Raymond Tan maintains a long-term holding attitude towards mature Chinese technology companies such as Tencent and Alibaba. He stated that these companies have clear business models, solid cash flow foundations, and well-defined market positions, demonstrating strong risk resistance in complex environments. They are suitable as the 'core allocation' of Chinese technology assets.
According to Zhitong Finance APP, on January 16, Raymond Tan, Chairman of Singapore Taixin Global Financial Group, a hedge fund company, was interviewed by Xinzhi Fund Network. During the interview, Raymond Tan stated that in terms of investment value, the biggest advantage of Chinese technology stocks currently lies in their relatively low valuation levels. In contrast, the U.S. technology sector has experienced significant valuation expansion over the past few years, with overall pricing at a higher range. Meanwhile, although Chinese technology companies are still developing steadily, their market valuations remain relatively reasonable, providing global capital with an important window for diversified allocation. The recent inflow of some international funds back into the Chinese capital market is itself a noteworthy signal.
In terms of specific allocations, Raymond Tan maintains a long-term holding attitude towards established Chinese technology companies such as Tencent and Alibaba. He expressed that these companies have clear business models, solid cash flow foundations, and well-defined market positions, which equip them with strong risk resistance in complex environments, making them suitable as 'core allocations' within Chinese technology assets. On the other hand, he adopts a more cautious approach toward emerging technology companies in the AI field. He believes that while this area holds great potential for imagination, new companies face numerous variables, including regulatory policies, the speed of technological iteration, commercialization capabilities, and market acceptance. Additionally, many enterprises have yet to achieve stable profitability but have already been assigned high valuations, presenting risks of bubbles and volatility that cannot be ignored. Therefore, he prefers observation and selection rather than blindly intervening at an early stage.
Entering 2026, as Germany and China's economies gradually stabilize, non-U.S. markets regain attractiveness. With the world entering a multipolar phase and interest rate spreads converging, Raymond Tan believes there are several key directions to focus on in asset allocation: First, the proportion of non-U.S. assets should be increased, particularly German and Chinese markets that are relatively reasonably valued and in the recovery phase. Second, the importance of physical assets like gold significantly rises. In an environment of inflation uncertainty, unresolved geopolitical conflicts, and declining policy credibility, demand for safe-haven assets will continue to strengthen; gold is no longer merely a 'hedging tool' but an essential source of stability in portfolios. Third, emerging markets, under the combination of 'high domestic interest rates and possible U.S. easing,' are seeing improved risk-reward profiles, and once the dollar enters a structurally weakening phase, the elasticity of this sector will become more pronounced.
Regarding U.S. assets, Raymond Tan emphasized that it is not about complete avoidance but exercising greater caution. Although core U.S. technology leaders still command high valuations, their technological monopolies, scale advantages, and policy support remain robust, ensuring irreplaceable long-term allocation value even after adjustments. However, investment prerequisites should shift towards selecting targets carefully, controlling timing, and managing position sizes, rather than continuing the previous 'passive overcrowding' allocation method.
Below is the full transcript of the exclusive interview by Xinzhi Fund Network:
PART I A 43-Year Investment Journey in Sync with the Times
Xinzhi Fund Network: Could you share with us when you officially entered the investment circle?
Raymond Tan: My professional starting point can be traced back to 1978, when Japan held a leading position in the Asian financial market. I began my career in buying and selling transactions at a Japanese futures brokerage firm, then served in the Singapore Air Force as a fighter pilot. In early 1982, after completing my service, I officially returned to the financial industry, embarking on what has become a lifelong professional journey in finance.
If we exclude the initial preparatory period, I have accumulated approximately 43 years of practical investment experience, having fully witnessed the transformation of major Asian financial markets from being closed to becoming fully open, and also experiencing the profound evolution of investment methods from traditional frameworks to becoming technologically advanced and systematized.
Xinzhi Fund Network: Was your entry into the investment field influenced by your personal academic background or driven by family circumstances?
Raymond Tan: My career choice was not influenced by my family background but rather a natural progression into the financial industry driven by the trends of the times. I entered the financial sector in the 1980s, during which the United States and the United Kingdom strongly promoted neoliberal reforms. The core of these reforms involved relaxing border controls and advancing market and capital liberalization. Meanwhile, Singapore had already initiated its opening-up process as early as 1975.
With the advancement of border and trade liberalization, Singapore, as a major global trade transshipment hub in Asia, experienced rapid growth in trade volume, leading to an increased demand for financial services. Driven jointly by policy guidance and shifts in the international landscape, Singapore gradually established itself as a key financial center in Asia, providing me with an opportunity to enter the industry.
Before entering this field, I had only received basic economics education and had almost no systematic training in finance or investment-related expertise. My investment knowledge primarily came from hands-on work experience. At that time, the government actively promoted the development of financial talent and specially introduced a two-and-a-half-year investment analysis program. To enhance my professional capabilities, I voluntarily enrolled in the course. Thus, my investment knowledge framework was entirely built around practical needs, with a high degree of integration between practice and theory.
In the 1980s, Singapore began promoting the development of its financial markets to align with the rapidly expanding foreign trade. Starting with early commodities futures and gold trading, the acceleration of globalization made foreign exchange the dominant trading product. To improve the market's ability to manage investment risks, options tools were subsequently developed, and Singapore established the International Monetary Exchange during this period, becoming a significant options trading center in the region.
Entering the 1990s, globalization further deepened, and the free flow of capital brought notable impacts. Rapid inflows and outflows of hot money made emerging markets more susceptible to volatility, leading to successive localized financial crises, culminating in the 1997 Asian financial crisis, which extended to the subsequent Latin American crisis.
In the 2000s, new technologies drove rapid expansion in financial activities, triggering the burst of the internet bubble; later, the global financial crisis erupted in 2008. In this series of systemic risk events, the market's demand for risk management continuously increased, and available risk management tools and hedging strategies gradually matured and became widely adopted.
The quantitative easing policies following the financial crisis injected unprecedented liquidity into global markets, making asset price bubbles a new norm and reshaping the structural landscape of investment markets. Subsequently, additional liquidity injections during the pandemic era, coupled with weakened technological regulation capabilities, further accelerated changes in market dynamics, forcing investors to reassess their risk management frameworks and seek new methodologies in an ever-changing market environment.
Throughout my career, I have not only witnessed multiple iterations of Asia's financial industry but also personally experienced the complete evolution process from emerging to mature markets. In my early years, I worked successively in Singapore, Hong Kong, China, and Taiwan, China, experiencing the gradual opening up of local financial markets. From initial commodities futures to the rise of stock and foreign exchange trading, and then to the application of more complex derivative tools such as options, I have witnessed the continuous improvement of market infrastructure and institutional systems along the way.
PART II Investment Strategy: Dynamic Iteration from Allocation to Quantitative Approaches
New Wisdom Fund Network: We understand that you currently manage hedge funds. Could you share your core investment strategy with us? What role does gold play in your investment portfolio?
Raymond Tan: Our core investment philosophy is built on a systematic global macro perspective. We do not rely on a single market direction or a single asset but combine long-term structural trends with short-cycle market changes. Through a scenario-based approach, we assess which assets and strategies are more advantageous in different macro environments and accordingly implement cross-asset allocation and multi-strategy positioning.
The purpose of a multi-strategy framework is to allow different strategies to play their roles at different market stages, enhancing the overall robustness and sustainability of returns through portfolio complementarity, rather than relying on one judgment being consistently 'correct' over the long term.
At the execution level, compared to traditional investment methods that focus primarily on long-term holding, hedge funds place greater emphasis on sensitivity to market changes and risk management capabilities. Therefore, we continuously track changes in the macro environment and market structure, revising and optimizing investment models to ensure strategies can adapt to rapidly changing market conditions.
For investors who already have long-term asset allocation, multi-strategy hedge funds serve as an important supplement, addressing risk ranges that traditional assets struggle to handle, thereby enhancing the stability and resilience of the overall portfolio.
In recent years, our allocation to and returns from gold have indeed increased significantly, which might lead to the misconception that we focus primarily on gold as an investment. In fact, gold is merely a strategic tool within our diversified asset framework.
The recent increase in gold's weight in the portfolio is mainly due to its hedging properties and liquidity characteristics aligning better with our current strategy needs under specific macro conditions, rather than a deliberate preference for any single asset class. As market scenarios change, the allocation weight of gold will also be dynamically adjusted, which is precisely part of our multi-strategy, scenario-driven investment framework.
New Wisdom Fund Network: We noticed that your investment experience seems to be divided into three stages: buy-and-hold, macro hedging, and quantitative investing. Has your managed product truly gone through such segmentation? What is the logic behind this transformation?
Raymond Tan: Your summary is very accurate, but it is not a strict 'segmentation' in the literal sense—it’s more of a dynamic adjustment driven by the changes of the times. When I first entered the fund industry, the market was still very immature, and everyone was doing simple asset allocation, or 'buy-and-hold.' At that time, there were not many sophisticated tools available, so decisions were made based on observing long-term market trends.
In the 1990s, the acceleration of globalization, capital flows, and financial leverage expansion made markets more susceptible to shocks, with various crises occurring frequently, making risk management the central theme. As the investment framework was redefined, the concept of hedging gradually gained prominence.
Coming from a trading background, I was more sensitive to market volatility and realized earlier that relying solely on long-term allocation could no longer address the new market environment. Therefore, I naturally transitioned from a singular allocation mindset to hedging and arbitrage strategies, managing risks more proactively and building a more resilient investment system.
The 2008 global financial crisis was a pivotal turning point for the capital markets. With large-scale government intervention in the markets, quantitative easing became the norm, injecting unprecedented liquidity into global markets and leading to a clear trend of asset price bubbles. In the era of quantitative easing, the disconnect between asset prices and fundamentals became increasingly evident, making it difficult for traditional macro analysis to keep pace with market changes. Consequently, I began incorporating quantitative methods such as momentum and factor analysis to gain a more objective understanding of market structure and sentiment. Over the past decade, I have systematized and quantified the investment process, transforming information gathering, data processing, and analytics into repeatable models and procedures to enhance accuracy and strategy verifiability.
In recent years, with the rise of AI, the concept of quantification has been further upgraded. We are no longer limited to actuarial calculations based on numbers and statistics; instead, we have begun attempting to quantify macro knowledge through language models—such as converting traditionally unstructured factors like economic policies and geopolitical dynamics into signals that models can identify, allowing strategies to more comprehensively reflect the true structure of the market.
More interestingly, new developments emerged in 2025, as Trump once again disrupted the global landscape, completely disrupting the rhythm of policy implementation and信息发布 systems. In the past few months, even the most basic data could not be obtained in a timely manner. Under these circumstances, we had to revert to early-stage allocation thinking, but this is no longer passive long-term holding in the traditional sense—it is an active, strategic 'comprehensive core allocation.'
For example, stocks and gold must serve as foundational allocations during periods of information blockages and heightened noise, much like reinforcing city defenses. As long as the core logic remains intact, the entire investment system will remain stable, avoiding structural errors.
Looking back over the past 40-plus years, my investment methodology has never been a fixed formula but rather a continuously evolving process adapting to market environments, policy changes, and technological advancements. From macro analysis to hedging concepts, from traditional quantification to AI-driven knowledge quantification, and to proactive allocation during special periods, continuous adjustment and upgrading are the fundamental reasons why we have been able to navigate through cycles.
New Wisdom Fund Network: You mentioned 'multi-strategy' earlier. Could you elaborate on how multi-strategy is implemented in the current market environment? What core problems does it address?
Raymond Tan: The current market exhibits characteristics such as high volatility, incomplete information, rising policy uncertainty, and rapid structural changes. In this environment, the effectiveness period of any single strategy is significantly shortened, making it difficult to consistently generate stable returns. Therefore, we adopt a multi-strategy investment framework, allowing different strategies to play to their strengths under varying market conditions. Through structured portfolio construction, we reduce the risk of failure for any single strategy in highly uncertain environments.
It is important to emphasize that this diversification is not simply about allocating funds across different assets but involves designing and deploying corresponding strategies for different types of risk sources. Our capital allocation spans multiple strategy dimensions, with each type of strategy having its own independent model, rules, and risk control system, ultimately integrated into a unified portfolio framework. This framework can effectively control drawdowns during short-term market shocks through dynamic adjustments and risk hedging and, when necessary, quickly switch to core assets, defensive allocations, or event-driven strategies. Even if individual models fail temporarily, the overall portfolio can maintain relatively stable operation.
Strategies maintain low correlations with one another, enabling them to complement each other when one strategy is under pressure, thereby enhancing the overall resilience and sustainability of the portfolio. In practical execution, we combine various strategies such as global macro, arbitrage, momentum, event-driven, and risk management, enabling the portfolio to handle short-term volatility while maintaining long-term return stability.
Within this multi-strategy system, quantitative tools are an indispensable component. With technological advancements, quantitative methods have become crucial for identifying risks and mitigating uncertainty. However, we do not rely on models for automated decision-making; instead, we use quantification as an auxiliary tool to capture short-term risk changes, market sentiment, and structural signals. Final investment decisions remain centered on macroeconomic conditions and policy directions, combining the precision of quantification with the directional judgment of macro analysis. This enhances decision quality while avoiding being overly influenced by short-term data noise.
PRRT III Team Operations: A Human-Machine Integrated Decision-Making System
New Wisdom Fund Network: In current investment transactions, does your team adopt an individual-led, team-collaborative, or algorithm-driven automated model? What responsibilities do different roles bear in the decision-making process?
Raymond Tan: We follow a typical team-based collaborative model and do not rely on full automation. Macro-level perspectives are crucial; a team must have its own clear 'View.' My core role is to provide macro judgments at the front end—tail risks and return expectations. For example, based on current geopolitical developments and economic policies, I assess the broader market direction and set reasonable return targets while managing tail risk expectations.
Building on this, we have a dedicated quantitative team whose primary task is to focus on factors affecting short-term risks. They gather information and capture market signals in a broader manner. While we do use automated tools, we do not depend entirely on them. For instance, data collection can be automated, but decisions about which data to collect, how to validate it, and how to process it require human judgment. We do not directly base decisions on raw data outputs; instead, data serves to verify my macro views, ensuring my assessments align closely with actual market conditions without significant deviation.
The entire decision-making process is systematic: I first propose macro views, tail risks, and return expectations. The intermediate team then collects data, analyzes it, verifies my viewpoints, and suggests adjustments. Afterward, they feed the processed data results back to me, and I make the final decision by integrating these insights. This approach leverages the team’s professional strengths while ensuring continuity and accuracy in decision-making.
New Wisdom Fund Network: How do you balance conflicts between personal macro judgments and data-driven support during the decision-making process? If your personal view differs from the data outcomes, how would you handle it?
Raymond Tan: My core belief is that 'personal judgment is not necessarily correct,' so I never reject corrections or recalibrations based on data. Hence, we have established a systematic review and validation mechanism where the data team independently tests my views, comparing macro judgments with actual data to identify discrepancies. Together, we explore the sources of deviations—whether I overlooked certain factors, the data itself is biased, or new variables have emerged in the market.
For example, I might predict that a particular industry will enter an upward cycle based on macroeconomic data. However, the data collected by the team regarding market sentiment and capital flows may indicate the opposite trend. In such cases, we avoid blindly trusting either side and instead conduct further verifications: Is there a lag in the economic data? Or has market sentiment been distorted by short-term events? Through this iterative calibration, our final decisions align both with macro logic and real market conditions.
Our organizational structure operates around this principle as well: the technology team handles data processing, model development, and tool construction; the macro research team focuses on the macro framework, economic logic, and structural analysis; and I am responsible for integrating the findings from both teams to make trend assessments and ultimate strategy decisions based on an understanding of the data. This cross-team, multi-method collaboration allows us to continuously refine our cognition, avoid subjective biases, and build a more robust investment system.
PART IV Scale and Returns: Striking a Balance Amidst Contradictions
Xinzhi Fund Network: There is a widely held view in the industry that product returns tend to decline as the scale expands. Has your product encountered such an issue? How do you address the challenges brought by scale expansion?
Raymond Tan: It is indeed common in the industry for returns to decrease as the scale of a product grows, but this fundamentally depends on the investment strategy and asset class adopted. For traditional equity funds focusing on individual stock selection, the number of high-quality targets with sufficient liquidity and attractive valuations is limited. As the fund size increases, they often face significant 'diminishing marginal returns,' which can lower overall returns.
Our situation is somewhat different. First, at the asset allocation level, we are not limited to a single equity strategy but cover multiple highly liquid markets, including equity indices, currencies, and commodities. These assets have ample capacity and are better suited to accommodate scale growth. Second, in terms of strategy, we use a multi-strategy portfolio approach rather than relying on a single concentrated strategy, giving our capital deployment greater flexibility and selectivity. As a result, overall returns do not naturally decline as the scale expands.
Looking at our development history, three decades ago, when our client base was smaller, operating with a single strategy did not pose significant limitations. As the number of clients and the scale of funds gradually grew, we proactively adjusted our investment model by introducing a more diversified strategy system. Currently, our total managed assets are approximately $1.5 billion, and under our existing strategies and asset structure, further expansion does not present substantial pressure.
Additionally, we have implemented clear tiered management for our clients. For clients with larger capital volumes, we adopt a separately managed account model, customizing investment strategies based on their risk preferences and objectives to avoid excessive concentration of large-scale funds within a single product. This arrangement not only ensures the stability of returns for existing clients but also allows room for orderly scale expansion, achieving long-term, sustainable growth.
Xinzhi Fund Network: In response to the shift from low inflation to high inflation and increased market volatility, how do you set return targets for clients to ensure return stability?
Raymond Tan: Our approach to setting return targets has always followed a very clear and pragmatic principle—first defining achievable stable return goals, then reverse-engineering the implementation path, rather than passively chasing market performance. This philosophy can be traced back to the aftermath of the Asian financial crisis in the 1990s.
At that time, many investors, particularly retirees, developed a deep aversion to stock market volatility. Their primary concern was not short-term high returns but predictable and sustainable cash flow and asset stability. It was against this backdrop that we abandoned the traditional mindset of 'beating the market' and instead set clear annual return targets. Through disciplined execution and strict risk control, we achieved returns in a more controlled, 'engineered' manner.
In terms of execution, we break down annual targets into quarterly objectives, treating the entire investment process as the accumulation of multiple stages rather than a one-time outcome assessment. This approach helps mitigate the impact of volatility at any single point in time, which is especially important in high-inflation and high-volatility market environments because it emphasizes pacing and process control rather than betting on the final market performance.
All investment decisions operate within a structured scenario framework rather than being made impulsively. We believe that market price movements are often driven by evolving global macro factors. Therefore, we identify potential market inefficiencies through a macro perspective and position ourselves around key events in advance, such as monetary policy direction, interest rate paths, trends in major macroeconomic data, or the impact of political and policy environments on market structure and risk appetite. Before entering an investment, potential risks, return ranges, and outcome pathways are clearly defined.
In terms of strategy implementation, we adopt a multi-strategy portfolio, with different strategies adapted to different market environments. Execution is strengthened through quantitative rules to minimize the interference of emotions and subjective judgments on outcomes, thereby enhancing overall performance consistency and repeatability.
Over the past few decades, it is precisely this goal-setting and execution system centered on stability that has helped us maintain relatively steady performance across various inflation cycles and market environments. It has also enabled us to build highly stable customer relationships. Many clients have stayed with us from the early days, even extending to the next generation—this, in itself, serves as the most direct and powerful long-term validation of our stable return strategy.
PART V Global Perspective: Chinese Assets and the US-China Competitive Landscape
New Wisdom Fund Network: You are a native Singaporean. Could you share with us when you started paying attention to Chinese asset investments? What were some of the key opportunities and experiences behind this?
Raymond Tan: I have deep ties with the Chinese market. As early as the 1990s, during the initial stages of China's opening-up, I recognized its enormous potential for development and was among the first to enter the real estate investment sector. At that time, private equity (PE) had not yet matured as a concept and was more akin to early exploration. Shenzhen, as a Special Economic Zone just starting out, was still figuring out policies and market rules. We moved forward amidst an imperfect institutional environment, inevitably encountering some setbacks along the way.
After 2000, I gradually exited direct investments, primarily due to the lack of local resource support, which placed foreign investors at a disadvantage in the Chinese market. However, I did not completely leave the Chinese market. During this period, I met many local Chinese investment professionals and collaborated with them in an advisory capacity, providing macro-level insights and recommendations. For instance, I helped them grasp market cyclical changes and assisted in formulating investment decisions from a relatively conservative and prudent perspective.
Over the years, I have kept an eye on A-shares and H-shares and held shares in some Chinese technology companies. The appeal of the Chinese market continues to grow, especially in recent years as China accelerates its technological layout, revealing new structural opportunities.
New Wisdom Fund Network: Currently, competition between the US and China in the technology sector is intensifying. How do you view the investment value of Chinese technology stocks? What differences exist in your investment approach toward companies like Tencent and Alibaba, as well as emerging firms in the AI field?
Raymond Tan: This year, two critical shifts have occurred in the global technology competition landscape. One is that under Trump’s administration, the US brought what was previously a relatively implicit competition fully into the open, whether in trade, technology, or industrial policy—it has been brought directly to the forefront. The other is that China is no longer merely passively responding or symbolically participating but has genuinely and systematically engaged in technological competition. This shift in dynamics has profoundly impacted the logic of global capital allocation.
From an investment value perspective, the biggest advantage of Chinese technology stocks currently lies in their relatively low valuation levels. In contrast, the US technology sector has experienced significant valuation expansion over the past few years, with overall pricing now at a higher range. Meanwhile, Chinese technology companies continue to develop steadily, but their valuations remain relatively reasonable, providing global funds with an important opportunity for diversified allocation. In fact, in recent months, there have been observable inflows of international capital back into the Chinese capital market, a signal worth noting in itself.
In terms of specific allocation, I maintain a long-term holding attitude toward mature Chinese technology companies such as Tencent and Alibaba. These companies have clear business models, solid cash flow foundations, and well-defined market positions, demonstrating strong risk resistance in complex environments. They are suitable as the 'core allocation' of Chinese technology assets.
In contrast, I am more cautious about emerging technology companies in the AI field. While this area is indeed full of potential, new companies face numerous variables, including regulatory policies, the speed of technological iteration, commercialization capabilities, and market acceptance. Moreover, many enterprises have not yet achieved stable profitability but have already been assigned high valuations, making risks of bubbles and volatility impossible to ignore. Therefore, I prefer observation and screening over blind intervention at an early stage.
Overall, I believe that the main axis of future technological competition will be highly concentrated between China and the United States. Although the U.S. currently leads in multiple key areas, China's technology industry, driven by policy support and vast market demand, is developing rapidly and holds long-term potential. Thus, in allocating Chinese technology assets, we will continue to focus on mature enterprises while closely monitoring policy directions and industrial rhythms, dynamically adjusting our layout rather than chasing short-term concepts.
PART VI The AI Era: Restructuring Investment Logic and Assessing Bubbles
Xin Zhi Fund Network: Comparing with the Internet era of 2000, do you think the current AI era may experience a similar bubble burst? Could leading technology companies like NVIDIA be replaced by new technologies or companies?
Raymond Tan: Many people tend to compare the current development phase of AI with the Internet boom around 2000 and worry about a repeat of large-scale bubble bursts based on such analogies. However, I disagree with this simplistic comparison because there are fundamental differences in the historical context, industrial structure, and value realization paths between the two eras.
The core issue of the 2000 Internet bubble was not the incorrect direction of technology but a severe mismatch between profit realization and valuation expansion. At that time, the Internet did represent a long-term trend, but the market prematurely reflected decades of growth expectations into valuations all at once. However, the actual environment was still dominated by the 'old economy,' with limited Internet penetration and immature application scenarios; companies' business models and profitability lagged far behind the capital market's expectation expansion, ultimately causing an imbalance in the valuation system and completing adjustments through a bubble burst.
In contrast, the current AI cycle exhibits distinct 'top-down' characteristics. AI applications are not primarily aimed at ordinary consumers but instead serve a few large enterprises with core capital, computing power, and decision-making authority. The efficiency improvements and process restructuring achieved through AI have already translated into tangible profits on real financial statements, rather than remaining at the conceptual or long-term assumption level. This means that the valuation foundation of current AI is much more solid than during the early days of the Internet, with systemic bubble risks effectively offset by realized profits.
A deeper difference lies in the dramatic change in the policy environment. After the 2008 financial crisis, governments worldwide became more proactive and sophisticated in regulating asset bubbles and economic cycles. In 2000, policies generally allowed bubbles to complete their liquidation through market-driven violent crashes. Today, however, AI has become a core domain in major-power competition, and governments will not allow uncontrolled collapses in key technological fields. Instead, they will maintain the stable development of leading enterprises and core ecosystems through industrial policies, fiscal support, and regulatory coordination.
As for whether leading technology companies like NVIDIA could be quickly replaced, I believe this possibility is extremely low. Such enterprises are no longer just single technology suppliers but have formed highly complex competitive barriers across multiple dimensions, including technology, policy collaboration, capital, industrial chains, branding, and ecosystem systems. By establishing long-term partnerships with national institutions and large enterprises, they have locked in profit margins for the next several years in terms of computing power, supply chains, and technical standards. In contrast, those small AI companies lacking core technologies, capital support, and relying mainly on conceptual narratives are the ones truly facing elimination risks. During the process of intensifying competition and increasingly rational capital, these enterprises are likely to be the first to exit the market.
Overall, I believe the current AI cycle is more akin to a highly concentrated, policy-supported, profit-driven structural upgrade process, rather than the expectation-driven, uncontrollable bubble scenario witnessed in 2000.
Xinzhi Fund Network: What changes has AI development brought to the traditional investment valuation system? What new logic should we use to assess corporate value in the AI era?
Raymond Tan: The rise of AI is fundamentally reshaping the traditional investment valuation system. In the past, we were accustomed to predicting a company's future based on the consumption capacity, employment rate, and income growth of the middle class—a logic that essentially rested on an economic model driven by individual consumption as the core engine.
However, in the AI era, this premise is being challenged: the large-scale application of AI not only significantly reduces companies' reliance on labor costs, enabling them to achieve profitability through process restructuring and technological substitution, but also weakens the support role of the traditional middle-class consumer market for the economy due to its replacement of high-paying standardized intellectual jobs. In other words, the focus of profit generation is shifting from 'demand-side expansion' to 'supply-side efficiency enhancement.'
From the perspective of industrial structure, leading technology companies are forming a highly concentrated, mutually reinforcing 'self-circulating profit' system. For example, NVIDIA's investment in OpenAI allows OpenAI, as a developer of large AI models, to sustainably drive up demand for computing power after securing substantial funding. This demand is primarily fulfilled through Oracle, a collaborative cloud computing platform.
Cloud service providers gain stable revenue while providing AI computing power services to model developers but are also compelled to continuously increase their investment in computing infrastructure, directly driving investments in high-performance GPUs and data center ecosystems. This expansionary demand ultimately flows back to upstream core hardware suppliers like NVIDIA, creating a steady source of orders and profits. The defining characteristic of this system is that demand does not come from dispersed end consumers but is continuously amplified within the system by a few enterprises and institutions with capital strength, technical capabilities, and scale advantages.
Meanwhile, AI is profoundly altering the employment and income structure of the middle class. In the past, entering high-paying positions through education and experience accumulation was a key foundation for the formation and expansion of the middle class’s consumption capacity. However, AI is now replacing a significant amount of standardized, replicable intellectual labor, reducing both the number and accessibility of high-paying jobs, thereby weakening the middle class's supporting role in economic growth and consumption. Against this backdrop, it is evidently necessary to rethink the traditional valuation system centered on consumption growth assumptions.
In the AI era, the core of enterprise value assessment has shifted from 'focusing on past profitability' to 'control over future resources.' We no longer rely solely on simple extrapolations of historical financial data but instead examine whether a company possesses irreplaceable technological barriers composed of core algorithms, computing architectures, and massive datasets.
More importantly, it is crucial to evaluate whether a company’s strategic direction aligns closely with national policy orientations and occupies a central position in critical sectors such as technological competition and energy transition. Finally, attention must be paid to whether a company secures long-term demand and relatively certain profit sources by controlling key nodes in the supply chain and establishing deep binding relationships with government agencies or industry leaders. In short, assessing a company’s value essentially involves evaluating the depth and breadth of its control over technology, power, and resources.
PART VII Market Outlook: Investment Directions for 2026
New Intelligence Fund Network: The year 2025 has passed, and looking back at last year, many significant events occurred, such as the bottom adjustment in the Chinese market, Trump's reciprocal tariff policies, geopolitical conflicts (India-Pakistan conflict, Israel conflict, etc.), and gold prices breaking through 4,000 USD/ounce. What are your forward-looking judgments for investments in the first half of 2026? Which assets should be given close attention? And what are the core risks?
Raymond Tan: From a macro perspective, an important change is taking place in the global investment environment, where capital no longer flows 'automatically' only to the United States.
The "There Is No Alternative (TINA)" logic that dominated markets in 2025 is significantly weakening. The continuous inflow of funds into U.S. assets was largely driven by the lack of better alternatives rather than consistently superior fundamentals. However, entering 2026, with Germany and China’s economies gradually stabilizing, non-U.S. markets are regaining attractiveness.
At the same time, the global interest rate environment is also changing: the easing cycles in Europe and many emerging markets are nearing completion or have concluded, while the U.S. may enter a more pronounced easing phase due to economic slowdowns. A clear narrowing of interest rate differentials in global monetary policy is reducing the dollar's advantages and increasing the motivation for capital to reallocate diversely.
Additionally, Japan’s gradual normalization of interest rate policies toward rate hikes implies that the carry trade of "borrowing yen to buy high-risk assets," which has prevailed for many years, carries reversal risks. Once unwound en masse, it could lead to amplified volatility in high-valuation risky assets.
As the world enters a multipolar phase with converging interest rate differentials, I believe several asset allocation directions deserve particular focus.
First, the proportion of non-U.S. assets should be increased, particularly those with relatively reasonable valuations and currently in recovery phases, such as the German and Chinese markets. Second, the importance of tangible assets like gold is rising significantly. In an environment of inflation uncertainty, unresolved geopolitical conflicts, and declining policy credibility, demand for safe-haven assets will continue to strengthen. Gold is no longer just a "hedging tool" but an essential source of stability within portfolios. Third, emerging markets are seeing improved risk-reward profiles under the combination of "relatively high domestic interest rates and potential U.S. easing." Once the dollar enters a structural weakening phase, this sector's elasticity will become more pronounced.
As for U.S. assets, they should not be completely avoided but approached with greater caution. Although the valuation of core U.S. technology leaders remains relatively high, their technological monopolies, economies of scale, and policy support remain robust. After adjustments, these assets still hold irreplaceable long-term allocation value. However, investment premises should shift toward selective targeting, timing control, and position management, rather than continuing the previous "passively crowded" allocation approach.
Another trend worth noting is the growing severity of wealth inequality. In a context dominated by hegemonism, individuals with resources and capabilities will gain even more advantages, while safety nets for vulnerable groups will gradually disappear, impacting overall consumption patterns and economic growth drivers. This reflects in investment strategies by focusing on enterprises with "power, resources, and technological monopolies," while avoiding assets reliant on traditional consumption-driven models.
Regarding risks, the most critical short-term concern remains whether inflation rebounds. If inflation rises again, central banks may be forced to tighten policies, impacting market liquidity. Deeper risks stem from policy frameworks and market structures themselves.
The US's long-term reliance on fiscal stimulus and policy support is being constrained by high debt levels and political cycles, and market confidence in 'the government will always bail out the market' is no longer as strong as it used to be. If the interest rate advantage of the US dollar diminishes too quickly, it could trigger a rapid withdrawal of funds from US Treasuries and overvalued assets; combined with the concentrated unwinding of yen carry trades, this could easily lead to amplified market volatility.
Finally, due to the lag and distortion present in economic data over the past period, once the true growth and inflation conditions gradually emerge in the first half of 2026, the market may need to complete repricing within a short period, naturally leading to increased volatility.
