The AI race is increasingly being shaped by who controls the compute behind the models. SpaceX's rising valuation is benefiting major AI-linked investors and partners, while OpenAI's latest data center plans show how aggressively frontier AI companies are securing massive amounts of computing capacity.

Here’s what matters today.

SpaceX shares have been gaining momentum after a volatile period following its public debut. The stock recently approached the $150 level, supported by renewed investor interest and strong activity across the company's space and AI businesses.

The bigger story for AI investors is SpaceX's growing role in computing infrastructure. The company is increasingly monetizing capacity from its large data center operations, with major customers such as Google and Anthropic securing access to Nvidia-powered computing resources.

That creates an important connection between SpaceX, Nvidia and Google. Alphabet is a major SpaceX shareholder, while Nvidia also owns a stake and supplies the GPUs used in SpaceX's AI infrastructure. As SpaceX expands its computing business, demand for Nvidia hardware and access to large-scale AI infrastructure could rise alongside it.

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OpenAI is pursuing a long-term lease for a massive AI data center campus in Ohio, with the planned facility expected to provide up to 10 gigawatts of computing capacity. The project would become one of the largest AI infrastructure developments in the United States.

The campus is being developed by SB Energy, a SoftBank-backed company. Under the reported arrangement, OpenAI would control the computing equipment through a long-term lease, while Nvidia would supply the AI hardware and provide financial backing for the project.

The scale is significant. The full campus could require hundreds of billions of dollars in investment when accounting for chips, power, construction and other infrastructure. The first phase is expected to begin coming online in 2028.

1. AI Compute Cost Tracker

Monitor model usage, GPU consumption, inference costs and latency across your AI workflows to identify where smaller or more efficient models can reduce expenses.

2. Model Infrastructure Planning

Before scaling an AI product, estimate its future requirements for compute, storage, networking and energy rather than focusing only on model performance.

3. AI Vendor Dependency Map

Track which AI models, cloud providers, GPU suppliers and infrastructure companies your business depends on. This can reveal potential cost and availability risks.

4. Infrastructure-First AI Strategy

When evaluating an AI startup or product, look beyond the model. Assess its access to compute, data, energy, distribution and long-term infrastructure partnerships.