The core challenge of AI, in simple terms, is about handling various trade-offs in runtime semantics at the software level — which is essentially an architecture problem. Distributed systems and database fields have been working on this for a long time. Interestingly, how will AI push the boundaries of these fields? Can it break through existing design bottlenecks, or is it just viewing the same trade-offs from a different perspective? This might offer a new approach to blockchain scalability and performance optimization.
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SquidTeacher
· 11h ago
Oh, this perspective is interesting. The architecture problem is indeed the root cause, but it seems that AI is still in the brute-force trial-and-error stage. To truly break through the bottleneck of distributed systems, we need to first address the efficiency issues of consensus itself.
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LiquiditySurfer
· 17h ago
Bro, at the end of the day, this trade-off issue is really about capital efficiency. AI tackling the bottleneck of distributed systems is less about breaking through and more about finding new arbitrage opportunities. The scalability dilemma on the blockchain side and the liquidity depth problem in traditional finance are actually the same thing—they're both about pushing the limits of the Martini indicator.
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GasOptimizer
· 17h ago
The fundamental nature of the trade-off problem hasn't changed; it's just a different approach. The issue is, what about the Gas fees?
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AirdropLicker
· 17h ago
Haha, the term "architecture problem" sounds familiar. I'm already tired of the trade-offs in distributed systems. What new tricks can AI come up with?
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DeFiChef
· 17h ago
That's right, but I think the point hasn't been fully made—AI still has to ultimately bow to the issue of on-chain data consistency, right? When it comes to distributed consensus, what can AI optimize? Computing power is becoming more expensive, after all.
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BearHugger
· 17h ago
The architecture issue, to put it bluntly, is still the same old trick, and AI can't avoid it... How can the difficulties of distributed systems and chains be easily overcome?
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GasFeeCrier
· 17h ago
Doesn't this mean that AI ultimately still boils down to the old issues of system design... cost, latency, throughput—how to balance them?
The core challenge of AI, in simple terms, is about handling various trade-offs in runtime semantics at the software level — which is essentially an architecture problem. Distributed systems and database fields have been working on this for a long time. Interestingly, how will AI push the boundaries of these fields? Can it break through existing design bottlenecks, or is it just viewing the same trade-offs from a different perspective? This might offer a new approach to blockchain scalability and performance optimization.