The AI industry has undergone significant consolidation and strategic shifts over the past year, according to industry observers discussing developments at AIE Europe. Infrastructure platforms have largely stabilized after years of constant reinvention, with application companies proving more resilient to model volatility than pure infrastructure plays. The emerging consensus centers on a "skills" or agent-based packaging format as the minimal viable unit for AI products, fundamentally changing how companies approach building and deploying AI systems. Domain specialization has moved from marketing narrative to operational reality, with companies like Cursor and Cognition demonstrating that in-house trained models can outcompete general-purpose alternatives when backed by sufficient proprietary data and user behavior signals. The debate between vertical and horizontal AI startups is resolving toward a hybrid model where specialized applications can serve as outsourced AI teams for enterprises, while infrastructure platforms increasingly function as development sandboxes. Open-source models and non-NVIDIA hardware alternatives have gained unexpected traction as companies seek efficiency gains and reduced latency, with every 10x speedup unlocking new product experiences. Looking forward, several trends are reshaping competitive dynamics: AI coding has become the largest and fastest-growing category in the field, memory and personalization systems are emerging as key product differentiators beyond simple model capability, and the industry remains in an experimental phase prioritizing token throughput over efficiency. The rise of agent-first architecture means traditional developer experience and API quality increasingly determine product success, while pretraining data incumbents compound their advantages in this new paradigm.