "bounces" between A and B, never settling

Mathematical reality: A ≠ B, so no single value satisfies both

Scaling Law#

Reference:#

Impact:#

Why can fine-tuning/distillation not solve the problem?#

Why does the above problem exist mathematically and in simple language?#

Scaling Law

Reference:

Impact:

Why can fine-tuning/distillation not solve the problem?

Why does the above problem exist mathematically and in simple language?