Large cybersecurity incumbents are not fully embracing an AGI-centric strategy for forensics. Their focus on existing product revenue, combined with a cultural skepticism among security professionals about AI's true capabilities, means they are undervaluing the paradigm shift. This inertia provides a crucial opening for 'AGI-pilled' startups.

Asymmetric Security operates on the assumption that AGI is inevitable. This 'AGI-pilled' worldview shapes their strategy to completely rethink cyber defense, preparing for a world with a virtually unlimited supply of intelligent labor, rather than just automating current tasks.

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 current cyber defense model is reactive, using triage for endless alerts. Asymmetric Security's AGI-premised strategy is to shift this paradigm to proactive, continuous digital forensics. AI agents provide the 'infinite intelligent labor' needed to conduct deep investigations constantly, not just after a breach is suspected.

AI tools drastically accelerate an attacker's ability to find weaknesses, breach systems, and steal data. The attack window has shrunk from days to as little as 23 minutes, making traditional, human-led response times obsolete and demanding automated, near-instantaneous defense.

The long-term trajectory for AI in cybersecurity might heavily favor defenders. If AI-powered vulnerability scanners become powerful enough to be integrated into coding environments, they could prevent insecure code from ever being deployed, creating a "defense-dominant" world.

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.

Most AI "defense in depth" systems fail because their layers are correlated, often using the same base model. A successful approach requires creating genuinely independent defensive components. Even if each layer is individually weak, their independence makes it combinatorially harder for an attacker to bypass them all.

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.