Capital Allocation in the AI-Hardware Boom: Optimizing Long-Term Value

Introduction: Semiconductors at a Historic Juncture

AI’s global surge is reshaping how the semiconductor industry invests. McKinsey estimates fabs will pile up a staggering $1 trillion in investment by 2030, nearly all of it pushed by AI and advanced-node chips. By 2025, SEMI forecasts that AI will be driving half of sector expansion on its own. At 2nm and beyond, funding is going heavily into advanced logic, memory, packaging.

The CapEx playbook is being rewritten—investments no longer revolve solely around adding raw capacity. Instead, the focus is on smart, long-term allocation across compute, memory, packaging, and the architectural innovation necessary for AI-driven workloads.

AI-driven applications and hyperscale data center growth are fuelling a shift:

• Semiconductor CapEx priorities have expanded to include not just core manufacturing but a holistic focus on high-bandwidth memory (HBM), advanced packaging (like 2.5D/3D stacking), and compute acceleration.HBM sits at the heart of this, delivering unmatched data consumption performance with the most advanced memory stacks to date for ultra-high-speed and power-efficient AI workloads. This breakthrough will enable more tightly integrated CPUs, GPUs and memory modules than traditional scaling can achieve — breaking bottlenecks that the industry cannot scale out of and enabling new chiplet-based modular architectures.
• The CapEx intake of the past—aimed at stepping up production volume—is giving way to a new agenda: supporting novel AI architectures, pre-silicon design efficiencies, and next-gen system integration.This shift of CapEx priorities hints at the evolution within the industry towards continuous innovation to meet the evolving and complex requirements that come with AI at scale.

Changing CapEx Priorities: The New Move

1. Rapid AI Adoption Drives Smart Capital Allocation

• AI as a CapEx Catalyst: The past two years have seen AI take center stage as the pivotal force for semiconductor demand. SEMI notes that AI will be the key driver, accounting for half the chip market’s growth through 2030.
• $1 Trillion Ambition: McKinsey’s analysis shows nearly $1 trillion will be spent on new fabs through 2030, supplementing a push toward regional supply chain resilience and advanced-tech leadership.

2. Where the Money Goes: Compute, Memory, Packaging

• Compute: Capital is focused on enabling high-performance AI hardware—think GPUs, accelerators, and logic at 2nm and below.
• Memory: AI requires a high bandwidth memory and advanced memory subsystems, which transitions CapEx to scale next-gen memory.
• Advanced Packaging: Investment in 2.5D/3D and chiplet architectures is up significantly, which are the foundation for achieving future AI workload performance and power efficiency. Today, CapEx for packaging is roughly 15% of the total wafer-level spend at the leading edge and poised to increase further in the future.

3. Regionalization and Supply Chain Resilience

• As nations scramble to localize production, CapEx is increasingly distributed across the US, Europe, Southeast Asia, and China—balancing risk and regional security.
• Big government incentives (U.S. CHIPS Act) help defray upfront price of new fabs but don't fully makeup higher ongoing operational costs vs Asia.

4. Rising Complexity and Cost

• As node sizes shrink to 2nm and below, rising process complexity strains investment and supply chains, boosting demand for materials, talent, and strategic supplier alliances.

Smart CapEx and Real Results

Tech Mahindra has consistently demonstrated its ability to improve CapEx efficiency for some of the world’s most complex and high-stakes operations. A US integrated device manufacturer (IDM) engaged Tech Mahindra to revamp the design of its semiconductor subsystem and expand its supply chain. The project achieved 8-10% cost savings, 18-20% increase in productivity, and strategic diversification of over $600M in critical spend by adopting 35 alternate suppliers and deploying advanced AI-powered visibility tools.

In another example, partnering with a global industrial equipment major, Tech Mahindra rolled out an advanced spare parts taxonomy classifying 100,000+ parts across 14 commodities and $420M in spend. This approach provided precise inventory visibility, vendor harmonization and, as a result, significant reductions in the cost of inventory. Together, these initiatives showcase Tech Mahindra’s ability to maximize CapEx efficiency, streamline procurement, and accelerate AI-driven transformation for industry leaders.

Barriers and Opportunities Ahead

• Cost Pressure: Fabs in the US/Europe consistently cost at least 10–35% more to build and operate than equivalent fab capacity in Asia. This forces chipmakers to analyze on return for investment using AI and put capital in avenues of insight led innovation instead of just capacity expansion.
• Material and Talent Shortages: Advanced packaging and memory demand more specialized materials (often from limited global suppliers) and a highly skilled workforce in short supply. The nature of these technologies means that scaling them requires a highly specialized workforce, for which there is high demand and little supply worldwide, leaving firms to invest in talent development and retention.
• Supply Chain Complexity: Growing regionalization and scaling technology portend exactly why an agile, resilient logistics network is more important than ever as supply chain complexity increases. Building localized innovation hubs and robust supplier ecosystems will be key to mitigating future risks and ensuring sustained leadership in the turbulent era of AI hardware advancement.

The Way Forward: Moving from Growth to Resilience

By the next decade, we need to move away from capital chasing growth to capital dedicated to creating sustainable foundations for the future. And it means targeting strategic AI investments across the entire hardware stack, from compute to memory to packaging to integration. It also involves fostering collaborative innovation from silicon design to system-level optimizations, accomplished through strategic partnerships in which partners pool innovation capital, exchange forward-edge technologies and share risks. The AI hardware race is too large and important for any one player to do it alone. Indeed, just as important are talent and ecosystem excellence across engineering disciplines (including machine learning), AI algorithms, hardware-software co-design, and supply chain strategy. In a sector where disruption is inevitable, preparedness will separate leaders from followers.

Ready to optimize your capital allocation for the AI-hardware revolution? Connect with Tech Mahindra’s semiconductor experts today to forge strategic partnerships, and lead the next wave of innovation.