Design is about problem-solving and requires a deep understanding of user needs, which current AI models struggle to replicate.

Achieving sustainable scale in AI applications requires aligning models with real-world use cases and optimizing user behavior throughout the product design process.

To create effective AI models for engineering, we need to generate synthetic training data that is physics-based and informed by supply chain considerations for various physical product domains.

I believe taste and design will become increasingly valuable in the application layer of AI, as good design can differentiate products.

Designers should advocate for users, ensuring that products address not just the ideal cases but also the edge cases that often lead to dissatisfaction.

In consumer AI, it's crucial to let data guide product development, focusing on what truly meets user needs rather than just following trends.

The future will likely see more researchers embedded in every product team, as fine-tuning models for specific use cases becomes a core workflow for building products.

Good design is a lost art in early-stage startups, and now is the moment for design to have a significant impact as everything is being reimagined with AI.

As AI becomes more integrated into various applications, we need to optimize for specific internal use cases, which may lead us to use other models if they better serve those needs.