By 2026, what will the technological architecture of those truly successful million-dollar AI companies with viable business models look like?

No longer just stacking models, but building around data flow, inference optimization, and cost control. The core architecture will include: intelligent data processing layers (automatic cleaning, labeling, augmentation), multimodal inference engines (compatible with text, speech, visual tasks), dynamic inference routing (adapting to scenarios by calling lightweight or heavy models), and real-time feedback loops (continuously optimizing output quality).

From the early days of "large model direct connection" to the current "model orchestration" and into the future of "intelligent agent networks," this evolutionary path is already quite clear. Teams that can push costs to the marginal level, control response times within milliseconds, and maintain stable output will be the true winners by 2026.