The initial AI rush for every company to build proprietary models is over. The new winning strategy, seen with firms like Adobe, is to leverage existing product distribution by integrating multiple best-in-class third-party models, enabling faster and more powerful user experiences.
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The "AI wrapper" concern is mitigated by a multi-model strategy. A startup can integrate the best models from various providers for different tasks, creating a superior product. A platform like OpenAI is incentivized to only use its own models, creating a durable advantage for the startup.
Don't just sprinkle AI features onto your existing product ('AI at the edge'). Transformative companies rethink workflows and shrink their old codebase, making the LLM a core part of the solution. This is about re-architecting the solution from the ground up, not just enhancing it.
As foundational AI models become more accessible, the key to winning the market is shifting from having the most advanced model to creating the best user experience. This "age of productization" means skilled product managers who can effectively package AI capabilities are becoming as crucial as the researchers themselves.
As consumers become wary of "AI," the winning strategy is integrating advanced capabilities into existing products seamlessly, like Google is doing with Gemini. The "AI" branding used for fundraising and recruiting will fade from consumer-facing marketing, making the technology feel like a natural product evolution.
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.
As foundational AI models become commoditized, the key differentiator is shifting from marginal improvements in model capability to superior user experience and productization. Companies that focus on polish, ease of use, and thoughtful integration will win, making product managers the new heroes of the AI race.
The fundamental shift from AI isn't about replacing foundational model companies like OpenAI. Instead, AI creates a new technological substrate—productized intelligence—that will engender an entirely new breed of software companies, marking the end of the traditional SaaS playbook.
Contrary to early narratives, a proprietary dataset is not the primary moat for AI applications. True, lasting defensibility is built by deeply integrating into an industry's ecosystem—connecting different stakeholders, leveraging strategic partnerships, and using funding velocity to build the broadest product suite.
Instead of offering a model selector, creating a proprietary, branded model allows a company to chain different specialized models for various sub-tasks (e.g., search, generation). This not only improves overall performance but also provides business independence from the pricing and launch cycles of a single frontier model lab.
Large companies integrate AI through three primary methods: buying third-party vendor solutions (e.g., Harvey for legal), building custom internal tools to improve efficiency, or embedding AI directly into their customer-facing products. Understanding these pathways is critical for any B2B AI startup's go-to-market strategy.
