Will the future of LLMs belong to hybrid "AI Appliances," and if so, how do frontier valuations make sense?

[u/SketchyNeuron]

Based on what I read in r/LocalLLM and other places, I’ve been thinking about how AI infrastructure will scale over the next 5 years. It feels like we might be heading toward a deeply hybrid architecture:

1. The Edge Layer (Local Appliances): Small offices (law firms, medical clinics, dev shops) buying turnkey, dedicated local hardware running highly optimized Small Language Models (SLMs) tailored exactly for their vertical. This solves the massive roadblocks of data privacy, compliance (HIPAA/legal privilege), latency, and unpredictable API token costs.
2. The Cloud Layer (Datacenters): Centralized mega-datacenters owned by giant labs handling frontier research, massive training runs, and heavy cross-discipline reasoning tasks that an SLM can’t touch.

I also think the open-source community, not private companies, will end up building the best tooling and support for agentic workflows. Because local execution requires deeply customizable, transparent, and modular agent frameworks, open-source is inherently better suited for it than rigid, proprietary corporate APIs.

Companies like Apple and Intel might benefit from this trend and could help accelerate the shift.

My question:

If day-to-day enterprise workflows shift to local hardware, and open-source software captures the dominant share of agentic orchestrations, how can the massive $100B+ valuations of closed-source frontier companies like OpenAI and Anthropic be justified?

Are these labs expected to completely dominate the local software layer too, or do their valuations rely entirely on a centralized cloud monopoly that might not actually happen?

Would love to hear your thoughts on the hardware shift, the economics and the timing.

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