The Convergence
Open a random AI product's landing page in 2026 and you will see: the same chat interface, the same "Upload a document" capability, the same model selector dropdown, and the same bulleted feature list. The underlying models are often the same too — GPT-4o or Claude powering yet another "AI assistant for X" product.
Why This Happened
Differentiation in AI products requires either proprietary data (rare), novel interaction patterns (hard to design), or domain-specific fine-tuning (expensive). Most teams don't have any of these, so they compete on marketing and UI polish. The result is a market where products feel interchangeable because, technically, they largely are.
Does It Matter?
Surprisingly, less than you might think. Users don't choose products based on model architecture — they choose based on workflow fit, reliability, and whether the product understands their specific use case. The sameness is real, but it may not be the competitive disadvantage it appears to be. The winners will be the ones who figure out that distribution and domain expertise beat model differentiation.