Open Models Expand Compute Demand
Open-weight models act as free complements that drive AI into more private and sovereign environments, lifting total demand for chips, servers, networking, and security.
When model weights are cheap and downloadable, AI spreads beyond a few API vendors into corporate data centers, banks, governments, and military networks. Running cost remains, so value shifts downstream to infrastructure. Self-hosted and air-gapped deployments also size for peak load rather than shared cloud averages, raising idle capacity and total hardware sold even if cost per token falls.
| Instrument | Side | Target | Reason |
|---|---|---|---|
| NVDA | Long | We believe open weights will multiply the number of places that run AI on dedicated silicon. NVIDIA sits at the center of that compute stack, including government-facing open-model deployments in closed environments, so broader self-hosted adoption should expand its addressable market even as competition and efficiency improve. | |
| AMD | Long | We believe the same open-weight wave enlarges the whole accelerator market rather than only one vendor’s share. AMD is positioned as a primary alternative GPU supplier for enterprises and institutions that will host more models on-prem for control, cost, and policy reasons. |
Defender-controlled open models become strategic infrastructure for cyber and sovereign AI, opening a U.S. gap that chip and security vendors can monetize.
Attackers can use unrestricted models while defenders are constrained by API safety filters and vendor policy. Governments, critical infrastructure, and security teams therefore need pre-tested models under their own control in isolated networks. Strong open models for real deployment are often Chinese, while U.S. public buyers may reject them, leaving demand for trusted American open stacks plus the servers, networks, and cyber tooling around them.
| Instrument | Side | Target | Reason |
|---|---|---|---|
| CRWD | Long | We believe AI that lives inside more enterprise and government environments expands the attack surface and the need for security platforms that operate where models run, not only where APIs are rented. CrowdStrike benefits if cyber defense spend rises with distributed AI deployment. | |
| PANW | Long | We believe closed-network and enterprise AI estates will pull more budget into network and cloud security as organizations isolate data, models, and execution. Palo Alto Networks is a core way to express rising security demand around self-hosted intelligence. |
Themes
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