Investing Along AI’s Value Chain: Discover the Hidden Winners
Where the bottlenecks are forming
AI and high-performance computing have created new constraints. Memory, fibre optics and connectivity now limit system throughput and speed.
These bottlenecks give firms positioned in these segments pricing power. They also confer influence over industry standards.
Upstream chip manufacturing
TSMC, founded in Taiwan in 1987, remains the largest dedicated semiconductor foundry. Its latest 2-nanometre process delivers roughly a 30 percent gain in power efficiency.
This advantage helps TSMC serve designers of GPUs and ASICs. Companies such as Nvidia and Google rely on foundries for advanced node production.
Memory and high-bandwidth needs
Micron Technology, founded in 1978, is one of three global-scale DRAM producers. The others are Samsung and SK Hynix.
High Bandwidth Memory (HBM) is emerging as a critical constraint for AI workloads. Micron is a qualified HBM supplier with visible multi-year demand from hyperscalers. Key customers include Amazon Web Services, Google Cloud and Microsoft Azure.
Fibre optics and materials science
Corning has a long history in materials science and optical fibres. The firm provides the physical layer that connects rack networks and data centers.
Corning also secured a manufacturing commitment from Apple for cover glass production in Kentucky. That deal underscores the company’s role supplying large clients.
Connectors, cables and interconnects
Amphenol, approaching its centenary, leads in connectors and interconnect systems. These components enable reliable data and power transfer.
The company reported a 52 percent increase in sales and a 77 percent rise in EPS in 2025. Growth was strongest in communication solutions and datacom demand.
Why investors should look beyond hyperscalers
Nvidia and other platform leaders drive much of the AI narrative. Still, broader infrastructure determines how fast the ecosystem scales.
Replacing overvalued giants with upstream and downstream suppliers can lower concentration risk. It can also reveal the hidden winners in the AI value chain.
Practical investment takeaways
- Identify segments where capacity or technology is scarce, such as HBM and optical fibre.
- Focus on companies with high barriers to entry and long-term customer commitments.
- Diversify exposure across the AI value chain to reduce platform concentration risk.
Giuseppe Perrone, president and managing partner at Varenne Capital Partners, highlights that infrastructure and supply constraints are central to long-term returns. Investors should consider this when investing along the AI value chain to discover hidden winners.
Filmogaz.com presents this analysis for readers tracking hardware and supply-side opportunities in the AI era.
