Frontier AI Pace & Scaling Simulator

Hypothesis under test: “The great AI slowdown? Nothing is now stopping Anthropic and OpenAI from pacing the frontier on their own.” (WSJ Opinion). This simulator tests whether cluster scaling limits, algorithmic overhang discovery, or capital velocity represent true binding constraints before 2028.

Trajectory Scenarios

Scaling Vectors

Cluster Scale Velocity 2.50x/yr

Annual multiplication of active GPU/TPU cluster training capacity.

Algorithmic Efficiency 1.80x/yr

Effective compute multiplier from synthetic data, architectures, & post-training reasoning.

Capital Deployment Pace 3.00x

Relative institutional debt and equity funding cadence (1.0x baseline to 5.0x hyperscale).

Datacenter Power/Grid Ceiling 1,000,000 PF

Hard gigawatt/grid connection constraint on peak cluster size.

Frontier Status

Pacing Unimpeded

Both labs scaling at max pace

Cumulative FLOPs

28.6 log10

~4.0e28 Total FLOPs

Next 100x Milestone

2027.4

Expected flagship release

Primary Bottleneck

Capital Allocation

Pacing factor

Cumulative Effective Training Compute (log10 FLOPs)

Simulated Pace

Landmark Anchors

Estimated Grid Cap

WSJ Opinion Assessment: Pacing Dynamics

At a cluster scaling rate of 2.5x/yr and algorithmic efficiency multiplier of 1.8x/yr, OpenAI and Anthropic are pacing frontier capability far ahead of public slowdown claims. The primary operational bottleneck remains Capital Allocation, with the physical grid ceiling remaining non-binding until past late 2027.

Contextualizing OpenAI Orion / Anthropic Claude 3.5 Sonnet & Opus trajectories

Current State: 28.6 log10 FLOPs • Milestone: 2027.4 • Bottleneck: Capital Allocation

Artifact ready for reproducible export