Grouping the Stochastic Machine: Precision, Not Capability, as the Frontier Metric for AI Systems
Frontier language models are compared, marketed, and benchmarked on capability -- what their best or average output can achieve. I argue this measures the wrong axis. The models have saturated accuracy: their mean output lands on the target. What now separates one system from another in practice is precision: how tightly concentrated their outputs are around that target across repeated, identical requests. Borrowing the marksman's distinction, capability is where the average shot lands; reliability is the size of the group. I make three claims. First, precision, not capability, is the frontier differentiator between systems, and benchmark culture systematically fails to measure it, report...
| Authors | George Andrikopoulos |
|---|---|
| Primary category | cs.AI |
| Published | 19 August 2026 |
| Last updated at source | 19 August 2026 |
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