Large AI labs like OpenAI are not always the primary innovators in product experience. Instead, a "supply chain of product ideas" exists where startups first popularize new interfaces, like templated creation. The labs then observe what works and integrate these proven concepts into their own platforms.

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The traditional, linear handoff from product (PRDs) to design to dev is too slow for AI's rapid iteration cycles. Leading companies merge these roles into smaller, senior teams where design and product deliver functional prototypes directly to engineering, collapsing the feedback loop and accelerating development.

The rapid growth of AI products isn't due to a sudden market desire for AI technology itself. Rather, AI enables superior solutions for long-standing customer problems that were previously addressed with inadequate options. The demand existed long before the AI-powered supply arrived to meet it.

Incumbent companies are slowed by the need to retrofit AI into existing processes and tribal knowledge. AI-native startups, however, can build their entire operational model around agent-based, prompt-driven workflows from day one, creating a structural advantage that is difficult for larger companies to copy.

Startups like ElevenLabs and Midjourney compete with large AI labs by imbuing their models with a founder's specific 'taste.' This unique aesthetic, from voice texture to image style, creates a product identity that is difficult for a general, large-scale model to replicate.

Product managers at large AI labs are incentivized to ship safe, incremental features rather than risky, opinionated products. This structural aversion to risk creates a permanent market opportunity for startups to build bold, niche applications that incumbents are organizationally unable to pursue.

Beyond its technical capabilities, OpenAI's app ecosystem within ChatGPT functions as a new distribution platform. For founders, this creates a strategic opportunity to build apps that serve as an interface layer to their product, opening a novel and potentially powerful channel for user acquisition and growth.

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.

Despite comparable model capabilities, OpenAI's thoughtful UX, like providing trending templates in a TikTok-style feed for image generation, successfully guides users. In contrast, Google's blank-slate interfaces can intimidate users, proving that small product details are crucial for adoption.

Large labs often suffer from organizational friction between product and research. A small, focused startup like Cursor can co-design its product and model in a tight loop, enabling rapid innovations like near-real-time policy updates that are organizationally difficult for incumbents.

Many engineers at large companies are cynical about AI's hype, hindering internal product development. This forces enterprises to seek external startups that can deliver functional AI solutions, creating an unprecedented opportunity for new ventures to win large customers.