Theory Ventures' Andy Triedman on AI-Native vs AI-Feature Teams

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. 

Topics Discussed:

• 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.

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