Google’s VP Darren Mowry warned last month that AI startups falling into the “LLM wrapper” or “AI aggregator” mold face extinction, after reviewing 4,000 proposals. Startups that survived built proprietary models for verticals, exploiting the right AI technique instead of a single general‑purpose LLM.

The article maps a decade of AI breakthroughs—CNNs for vision, Deep Reinforcement Learning for dynamic control, and today’s LLMs for language. Each technology excels at a distinct problem class, yet the market still over‑promotes monolithic LLM solutions.

In sales, drafting emails suits LLMs, but managing pipeline dynamics demands reinforcement learning. A model that learns to sequence stakeholder engagement can lift close rates threefold, a capability LLM wrappers cannot deliver.

The future, the piece argues, mirrors past software shifts: from single‑monolith apps to networks of specialized services. AI will adopt the same route, deploying millions of focused agents that communicate through reasoning layers, rather than a single all‑purpose transformer.