AI stack — application layer wins long term, hardware offers near-term upside
Semiconductor and AI hardware names retain upside from current levels
Demand for accelerators, networking, and power-dense compute remains ahead of supply in key AI build cycles, supporting earnings revisions and multiple support for leading hardware vendors before the stack fully matures into software-led economics.
| Instrument | Side | Target | Reason |
|---|---|---|---|
| NVDA | Long | We believe the leading GPU and systems vendor remains the clearest expression of near-term AI capex and supply constraints, with upside as hyperscaler and enterprise deployments continue to favor accelerated compute from current levels. |
Application layer captures durable value as AI commoditizes infrastructure
As training and inference hardware scale and margins compress at the chip and cloud layer, differentiated products, workflows, and distribution at the application layer should compound returns over multi-year horizons. Software with embedded AI features, vertical solutions, and platform ecosystems benefit from falling unit costs and widening adoption.
| Instrument | Side | Target | Reason |
|---|---|---|---|
| MSFT | Long | Large-cap platform exposure to enterprise AI copilots, cloud distribution, and application monetization aligns with value accruing above the hardware stack as inference becomes cheaper and products embed models by default. |
Themes
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