When creation is cheap, judgment becomes the moat. Taste is how PMs decide, fast and accurately, what’s worth building.

Training LLMs with reinforcement learning is powerful but often expensive and unstable. GRPO changes that. By removing the critic model and using group-level comparisons, it can deliver faster and more reliable optimization for real-world AI systems.

AI can feel objective, but the data it learns from often isn’t. This article explores how bias enters machine learning systems, why fairness matters, and how developers can use proven mitigation techniques to build safer and more equitable models.

Most AI pilots don’t fail because the models are weak. They fail because enterprises pick the wrong problems, measure the wrong outcomes, and struggle to integrate tools into real workflows. The MIT data isn’t a warning about AI; it’s a map of how the small group of companies doing it right are pulling away.