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.

Unlike traditional software where problems are solved by debugging code, improving AI systems is an organic process. Getting from an 80% effective prototype to a 99% production-ready system requires a new development loop focused on collecting user feedback and signals to retrain the model.

Minimax enhances its reinforcement learning process by treating its own expert developers as scalable reward models. These developers participate directly in the training cycle, identifying desirable behaviors and providing precise feedback on complex coding tasks, which creates a model tailored to professional workflows.

Instead of waiting for AI models to be perfect, design your application from the start to allow for human correction. This pragmatic approach acknowledges AI's inherent uncertainty and allows you to deliver value sooner by leveraging human oversight to handle edge cases.

The key advantage of labs like OpenAI isn't just pre-training, but their ability to continuously post-train models on product-specific data. This tight feedback loop between the model and the product is their real competitive moat, which Prime Intellect aims to democratize for all companies.

To avoid choosing between deep research and product development, ElevenLabs organizes teams into problem-focused "labs." Each lab, a mix of researchers, engineers, and operators, tackles a specific problem (e.g., voice or agents), sequencing deep research first before building a product layer on top. This structure allows for both foundational breakthroughs and market-facing execution.

By embedding product teams directly within the research organization, Google creates a tight feedback loop. Instead of receiving models "over the wall," product and research teams co-develop them, aligning technical capabilities with customer needs from the start.

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.

The Codex team combines research, product, and engineering, allowing them to solve problems at either the product level or the core model level. This tight integration creates a flywheel where product needs drive research and research breakthroughs are immediately applied to the product.