Computing was once centralized in the server rooms of banks, universities, and large corporations, until two people in a garage changed the game. With AI we have returned to a cloud-centric architecture. Is the same thing about to happen again, 52 years later?

From the 1950s to the 1980s, computing meant one thing: a mainframe in a basement, terminals on desks, and access controlled by whoever owned the machine. IBM. Universities. Banks. Insurance companies. Computing was powerful but inaccessible to almost everyone.
Then a group of hobbyists in garage workshops and Homebrew Computer Clubs decided that computing should belong to the individual. They built their own boards. They hacked Atari hardware. They formed a bohemian revolution of individualists who believed that computing was not a corporate privilege but a personal one. The personal computer was born. Apple did not invent the computer. They decentralised it.
Cloud AI has rebuilt the mainframe era, just with better UX. Intelligence lives on someone else's server, accessible through someone else's API, switched off by someone else's government on three days' notice.
Data sovereignty. IP sovereignty. Compute sovereignty. All concentrated in a handful of hyperscalers.
The Fable 5 shutdown was not an anomaly. It was a reminder of the architecture.
Should intelligence belong to whoever owns the largest server, or to whoever generates it? The Homebrew Computer Club answer was obvious. So is ours.
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Most perception stacks reason one frame at a time. The network detects, tracking is bolted on afterwards, and the system never really carries the world forward. A snapshot machine cannot validate cleanly, because the thing that would make its output trustworthy is the thing it discards between frames: continuity.
Gödel's incompleteness theorem, Hume's induction problem, the halting problem, and AI hallucination are not isolated failures of reason. They point to the same missing term: context.
Most ADAS perception stacks classify what they already know. The real world is combinatorial, contextual, and full of unknowns. Semantic fallback systems are needed when flat object lists fail.