Standard AI evaluations use well-defined scenarios. Military operations are inherently dynamic and unpredictable. National security AI therefore requires a new evaluation paradigm focused on specific, tailored use cases and operational reliability under unforeseen circumstances.

The cybersecurity landscape is now a direct competition between automated AI systems. Attackers use AI to scale personalized attacks, while defenders must deploy their own AI stacks that leverage internal data access to monitor, self-attack, and patch vulnerabilities in real-time.

The massive investment in AI mirrors the HFT speed race. Both are driven by a fear of falling behind and operate on a logarithmic curve of diminishing returns, where each incremental gain requires exponentially more resources. The strategic question in both fields becomes how far to push.

Building massive sensor networks or missile defense systems is physically observable, giving adversaries time to develop countermeasures. In contrast, a sudden leap in AI-enabled intelligence processing can be invisible, creating a surprise window of vulnerability with no warning.

In warfare or business, an opponent's sheer speed can render superior intelligence irrelevant. A novice chess player making four moves for every one of a grandmaster's will win. Similarly, AI systems that can execute faster will defeat more intelligent but slower counterparts.

Advanced AI models, like Anthropic's, that can identify deep cybersecurity risks and zero-day exploits transform the need for computing power from a commercial want to a national security imperative. This ensures that demand for compute will be funded regardless of economic conditions.

While AI gives attackers scale, defenders possess a fundamental advantage: direct access to internal systems like AWS logs and network traffic. A defending AI stack can work with ground-truth data, whereas an attacking AI must infer a system's state from external signals, giving the defender the upper hand.

Security's focus shifted from physical (bodyguards) to digital (cybersecurity) with the internet. As AI agents become primary economic actors, security must undergo a similar fundamental reinvention. The core business value may be the same (like Blockbuster vs. Netflix), but the security architecture must be rebuilt from first principles.

The old security adage was to be better than your neighbor. AI attackers, however, will be numerous and automated, meaning companies can't just be slightly more secure than peers; they need robust defenses against a swarm of simultaneous threats.