When a certain problem becomes too big for a single computer, the concept of distributed computing comes in. The same computing task is now split across multiple machines that work together to reach a solution.
Connecting 1,000 computers does not automatically equate to having a single computer that is just as powerful — a machine doesn't just become more powerful overnight. A hard task requiring the input of all the computers cannot always be completed more efficiently just because there are more machines. Splitting a task up like that won't always work as efficiently as we think it will.
There are limitations, such as:
Yes! Even if some of the 1,000 machines malfunction, lose their network connection, or send false information, they can still reach a consensus. Consensus algorithms are used to address the Byzantine Generals Problem, a foundational problem in computer science.
However, there is a strict mathematical limit to how many failures or "liars" the system can handle while still reaching a correct agreement. Crash faults and Byzantine faults are certain limitations tied to this problem — though there are protocols designed to refine and overcome these issues.
Most of the computation that occurs when you use these applications takes place in distant data centres that are either owned or leased by the businesses providing the service. The heavy lifting — such as generating AI text, searching billions of web pages, or running complex game logic — is done on powerful servers thousands of miles away, while your local device just manages what you see and interact with.
There are certain tasks humanity hasn't yet achieved that relate to the astronomical sector — real-time modelling of Earth and truly accurate forecasting haven't yet been achieved by our networks. Advanced scientific discoveries, like the instant molecular design of a protein or complex material physics, are other fields humanity hasn't been able to fully solve.
Things we could build: