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Is "tokenomics" breaking your AI budget? As AI use cases graduate from early experimentation into day-to-day operations, organizations are hitting a major roadblock: the unsustainable variable costs of running production AI at scale in the public cloud.
Data from the Private Cloud Outlook 2026 study shows:
🔹 56% of enterprises are moving production AI inference to private cloud.
🔹 Public cloud inference dropped 15 percentage points YoY.
Paul Turner on why private cloud is the cost-efficient choice for daily AI operations. See the study: https://brcm.tech/4vKuQii
Announced this week: VCF 9.1 is here!
Watch our new Virtually Speaking series featuring 9️⃣ dedicated episodes with VCF experts.
Explore how to scale AI workloads, automate deployments, strengthen cyber resilience, and more. See the 🎥 series on YouTube: https://brcm.tech/4dmYAeO
04/27/2026
Until recently, energy was treated as a steady operating cost. AI changes that equation.
This is where virtualization re-enters the conversation, not as a new idea, but as a necessary one.
Virtualization lets you allocate exactly what resources an AI service requires and nothing more, maximizing site power and server utilization.
Chris Wolf breaks down the infrastructure economics: https://brcm.tech/4u6oGs7
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