AI Income Investment Unveiled: Multi-Asset Strategy for Resilience and Cash Flow

The AI wave is boosting global stock markets, but heightened volatility stems from capital concentration in AI hardware stocks. Analysts indicate that the AI growth engine is gradually expanding from upstream infrastructure to end-user application layers, supported by solid long-term fundamentals. Amid high-level market corrections, experts recommend a multi-asset allocation strategy that balances capital appreciation with stable cash flow, enabling investors to adopt a balanced approach to capture long-term AI-driven market growth.

With rapid AI development and evolving global investment conditions, balancing growth opportunities with income and risk management has become a critical asset allocation challenge for investors. To address this, Chinex hosted a seminar titled 'Navigating Market Volatility: Income × Growth × Investment in the AI Era,' featuring fund expert Xiao Biyan, U.S. stock analyst Shi Yatang, and Yuanta Asset Management fund managers. They analyzed investment strategies in volatile markets, covering market trends, AI industry prospects, and multi-asset allocation.

Taiwan's stock market has repeatedly hit new highs, with AI as the primary driver.

Xiao Biyan noted that Taiwan's stock index took 34 years and 10 months to rise from 10,000 to 20,000 points, but only over a year to climb from 20,000 to 30,000 points—the biggest force behind this surge being AI. However, with AI-related themes flourishing, the real investment key isn't simply 'buying AI' but selecting assets with long-term growth potential.

Regarding concerns about an AI bubble, Xiao emphasized that instead of guessing when a bubble might burst, investors should first clarify their tolerance for depth and duration of market corrections. Reflecting on the 2000 tech bubble and the 2008 financial crisis, even investors who entered at relatively high levels had opportunities to recover over time. Thus, long-term, phased investors need not panic excessively over short-term fluctuations.

She also cautioned that the purpose of buying more during downturns should be to earn greater returns, not to average down losses. Only high-quality assets with long-term investment value are worth adding to during pullbacks.

Market 'Two Good, One Bad': Long-Term Themes Strong, Short-Term Volatility Rising

Yuanta Asset Management characterizes the current market environment as 'two good, one bad.' The first positive factor is that AI has entered its investment golden age. As infrastructure expands into diverse applications, the industry's growth cycle could last over 10 years. However, during this disruptive innovation phase, past industry leaders may not maintain dominance, making stock selection increasingly dependent on identifying the next generation of industry winners.

The second positive factor is rising global protectionism, shifting supply chains from efficiency-focused global division to localized production emphasizing resilience and security. To reduce geopolitical and supply chain disruption risks, companies are increasing backup inventories and local manufacturing, boosting demand in traditional industries, infrastructure, and manufacturing. Combined with AI-driven enhancements, the overall industry growth potential continues to expand.

In contrast, the biggest 'bad' factor is potential volatility from capital concentration. Yuanta points out that retail deleveraging in South Korea, sharp corrections in strong U.S. stocks, and Taiwan's brief sharp drop followed by a rapid rebound all reflect how concentrated investments in AI hardware amplify market volatility during profit-taking or deleveraging. Therefore, the current investment key isn't just choosing the right direction but also having the resilience to withstand volatility and remain invested long-term.

AI's Long-Term Momentum Continues: Capital Expenditure as Key Indicator

Regarding how long the AI rally can last, Shi Yatang draws parallels with internet development history. The internet entered commercialization in 1995 and saw a five-year bull run before the 2000 bubble burst. In contrast, ChatGPT emerged in late 2022, sparking the AI boom in 2023—about three years ago. If a similar trajectory unfolds, the AI investment boom could last until at least 2028, offering strong performance potential for both Taiwan and U.S. stock markets.

Looking ahead, Shi identifies three key AI growth engines: continuous model upgrades, expanding capital expenditures by tech giants, and accelerated adoption of AI applications like AI Agents. Notably, AI is advancing toward AI Agents capable of proactively executing tasks. As technology matures and application scenarios expand, this could become another major growth driver within the next 1–2 years.

Meanwhile, AI computing models are undergoing structural shifts. Past model training involved one-time capital investments, but as applications deepen, computational focus is shifting toward the repetitive inference phase. This inference phase is widely penetrating diverse scenarios such as customer service, search, daily office work, and enterprise operations, driving massive and sustained computing power consumption. This creates a long-term, stable 'long-tail effect' in AI market demand.

Shi emphasizes that tech giants like Microsoft, Google, Meta, and Amazon continue to raise AI-related capital expenditures, with total 2024 spending projected to exceed $700 billion. He believes the most critical indicator for sustaining the AI rally is whether large tech companies continue to increase capital spending. Only when companies clearly reduce procurement should investors heighten vigilance against bubble risks.

AI Shifts from Hardware to Applications: Investment Opportunities Expand

Yuanta Asset Management believes that as infrastructure matures, industry development will shift from hardware construction to end-user applications and cross-industry integration, opening a longer investment cycle. Referring to NVIDIA's 'five-layer cake' framework, current market capital remains concentrated in the foundational hardware layer. As the computing base matures, the growth focus is expected to shift from 'building AI' to 'using AI,' expanding beneficiaries from hardware supply chains to model and application sectors.

Comparing the current AI boom to a baseball game, Yuanta estimates we're only in the 3rd or 4th inning, indicating substantial room for industrial growth. As AI moves from infrastructure to large-scale applications, market themes will diversify beyond chips and servers to include AI applications, critical resources, defense and aerospace, manufacturing upgrades, and AI healthcare.

Notably, the U.S. leads in AI applications. As end-user applications become widespread and usage increases, they will in turn drive tech companies' capital expenditures, boosting demand for Asian chips and key components, creating a positive cycle from applications to capital spending to supply chains. Thus, Asian AI supply chains remain fundamentally supported, and the next phase should gradually focus on U.S. application-sector investment opportunities.

Multi-Asset Diversification for Risk Management: Balancing Growth and Cash Flow

Despite the optimistic long-term AI trend, navigating mid-course volatility remains a key challenge for investors. Xiao Biyan notes that many investors chase popular products without first clarifying their needs—preferring high-dividend bond funds a decade ago and shifting to high-dividend ETFs recently. When market conditions change, performance gaps often lead to disappointment.

She believes each asset class has pros and cons. Bonds primarily provide stable cash flow through coupon income but offer limited capital growth. High-dividend ETFs generate income from stock dividends and can deliver both income and capital gains in bull markets, but dividends aren't guaranteed. Therefore, to balance cash flow and asset growth, investors should diversify across stocks, bonds, and ETFs rather than relying on a single income product, building a growth-oriented income portfolio.

Yuanta Asset Management also recommends a multi-asset strategy to balance growth and income amid rising AI market volatility. For example, the Yuanta Quality Income Multi-Asset Fund allocates 75%–85% to stocks and ETFs, focusing on long-term growth sectors like AI, industry, defense, and healthcare to pursue capital appreciation. Bonds are invested in relatively low-default-risk U.S. Treasuries to reduce overall volatility, supplemented by a covered call strategy to generate additional premium income.

Shi Yatang reminds investors to continue monitoring tech giants' AI capital expenditures, as well as Federal Reserve policy and U.S. Treasury yield changes. As long as AI capital spending and corporate earnings trends don't clearly reverse, AI will remain a key driver of global stock markets and industrial investment. However, as high volatility may become the norm, diversifying risk through multi-asset equity-bond portfolios and multiple income sources will be crucial for investors to sustainably participate in the AI wave.