The Five Scaling Phases of AI: From “Make It Bigger” to “Reward the Truth”

The story of modern AI isn’t a straight line of “bigger models = better results.” It’s a sequence of paradigm shifts, each one redefining where compute creates capability.

Understanding these phases is the structural map that explains why 2025’s models weren’t dramatically larger than 2023’s — yet reasoned dramatically better.

The Five Phases in One Line Each

Phase 1 — Kaplan Era (2020): “Make it bigger.” Capability tracked parameter scale. GPT-3 used 175B parameters on 300B tokens — large but undertrained.

Phase 2 — Chinchilla Correction (2022): “Train longer, not bigger.” DeepMind proved data matters as much as size. The 20-token-per-parameter ratio became the new orthodoxy.

Phase 3 — Post-Training (2022–2024): “Thin layers, huge impact.” SFT + RLHF turned base models into ChatGPT — as explored in the intelligence factory race between AI labs — . But human preference is a fundamentally limited training signal.

Phase 4 — Test-Time Compute (Late 2024): “Think longer, not bigger.” OpenAI’s o1 decoupled capability from model size — the same model could think deeper on harder problems.

Phase 5 — RLVR (2025): “Reward the truth, not the vibe.” Train against verifiable rewards (math, code, logic). Models discover reasoning strategies nobody taught them.

The Key Insight

Each phase didn’t replace the previous — it rebalanced compute toward a more efficient conversion of resources into capability. The signal got cleaner. Not bigger. Cleaner.

The organizations that understand this structural map — not just “AI is getting better” but how and why and where next — ride the compound effect rather than get disrupted by it.

Read the full deep-dive on The Business Engineer →