Scaling Laws

After a decade of empirical scaling, we treat capacity planning like a financial instrument. Compute, data, and accuracy follow predictable power-law curves whose constants we can estimate by week two of any new project. That discipline turns speculative research into forecastable engineering.

Our latest 40-billion-parameter run served as a validation exercise. Loss fell with compute at an exponent of -0.48, matching forecasts derived from much smaller pilot models. Optimal batch size grew sublinearly, which let us reuse the existing data pipeline without rewriting the ingestion stack. Most importantly, freshness trumped volume: deduplicated streams of recent exploits produced larger gains than dumping additional archival corpora onto the GPU cluster.

These empirical laws feed straight into budgeting. When finance asks what another megawatt of power buys us, we can answer with confidence intervals. We map marginal compute to expected vulnerability detection rates and time-to-containment improvements, turning infrastructure discussions into risk-reduction debates instead of qualitative wish lists. Microsoft’s public data on Security Copilot adoption shows similar predictability: adding compute correlates with tangible reductions in alert backlogs, reinforcing that the curves hold in production, not just the lab.

There is also a governance angle. Regulators increasingly demand proof that AI-driven security systems behave predictably. By documenting our scaling constants, we can show that the system is not lurching unpredictably as we add resources. Instead, every unit of investment traces a smooth curve that auditors can review. Policy conversations, from NIST workshops to EU AI Act comment periods, now cite scaling transparency as a precondition for licensing high-risk AI systems.

Looking forward, we are extending the framework to incorporate carbon intensity and supply-chain volatility. As advanced accelerators remain scarce, knowing how performance degrades when we shift to lower-tier hardware lets us guarantee service levels even under constrained capacity. We are also experimenting with synthetic data augmentation tuned to the OWASP LLM Top 10 threat categories, measuring how targeted corpora bend the curves in our favor. Scaling laws are not curiosities—they are the control knobs for operational resilience.