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Cloud-based LLM gold rush is ending

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Why This Matters

The shift away from cloud-based large language models (LLMs) toward local AI processing marks a significant change in the tech industry, emphasizing on-device intelligence and specialized AI applications. This transition could reduce reliance on cloud infrastructure, lower costs, and reshape how AI tools are integrated into everyday devices, impacting both consumers and enterprise solutions.

Key Takeaways

Saturday, 13th, 2026 I wrote this article yesterday. I decided to let it mature before publishing. This is what I woke up to this morning: This is exactly what I mean when writing about the weaponization of AI and its use as a national security concern. I did not change a word of the article. Screenshot from Anthropic’s website.

Friday, 12th, 2026.

Is the LLM gold rush over?

I think it is. Not the AI one, though.

Apple announced a few days ago at WWDC something that goes way beyond Siri improvements; it is a signal of what the AI world looks like today, and where it is going.

Let me explain.

Apple believes that for most uses, we don’t need cloud-based LLMs. They have decided that Mac OS should be an AI-enabled system that processes workflows and tasks locally.

Cloud systems can be used when needed. It makes sense, users will not need to buy a monthly subscription if their Mac has the power to run AI automations and tasks natively.

What does that mean for you and me? Probably most of our automations and Claude skills will eventually run on our Macs. We will likely have to rebuild our apps.

So what happens to LLMs? They are already hinting at where they intend to go: advanced AI work: agents, harnesses, deep reasoning tasks. Specialist work, not default infrastructure.

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