The shift computing underwent in the 1970s-80s is happening again with AI.
Before personal computers, computing meant mainframes in universities and corporations. Access was limited to specialists. Then hobbyists started building their own computers, experimenting in garages and sharing ideas. People like Wozniak and Jobs were part of this community. The Apple I, hand-built in a garage, showed that computing could be personal and accessible.
Right now, a growing community self-hosts open-source AI models, fine-tunes them, and builds applications that run locally. It's still niche, but it mirrors early personal computing: people taking technology from centralized institutions and adapting it to their needs.
Cloud-based AI has problems. Every conversation with ChatGPT or Gemini is visible to employees and used for training. Your AI isn't yours, it is a rented spy not a friend.
Running AI locally solves this. Your data stays on your device. Your AI works without internet. No corporate servers logging everything you do.
But privacy is just the start.
Speed: Local AI responds instantly. No network latency, no waiting for server responses, no downtime when services go offline. Your AI is as fast as your hardware.
Control: You own the model. You can modify it, fine-tune it, restrict it, or completely redesign it. No terms of service changes. No censorship. No company deciding what your AI can or cannot do.
Always-On Agents: Cloud AI makes 24/7 agents prohibitively expensive. Running an agent continuously on someone else's servers costs real money per request. Local AI flips this: your agent runs constantly on your device at no incremental cost. It monitors your emails, manages your calendar, learns your patterns, automates your workflows. This isn't possible at scale with cloud models.
The decentralized approach isn't just better for users. It's the only approach that scales sustainably to billions of people using AI agents constantly.
Most daily tasks (writing, brainstorming, basic questions, simple automation) work fine with 5-10B parameter models. They exist now and they're getting better as hardware improves and architectures advance.
For complex tasks (scientific research, heavy coding), cloud models remain useful. But most people don't need that most of the time.
Specialized chips, better compression, and improved architectures will keep closing the gap.
The PC revolution didn't stop at individuals owning computers. It enabled the internet. Connected personal computers created something bigger: a global network enabling collaboration at massive scale.
Personal AI could follow the same path. When billions have capable AI agents on their devices, these agents will collaborate, share insights, coordinate on tasks.
AGI might not come from one massive centralized model. It could emerge from billions of personal AI agents working together. Decentralized, collaborative intelligence where no single organization controls everything.
We're early. Open models are improving. Hardware is getting better and cheaper. The builder community is growing.
The personal computer revolution democratized computing and created the internet. The personal AI revolution could democratize intelligence itself and give birth to AGI.
This is why I'm working on Wolle.AI—to help make local AI accessible and practical for everyone.
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