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Why AI Hardware Spending Is Shifting From Training to Inference

Why AI Hardware Spending Is Shifting From Training to Inference

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Lucas and Luna unpack a major inflection point in AI infrastructure: the shift from training-focused hardware spending to inference. They examine why NVIDIA's 7.7 percent weekly drop amid a broader AI hardware selloff may signal market recognition that the training buildout is peaking, while inference workloads — and the chips optimized for them — become the next growth frontier. The hosts walk through the economics: training a single large model can cost over $100 million, but inference — actually running that model millions of times for users — is where the recurring revenue lives. They cite Microsoft's 1.5 percent resilience this week as a sign that software platforms monetizing inference are outperforming pure hardware plays. The episode also explores how startups like Groq, d-Matrix, and Cerebras are challenging NVIDIA with inference-specialized chips, and why the hyperscalers (Amazon, Google, Microsoft) are designing their own inference silicon. A concrete look at why the AI chip narrative is shifting in mid-2026. #AI #Inference #NVIDIA #AMD #Microsoft #AIHardware #TrainingVsInference #Groq #Cerebras #DMaxtrix #Hyperscalers #CustomSilicon #Semiconductors #Technology #AIInfrastructure #FexingoBusiness #BusinessPodcast #GenAI Keep every episode free: buymeacoffee.com/fexingo
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