An impressive AI capability, like a multi-language voice agent, is a differentiator that can be copied. Lasting defensibility is achieved not by the AI feature itself, but by embedding it within an end-to-end workflow that becomes the system of record for the user.
Defensible companies build systems of record (like an ERP) that are so integral to a customer's operations that switching is prohibitively difficult. This creates a 'hostage' dynamic, providing a powerful moat against competitors, even those with better AI features.
The fundamental driver of AI adoption is its ability to help people do less work while gaining more economic value. This 'richer and lazier' principle explains why individuals and enterprises are rapidly embracing the technology, as it directly taps into a core aspect of human behavior.
Companies create defensibility by generating unique, non-public data through their operations (e.g., legal case outcomes). This proprietary data improves their own models, creating a feedback loop and a compounding advantage that large, generalist labs like OpenAI cannot replicate.
AI-native companies find more success selling to new businesses or those hitting an inflection point (e.g., outgrowing QuickBooks). Trying to convince established companies to switch from deeply embedded systems like NetSuite is a much harder 'brownfield' battle with a higher cost of acquisition.
While AI can improve existing software categories, the most significant opportunity lies in creating new applications that automate tasks previously performed by humans. This 'software eating labor' market is substantially larger than the traditional SaaS market, representing a massive greenfield opportunity for startups.
The shift to AI creates an opening in every established software category (ERP, CRM, etc.). While incumbents are adding AI features, new AI-native startups have an advantage in winning over net-new, 'greenfield' customers who are choosing their first system of record.
In categories like customer support, where AI can handle the vast majority of queries, charging per human agent ('per seat') no longer makes sense. The business model is shifting to be outcome-based, where customers pay for the value delivered, such as per ticket resolved or per successful interaction.
A powerful go-to-market strategy is for an AI company to buy a legacy business (e.g., a debt collector) with existing clients but declining revenue. This allows the startup to bypass the difficult early sales process, immediately deploy and refine its AI, and use the acquired firm's client roster as a launchpad.
Companies controlling proprietary data, even if publicly accessible but hard to collect (like FlightAware), can use AI to deliver a 'finished meal' instead of just the 'raw vegetables.' This moves them up the value chain from a data provider to a solutions provider, unlocking significant pricing power.
Like Kayak for flights, being a model aggregator provides superior value to users who want access to the best tool for a specific job. Big tech companies are restricted to their own models, creating an opportunity for startups to win by offering a 'single pane of glass' across all available models.
While AI can reduce labor costs, the most powerful value proposition is generating significantly more revenue. The AI company Salient found success not by pitching savings on call center staff, but by proving its AI could increase debt collection rates by 50%—a far more compelling outcome for clients.