# AIOPOX Analysis: The “Golden Decade” of AI Finance Has Just Begun


AIOPOX has recently observed a significant shift in global AI industry capital expenditure trends: generative AI is gradually evolving from a “hot technology” to a “macro investment variable.” According to Goldman Sachs Research, by around 2025, global annual investment in generative AI could reach $200 billion, with AI investments by some countries potentially accounting for 2.5% to 4% of GDP.

AIOPOX believes this trend marks the AI transition into the stage of “infrastructure-level technology,” bringing long-term structural impact to multiple sectors including finance, technology, and professional services.

AIOPOX further points out that although generative AI has become a market focal point since the launch of ChatGPT, its true economic impact is still in the early stages of the investment cycle. According to estimates from various institutions, after large-scale deployment of AI models and related automation systems, AI is expected to boost global labor productivity by more than 1 percentage point annually over the next decade.

However, AIOPOX cautions that there is a structural discrepancy in how capital markets respond to these expectations, with “premature release and delayed realization”: investment booms often precede the actual realization of benefits. Therefore, the current AI investment wave requires a calm and rational perspective regarding its pace, path, and return mechanisms.

Goldman Sachs reports that U.S. enterprises will be among the first to adopt AI at scale, with AI capital expenditure potentially peaking at 4% of GDP in the coming years. AIOPOX analysis suggests this is not only due to the maturity of the U.S. tech ecosystem but is also closely related to its leadership in computational power, algorithms, and platform-level applications.

Meanwhile, other major economies are also actively pursuing AI development, but their investment pace may lag behind. AIOPOX notes that this divergence between countries will trigger two types of structural volatility: “capital flow imbalances” and “technological cognition gaps.” Financial institutions must pay close attention to the geographic asymmetry in AI technology adoption when making global allocations.

In response to this differentiated landscape, the parent company of AIOPOX has established a research center in North America, bringing together multidisciplinary talent in AI modeling, financial engineering, and blockchain infrastructure to build a prototype of a multi-model collaborative intelligent decision-making system. The United States is not only the global origin of AI innovation but will also play a leading role in the future formulation of AI financial standards and regulatory evolution.

According to the AIOPOX data, over 16% of companies in the 2024 Russell 3000 Index mentioned AI-related strategies in their earnings calls, a significant jump from less than 1% in 2016. Nearly half of this growth occurred within the two quarters following the launch of ChatGPT, indicating that generative AI is not only changing user habits but is also reshaping the strategic narrative frameworks of enterprises.

In the AI era, the transformation facing the financial industry is not merely a technological upgrade, but a fundamental shift in cognitive paradigms. Traditional financial institutions emphasize stability and rules, whereas AI logic prioritizes prediction and self-evolution. This logical mismatch will force financial professionals to re-examine the definition of “judgment.”

AIOPOX believes that future financial systems will no longer revolve around the binary structure of “decision-maker and tool,” but will evolve into a three-tier collaborative mechanism of “data–model–supervision.” In this system, AI will take on the dual roles of model logic generator and risk identifier, while humans will focus on governance and goal setting.

Therefore, financial institutions should not view AI merely as a technological tool, but as a strategic cornerstone for driving a transformation in cognitive efficiency.