VCs traditionally advise against early product expansion. But with agentic AI, which leverages existing metadata to solve new problems without building new screens, startups can rapidly add capabilities to meet customer demand for a single, unified agent, accelerating the compound startup model.

Enterprises will move slowly on deploying AI agents due to massive security and integration risks with legacy systems. Startups, with less to lose and cleaner stacks, will adopt agent-based workflows rapidly, creating a significant competitive advantage and widening the gap between incumbents and challengers.

When building infrastructure for a nascent technology like AI agents, your core customers may not exist yet. This strategy, similar to Stripe's early days, involves betting on the future growth of an entire ecosystem. You are selling to the customers of tomorrow by building the foundational tools they will inevitably need.

When users access SaaS tools through their own AI environments like Codex, they use their own AI model tokens, not the SaaS vendor's. This eliminates a huge cost center for SaaS companies, shifting their business model toward making their apps agent-friendly rather than paying for AI features.

Building effective agents requires intensive, custom work for each client—data cleansing, training, and deployment by skilled engineers. Large incumbents lack the agility and cost structure to provide this bespoke service, creating an opening for focused startups who can afford the human capital.

Instead of selling AI tools to incumbents (e.g., law firms who bill by the hour), build an AI-first service that delivers the end result directly to the customer. This avoids conflicts of interest and captures more value.

By not raising a Series A, Paperflight retained the freedom to make unconventional product decisions. For instance, they built a content creation tool instead of a trendy coaching feature, waiting years until AI technology could truly disrupt the coaching space on their own terms.

The business model is shifting from selling software to selling outcomes. Instead of creating a tool and inviting users, create pre-trained agents that perform valuable work. Then, invite companies to a workspace where this 'team' of AI employees is ready to start delivering value immediately.

Traditional software required deep vertical focus because building unique UIs for each use case was complex. AI agents solve this. Since the interface is primarily a prompt box, a company can serve a broad horizontal market from the beginning without the massive overhead of building distinct, vertical-specific product experiences.