Andy Triedman, Partner at Theory Ventures, argues that AI is reshaping not just security capabilities, but the entire business model of security companies. Unlike traditional "insurance-style" security products that were difficult to evaluate quantitatively, AI-enabled platforms provide clear scoreboards that buyers can use to measure effectiveness, speed, and cost efficiency. This shift from relationship-based to results-based evaluation advantages product-led teams with genuine AI-native expertise over traditional sales-driven organizations.
With a background spanning computational neuroscience and machine learning, Andy brings a unique perspective on how AI transforms both attack and defense capabilities. He explains why security presents particularly compelling investment opportunities due to the perpetual attacker-defender dynamic and the chronic information overload that defines security operations. He also shares frameworks for distinguishing between teams that truly understand AI-native development versus those that added AI features after LLMs emerged.
How AI transforms security products from insurance-style solutions to measurable automation platforms with clear performance scoreboards.The shift from relationship-based to results-based security sales as buyers can now verify AI system effectiveness in production environments. Why security operations present ideal AI investment opportunities due to chronic information overload and the need to process high-volume, low-value alerts.Framework for evaluating AI-native founder understanding of non-determinism and experimental development processes rather than traditional sprint planning.The distinction between teams with genuine AI-native expertise versus those that retrofitted existing products with AI features after LLM emergence.Token economics and value creation ratios in AI security, comparing problems that require thousands versus millions of tokens to generate customer value.Most overhyped AI security capabilities including fully autonomous security, AI penetration testing, and AI system security solutions.Team structure evolution for AI-native companies, with dramatically leaner engineering teams using AI-assisted development while maintaining traditional go-to-market functions.Data quality requirements for AI security systems, emphasizing the need for representative training data versus cosmetic synthetic data generation.Pitch deck evolution for AI startups, focusing on technical architecture clarity and deep workflow understanding rather than broad total addressable market claims.Investment strategy implications including using venture capital to finance gross margin neutral or negative products for accelerated adoption and growth.How AI enables security teams to transition from reactive triage work to proactive threat hunting and security engineering initiatives.