Qiming Venture Partners has released its "2026 Qiming Venture Partners Top 10 AI Outlooks," outlining what it sees as the key artificial intelligence trends over the next 12 to 24 months. The report centers on four areas: foundation models, embodied intelligence, AI infrastructure and AI applications. In infrastructure, the firm said demand for AI compute is shifting from training to inference, while storage, advanced process technologies and advanced packaging are likely to remain in structural short supply. It also said compute asset reserves are set to become an important strategic resource for AI companies over the next two years, and competition in AI infrastructure is moving beyond standalone chips toward system-level capabilities spanning chips, interconnects, cooling and power supply. On the application side, Qiming expects commercialization to concentrate more heavily on vertical industries and high-paying users, with enterprise efficiency tools likely to scale first. As token costs fall and interaction models evolve, the firm said breakout consumer AI applications may gradually emerge. Qiming also expects AI-native organizations to move from concept to execution, with some companies potentially reaching per-capita productivity levels several times higher than those of traditional organizations.
Odaily reported that Qiming Venture Partners has published its "2026 Qiming Venture Partners Top 10 AI Outlooks," mapping out expected industry trends for the next 12 to 24 months across four areas: foundation models, embodied intelligence, AI infrastructure and AI applications.
Infrastructure focus shifts toward inference
On AI infrastructure, Qiming said demand for AI compute is moving from the training phase to the inference phase. It added that storage, advanced process technologies and advanced packaging are expected to continue facing structural shortages.
Over the next two years, the firm expects reserves of compute assets to become an important strategic resource for AI companies. It also said competition in AI infrastructure is moving beyond individual chips and toward system-level competition covering chips, interconnects, cooling and power supply.
Commercialization to center on verticals and high-paying users
For AI applications, Qiming expects commercialization over the next 12 to 24 months to focus more heavily on vertical industries and high-paying users, with enterprise applications aimed at improving efficiency likely to break out first.
As token costs decline and interaction models evolve, the firm said breakout consumer AI applications may gradually appear. Qiming also expects AI-native organizations to move from concept into practical implementation, with some companies potentially achieving per-capita productivity several times that of traditional organizations.
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