Does Scaling Law Still Hold? Analyzing OpenAI’s 12‑Day Mini Releases and the Future of GPT‑5

OpenAI’s 12‑day mini‑series introduced the o1 model on day 1 and a Reinforcement Fine‑Tuning (RFT) capability on day 2, illustrating a rapid shift from pre‑training to inference‑time reasoning and hinting at a possible early GPT‑5 release.

The piece then asks whether the scaling law that links model size to performance still applies, drawing on Epoch AI’s report “Can AI Scaling Continue Through 2030?” to assess the outlook.

Four key constraints are identified:

Power constraints – massive electricity demand outpaces current grid capacity; solutions discussed include building new gas or solar plants, nuclear projects, and geographically diversifying power sources such as Canada or the UAE.

Chip manufacturing capacity – GPU supply, high‑bandwidth memory (HBM) production, and advanced packaging (CoWoS) limit training compute; the report cites TSMC’s Advanced Backend Fab 6 and projected CoWoS expansion as potential relief.

Data scarcity – high‑quality large‑scale data are limited by copyright and modality challenges; synthetic data generation via chain‑of‑thought reasoning and multimodal data scaling are proposed mitigations.

Latency wall – larger models increase communication and processing delays, restricting training duration, batch size, and overall throughput; advanced network topologies, model pruning, and larger batch strategies are suggested.

If all constraints are addressed, Epoch AI estimates AI training compute could reach roughly 2 × 10^29 FLOP by 2030, a ten‑thousand‑fold increase over GPT‑4’s 2 × 10^25 FLOP, making a GPT‑5‑scale system plausible.

The authors conclude that despite OpenAI’s distraction with mini releases, the highest priority remains an early GPT‑5 launch, while competitors such as Google Gemini 2 and Meta LLaMA 3.3 intensify the race.

References:

https://epoch.ai/blog/can-ai-scaling-continue-through-2030

https://openai.com/12-days/