Follow the Money: Decoding AI’s Capital Stack

Key takeaways 

• AI capex boom is fundamentally different from dot-com era: Unlike the late 1990s, today’s hyperscaler investment is being driven by real, contracted demand, funded by strong balance sheets, and constrained by supply rather than excess capacity — making the cycle healthier, though still complex to underwrite.
• Timing and size of returns, not just spending, are critical: The sustainability of massive AI investment depends on whether companies can earn returns above their cost of capital through a mix of revenue growth (cloud, ads, enterprise demand) and operational efficiencies, and this varies significantly company by company.
• Evaluating AI requires collaboration across capital structure and sectors: Understanding risks around financing, energy availability, geopolitics, and off-balance-sheet structures demands close collaboration between equity, fixed income, and sector specialists to avoid blind spots and make better long-term investment decisions.

Artificial intelligence is driving one of the most capital‑intensive investment cycles in decades. Making sense of this moment requires moving beyond headline spending figures and familiar historical comparisons and instead focusing on how capital is funded, where returns are generated and how risks are distributed across companies and sectors. We believe selectivity and collaboration are becoming increasingly important as the AI capex boom evolves.

The AI capex boom is fundamentally different from the dot-com era 

Today’s AI investment cycle is meaningfully different from the dot-com era, despite surface-level similarities in scale and ambition. In the late 1990s, infrastructure was built speculatively and well ahead of demand. In the current cycle, hyperscalers are investing against real, visible, and contracted demand, particularly within cloud businesses. They are also supported by strong balance sheets and substantial cashflow generation. Importantly, the system remains supply-constrained rather than overbuilt, with limitations around chips, power, and datacenter capacity quickly absorbing new investments. While this does not eliminate risk, we believe it creates a healthier starting point than prior technology cycles, even as the complexity of underwriting outcomes continues to rise.

Practical Application: 
Investors should avoid broad analogies and focus instead on demand visibility, balance-sheet strength, and supply constraints. Distinguishing between companies investing to meet contracted demand and those pursuing more speculative buildouts is critical to managing downside risk.

The timing and size of returns, not just spending, are the critical component 

The sustainability of AI investment depends less on how much is spent and more on when and how returns are realized, and this varies significantly company by company. We believe some businesses have demonstrated a clear ability to convert AI capex into incremental revenue, while others face longer and more uncertain monetization paths. We also believe returns should not be viewed solely through a revenue lens. AI investment is increasingly delivering operational efficiencies, supporting margins and free cash flow through productivity gains and cost savings across engineering, IT, and back-office functions. As capex rises and free cash flow thins, we believe expectations are high and the margin for error is narrowing, increasing the importance of disciplined capital allocation.

Practical application: 
Focus on return profiles rather than headline spend, assessing whether companies can earn returns above their cost of capital through a combination of revenue growth and operational efficiencies. We believe this requires company-specific analysis of free cash flow durability and return timing. 

Evaluating AI requires collaboration across capital structure and sectors 

The rising capital intensity of AI means risk can increasingly emerge in less obvious places. Financing structures are evolving, with greater use of off-balance-sheet arrangements, partnerships, and long-dated commitments that shift where risk ultimately sits. At the same time, constraints around energy availability, infrastructure buildout, and geopolitics are becoming central to long-term outcomes. In our view, no single lens is sufficient: equity analysis highlights upside and operating leverage, while fixed income analysis focuses on resilience, funding stress, and downside risk management. Bringing these perspectives together, alongside insights from sectors beyond technology, is essential to avoiding blind spots.

Practical application: 
Investors should consider actively integrating equity, fixed income and sector expertise when evaluating AI exposure. Understanding financing structures, energy constraints, and geopolitical risks alongside operating fundamentals can lead to more robust long-term investment decisions.

Conclusion 

The AI capex cycle is real, accelerating, and led by companies with significant financial resources. However, in our view, outcomes will increasingly be driven by execution and fundamentals rather than narrative momentum. We believe investors must be selective, assessing the impact of AI investment on a company-by-company basis, with a clear understanding of funding structures, return profiles and risk allocation. Equally, we believe collaboration across capital structure and across sectors is essential to viewing risks and opportunities from all angles and building conviction in an increasingly complex investment landscape. 

The views expressed in this are those of MFS, and are subject to change at any time. These views should not be relied upon as investment advice, as securities recommendations, or as an indication of trading intent on behalf of any MFS investment product.

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