AI Investment Cycle Enters Second Half, Peak Expected Around 2028
On.cc · 1 SOURCESabout 2 hours ago2 MIN
Summary
Haitong International's Chief Economist Zhang Yidong (Zhang Yidong) delivered his autumn investment strategy outlook, declaring that the artificial intelligence-driven technology cycle is transitioning into its second half rather than approaching its end. Applying Juglar cycle theory—which identifies 8-10 year economic fluctuations—this current tech supercycle began in 2021, gained momentum in 2023, and is projected to reach its apex around 2028. The investment cycle's stock market peak is anticipated between 2027 and 2028, historically ahead of fundamental economic indicators by approximately six months.
Key Points
- Zhang Yidong from Haitong International sees AI technology cycle entering second half under Juglar cycle framework, with 2028 as projected peak
- AI industry focus will shift from infrastructure construction to AI model competition among tech giants and cross-industry AI applications
- Global GDP of approximately US$118 trillion in 2025 means 1% productivity boost from AI equals US$1.2 trillion annual return
- US cloud service providers and semiconductor companies maintain robust earnings growth, with profits spreading beyond the Magnificent 7
- China positions AI Plus as new economic engine, with coordinated fiscal, monetary, and industrial policies expected to accelerate
Why It Matters
Zhang outlined two structural investment themes for this phase: first, technology, media, and telecommunications (TMT) sector differentiation focusing on AI hardware leaders and AI-to-business applications including enterprise digitization, military AI, healthcare AI, and financial AI; second, value re-rating opportunities in non-AI sectors such as pharmaceuticals, non-ferrous metals, and export-oriented manufacturing chains . The broadening profit improvement beyond tech giants suggests AI's economic benefits are beginning to diffuse into traditional industries, potentially sustaining market momentum even as pure AI infrastructure plays mature.
Zhang outlined two structural investment themes for this phase: first, technology, media, and telecommunications (TMT) sector differentiation focusing on AI hardware leaders and AI-to-business applications including enterprise digitization, military AI, healthcare AI, and financial AI; second, value re-rating opportunities in non-AI sectors such as pharmaceuticals, non-ferrous metals, and export-oriented manufacturing chains . The broadening profit improvement beyond tech giants suggests AI's economic benefits are beginning to diffuse into traditional industries, potentially sustaining market momentum even as pure AI infrastructure plays mature.