01
Overview
🧠 Scaling Pivot: Brute-force data ingestion yields diminishing returns across LLM benchmark suites.
Inference Compute
Dynamic search algorithms Test-Time Compute replace static continuation.
02
AI Compute Evolution (Before ➔ Now)
Было: Extensive pre-training of trillion-parameter models with massive energy burn ➔ Стало: Test-time compute and dynamic verification on compact architectures
Было: Blind token generation via statistical probability ➔ Стало: Step-by-step hypothesis verification with automated error falsification
03
Overview
Paradigm: Test-Time Compute
Compute Focus: Inference & verification
Energy Efficiency: Up to 65% savings
Logic Accuracy: +48% reasoning boost
30 seconds
Key facts
Safe Superintelligence founder Ilya Sutskever confirmed that classical pre-training scaling laws have reached diminishing returns.
- Safe Superintelligence founder Ilya Sutskever confirmed that classical pre-training scaling laws have reached diminishing returns.
Want to go deeper?
The Hook & Core Paradox
The Collapse of Brute Force: Why terawatt datacenters no longer guarantee model supremacy?
Between the Lines (Market & Margin Shift)
Reasoning over memorization: model intelligence is now measured by search depth during inference rather than petabytes consumed during pre-training.
The Bottleneck (Physical & Engineering Barrier)
Energy wall: exponential thermal dissipation for diminishing sub-2% benchmark gains on static tasks.
3–5 Year Horizon (Structural Shift)
Edge renaissance: compact 7B reasoning models achieving parity with massive cloud models via test-time tree search.
Chronicle: Past 5 Years
2024–2025
Initial research phase and prototype validation.
2026
Production deployment and commercial integration.
Forecast Scenarios
3 Years
Ecosystem consolidation and protocol standardization.
5 Years
Ubiquitous deployment across operational platforms.
10 Years
Foundation for next-generation autonomous systems.