The narrative that new features from major AI labs kill startups is often wrong. Instead, these releases serve as massive free education, validate new user behaviors, and unlock enterprise budgets. This creates demand for more specialized, vertical-focused tools, ultimately growing the entire ecosystem for startups.

Legora wins 85% of competitive deals by focusing on three things: product quality, team dedication, and their long-term roadmap. In a fast-moving field like AI, enterprise clients are betting on a partner who can navigate the future, not just a tool for today.

Sandstone, a legal tech startup, is intentionally avoiding a focus on AI document redlining. Their strategy assumes this function will be commoditized by foundation model providers like OpenAI. Instead, they are building their moat around proprietary workflow automation and managing legal context across the enterprise.

Counter to fears that foundation models will obsolete all apps, AI startups can build defensible businesses by embedding AI into unique workflows, owning the customer relationship, and creating network effects. This mirrors how top App Store apps succeeded despite Apple's platform dominance.

For entrepreneurs building on top of large language models, the key differentiator is not creating general platforms but achieving deep domain specialization. The call to arms is to know a vertical better than anyone and imbue that unique knowledge into AI agents, creating a defensible moat against more generalized tools.

When prospects have already experimented with general AI tools like ChatGPT and experienced their limitations (lack of context, poor accuracy), they develop a tangible business pain. This makes them more receptive to a specialized enterprise AI solution, as they are already educated on the problem and the shortcomings of incumbent tools.

While many legal AI tools use the same foundational models, they differentiate by offering features crucial for law firms: strict permissions, compliance controls, and integrations with proprietary legal databases like Westlaw. This 'packaging' of trust is the real product, for which discerning law firms willingly pay a premium.

A key risk for AI tools is that LLM providers like Anthropic (Claude) could build competing products. However, it may be more economically rational for these giants to serve as the underlying engine for many specialized tools, collecting fees without needing to build and market for every vertical.

Instead of converging, major AI labs are specializing: ChatGPT targets the mass market with ads, Claude focuses on high-stakes enterprise verticals like finance, and Gemini leads with creative model releases. This strategic divergence means they can't cover every use case, leaving valuable, defensible gaps for startups to build significant businesses.