Bond.now post frames AI gains as domain-specific superpowers from scaling, not AGI thresholds

The article examines deployment records where incremental scaling produced targeted performance lifts. It cites patterns from model releases showing consistent benchmark deltas without requiring general intelligence milestones. Primary evidence draws from public leaderboards tracking coding, math, and reasoning tasks across successive releases.

Data from scaling law studies confirm that compute-optimal training yields predictable accuracy gains per order of magnitude. Kaplan et al. (2020) documented loss reduction tied directly to parameter and data volume increases. Recent model cards repeat the same log-linear relationship on new tasks, contradicting threshold narratives.

Operationally this directs engineering effort toward measurable metrics like SWE-bench pass rates or GPQA scores rather than speculative alignment taxonomies. Teams allocate resources to domain adapters and retrieval layers that compound existing base model strengths.

Next releases will likely ship narrow capability extensions, such as 5-8 point gains on agent benchmarks, while AGI definitions remain unstandardized.