Founder playbook: How to build an AI-native, product-first startup

Unlocking AI-Native Power: A Product-First Blueprint for Startup Success at Web Summit Vancouver 2026

Vineet Edupuganti, Co-founder and CEO of Cogent Security, addressed Web Summit Vancouver 2026, defining an “AI-native” company as one where AI is indispensable to its core operations, unlike “AI-enabled” firms that merely add AI tools. He stressed AI must be the organizational heart, not just a feature. Mr. Edupuganti then outlined three pillars for building such a startup: optimizing the software development life cycle (SDLC), establishing robust context architecture, and cultivating a “queriable company.” These are vital for embedding AI into every process to drive material business outcomes, moving beyond simple AI tool adoption.

Within the SDLC, human roles shift to architecting and managing AI agents. Engineers and product managers define specifications, while agents handle code generation, testing, and evaluation, including UAT, freeing humans for strategic oversight. Automating one step often reveals downstream bottlenecks, which must then be identified and “AI-ified” for continuous improvement. Data is paramount; agents need access to comprehensive tribal knowledge, often scattered across platforms like Slack, Notion, and GitHub, for better runtime decisions. This requires a culture of consistent, accurate documentation, using tools like Slack emojis and publishing design documents.

Architecturally, building systems that enable squadrons of AI agents to collaborate securely and in parallel is essential. Culturally, an AI-native environment demands talent with low ego, a fast-moving mindset, and a willingness to rebuild. Mr. Edupuganti noted that due to cheap code generation, completely re-architecting functional systems from scratch is now a viable strategy for rapid adaptation, requiring talent that thinks from first principles. Beyond engineering, context engineering drives AI gains across all business functions, including recording customer calls via tools like Gong and ensuring internal tools have production-grade APIs for seamless information integration.

Innovative methods, such as voice agents interacting with salespeople, help overcome reluctance to update CRMs, ensuring a continuous flow of valuable data. This comprehensive data layer is crucial for making the company “queriable” for both humans and agents. New hires achieve productivity in two weeks, querying data sources for instant insights. Agents, as key data consumers, require robust systems to operate consistently and generate value, avoiding the “garbage in, garbage out” problem. All organizational “exhaust”—meetings, channels, decisions—should be capitalized as training data, representing significant opportunity cost.

Ultimately, an AI-native company hinges on adaptable, low-ego talent focused on architecting and managing agents, rather than just coding speed, to gain competitive advantage in the rapidly evolving AI landscape by embracing continuous process evaluation.