I think the hybrid model makes the most sense. Local AI will not replace data centers, as the largest models and training workloads still need massive cloud infrastructure. But repetitive, privacy-sensitive and high-frequency inference could increasingly move local.
For me, the key is total cost of ownership, not just raw performance. If companies can buy hardware once and run thousands of AI tasks without paying for every API call, local inference becomes more attractive. $Apple(AAPL)$ Apple’s unified memory gives it an interesting position, while $NVIDIA(NVDA)$ remains dominant in large-scale AI compute.
I would watch enterprise adoption closely. If companies start buying Macs specifically for AI agents and local inference, it could create a meaningful hardware cycle. Personally, I see hybrid cloud + local AI as the most realistic outcome.
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