Companies like Intercom and Cursor are proving that fine-tuning open-weight models on specific, "last-mile" user interaction data creates cheaper, faster, and more accurate models for vertical tasks (like customer service or coding) than general-purpose frontier models from labs like OpenAI.

Startups like Cognition Labs find their edge not by competing on pre-training large models, but by mastering post-training. They build specialized reinforcement learning environments that teach models specific, real-world workflows (e.g., using Datadog for debugging), creating a defensible niche that larger players overlook.

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.

Coding assistant startup Cursor exemplifies a new AI playbook: start with a powerful open-weight base model (like China's Kimi), then apply significant reinforcement learning compute (3-4x the base model's) to achieve superior performance in a specific vertical. This strategy avoids the massive cost of pre-training a foundation model from scratch.

New AI companies reframe their P&L by viewing inference costs not as a COGS liability but as a sales and marketing investment. By building the best possible agent, the product itself becomes the primary driver of growth, allowing them to operate with lean go-to-market teams.

By training a smaller, specialized model where company data is in the weights, firms avoid the high token costs of repeatedly feeding context to large frontier models. This makes complex, data-intensive workflows significantly cheaper and faster.

The founder of Stormy AI focuses on building a company that benefits from, rather than competes with, improving foundation models. He avoids over-optimizing for current model limitations, ensuring his business becomes stronger, not obsolete, with every new release like GPT-5. This strategy is key to building a durable AI company.

The common critique of AI application companies as "GPT wrappers" with no moat is proving false. The best startups are evolving beyond using a single third-party model. They are using dozens of models and, crucially, are backward-integrating to build their own custom AI models optimized for their specific domain.

To escape platform risk and high API costs, startups are building their own AI models. The strategy involves taking powerful, state-subsidized open-source models from China and fine-tuning them for specific use cases, creating a competitive alternative to relying on APIs from OpenAI or Anthropic.