For vertical AI applications, foundation models are now sufficiently intelligent. The primary challenge is no longer model capability but building the surrounding software infrastructure—tools, UIs, and workflows—that lets models perform useful work reliably and trustworthily.

Advanced agentic AI coding tools have strong product-market fit with prosumers, but this is a high-churn, price-sensitive market. In the enterprise, the most established PMF is still with simpler autocomplete features like GitHub Copilot, not the more sophisticated—and less proven—agentic solutions.

Despite hype across many categories, data shows coding and software development tools account for 55% of all enterprise end-user spending on AI. This makes the developer tool market the current epicenter and most valuable battleground of the enterprise AI revolution.

Higgsfield initially saw high adoption for viral, consumer-facing AI features but pivoted. They realized foundation model players like OpenAI will dominate and subsidize these markets. The defensible startup strategy is to ignore consumer virality and solve specific, monetizable B2B workflow problems instead.

Specialized SaaS companies like Writer and Intercom are moving beyond simply wrapping OpenAI or Anthropic APIs. They are now training their own foundation models to create more defensible, vertically-integrated AI products, signaling a shift away from platform dependency toward bespoke AI stacks.

Early-stage AI startups should resist spending heavily on fine-tuning foundational models. With base models improving so rapidly, the defensible value lies in building the application layer, workflow integrations, and enterprise-grade software that makes the AI useful, allowing the startup to ride the wave of general model improvement.

The fear that large AI labs will dominate all software is overblown. The competitive landscape will likely mirror Google's history: winning in some verticals (Maps, Email) while losing in others (Social, Chat). Victory will be determined by superior team execution within each specific product category, not by the sheer power of the underlying foundation model.

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.

While the "bitter lesson" suggests powerful general models will dominate, vertical AI solutions can thrive by deeply integrating with a company's specific data, workflows, and project context. The model can't know this proprietary information; value is created by the application that bridges this gap.