Smart Capital Adjusts Before Turning Points

The adjustment of intelligent capital before the turning point is essentially a strategic restructuring, shifting from chasing short-term bubbles to building long-term value moats. This adjustment is not a simple change in investment strategy, but an inevitable choice after capital's deepening understanding of the essence of AI technology. Before this turning point for intelligent capital, many companies are reassessing their capital expenditure plans. As the market cautiously examines investment returns, many companies are beginning to cut committed capital expenditures to cope with future uncertainties. Standing at the starting point of 2025, the global artificial intelligence industry is undergoing a critical shift from scale expansion to value cultivation. The record-breaking $100 billion in financing in 2024, the diversified exploration of technological paths, and the reshaping of the global competitive landscape collectively outline a new picture of industry development.

The Logic of Capital is Changing: From Scale Expansion to Value Focus

In 2024, the global AI sector's financing scale exceeded $100.4 billion, setting a new record. Mega-scale financing (single deals exceeding $100 million) accounted for 69%, with leading companies such as OpenAI and Anthropic becoming the focus of capital pursuit. This phenomenon reflects both the rigid demand for infrastructure investment in computing power and data, and investors' emphasis on technological barriers. Notably, early-stage investment has increased to 74%, indicating that while capital is betting on giants, it is also positioning itself for the next generation of innovation ecosystems.

The adjustment to investment structure is even more significant

the proportion of investment in vertical fields (such as fintech and digital healthcare) has decreased from 38% in 2019 to 24% in 2024, with capital shifting its focus to infrastructure and horizontal technology platforms. This change stems from the strategic layout of the upstream industry chain triggered by generative AI—investors are more inclined to support general-purpose technology foundations that can empower multiple fields, rather than application development limited to a single scenario.

Technological Breakthrough: Multimodal and Lightweight Parallel Development

Faced with the intelligence ceiling of large language models, multimodal technology has become the key to breaking the deadlock. The human characteristic of perceiving the world through the five senses is driving AI to evolve from single text processing to multi-dimensional cognition including images, videos, and voice. Pan Helin points out that by 2025, AI companies will accelerate their multimodal deployments through self-development or mergers and acquisitions. For example, the computing power required to generate video is several times that of text generation, which brings both technological challenges and opportunities in niche areas such as computing chips and heterogeneous computing.

Meanwhile, lightweight, low-barrier-to-entry technology routes are emerging. Some companies aim to achieve the highest application performance with the lowest computing power consumption, lowering the user threshold by optimizing algorithms. For example, copywriting generation tools for self-media creators have rapidly captured the C-end market through minimalist interactive design, indicating that AI applications will shift from a "technology competition" to a "user experience competition."

A Turning Point Signal: The Divergence Between Bubble and Value

The current AI field is at a turning point, transitioning from a typical "frenzy period" to a "collaboration period." As seen in the references, the core contradiction at this stage lies in:

1. Capital Bubble:

The valuations of some AI projects are inflated. For example, the news that "Nvidia invested $100 billion in OpenAI" was over-interpreted by the market; in reality, it was mostly a replacement of equipment or services. A Zhihu (a Chinese Q&A website) answer pointedly stated: "By 2025, investment in AI will account for 40% of the US GDP growth, but related companies' actual revenue will only be $35 billion."

2. Value Differentiation:

Companies with genuine technological barriers and closed-loop business models are beginning to emerge. For example, Xinghaitu, a company in the field of artificial intelligence, has maintained a calm expansion while the industry was generally losing money, through its "elite team + precise business computing" model.

3. Core Signal:

When capital begins to shift from "computing power-only" to "application-only," and from "storytelling" to "detailed accounting," the turning point has arrived.

How to Transform?

1. From Information to Insight: A Fundamental Shift in Investment Logic

In today's highly transparent information environment, mere information no longer constitutes an advantage.The real competitive advantage lies in—how to transform information into insight.Algorithmic trading systems, data analysis platforms, and sentiment monitoring models have become standard tools for institutional investment.

However, the true value of intelligent investment lies not in "speed," but in "structural judgment."For example, while traditional investors are still analyzing quarterly financial reports, intelligent systems are already predicting profit trends through supply chain data, consumer behavior, and real-time price fluctuations;When market sentiment fluctuates wildly, quantitative models can instantly identify overreaction risk zones and adjust positions accordingly.

This shift from "passive analysis" to "active prediction" makes investment more scientific and strategically deeper.In the future, investment decisions will rely less on subjective experience and more on data verification and logical deduction.

2. AI and Capital Efficiency: A Revolution in Decision-Making Speed and Cost

The essence of capital is "efficiency," and AI technology is revolutionizing efficiency.Intelligent investment research systems can analyze hundreds of reports in minutes;Risk models can monitor market fluctuations in real time and automatically trigger profit-taking and stop-loss orders;

Portfolio rebalancing no longer requires manual intervention but is dynamically optimized by algorithms.The impact of these changes is not just increased speed, but more importantly—reduced costs and improved consistency.Institutions no longer rely on large human teams but on algorithmic models for 24/7 management.

This structural cost reduction allows more capital to be invested in strategic research and innovation.In the long run, this trend will lead to more efficient resource allocation in global capital markets.Winners will no longer be those with "more information," but those with "more accurate models."

3. The Dividing Line Between Technological Bubbles and Real Value

Every technological revolution brings bubbles and misunderstandings.The capital market in 2025 will be no exception.The popularization of the concept of artificial intelligence has led many companies to enter the market under the banner of "intelligentization" and "automated decision-making."However, companies with genuine data accumulation and algorithmic capabilities remain a minority.

A rational return of capital is underway: The market no longer pays a premium for "technological narratives" but demands to see "technological achievements" and "commercial implementation."Technology companies that cannot translate innovation into sustainable profit models will be quickly eliminated.

Investors should also be wary of emerging technology sectors: Technological breakthroughs do not necessarily equate to profit growth,and profit growth often depends on the depth of integration between technology and industry.The true benefits of technological innovation come from efficiency, not concepts.

Conclusion

In 2025, the AI industry is transitioning from a "concept bubble" to "value realization." Capital is returning to rationality, technological paths are diversifying, and global competition is intensifying. For companies, core competitiveness lies in: the ability to build barriers in the computing power arms race, the ability to break through application boundaries through multimodal technologies, and the ability to achieve a closed-loop business model in specific scenarios. For investors, the focus should be on talent reserves, the uniqueness of innovation paths, and global deployment capabilities. AI is essentially a tool; its value lies in scenario innovation. Companies that can deeply embed technology into traditional sectors such as manufacturing, agriculture, and education will ultimately establish long-term value through cyclical changes.