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Why Smarter AI Isn’t the Point of MCP
Everyone’s talking about smarter AI.
The MCP conversation made me realize something uncomfortable:
our biggest AI problems aren’t intelligence problems — they’re coordination problems.
MCP isn’t flashy. It doesn’t promise smarter models or autonomous agents taking over workflows. What it does instead is make context explicit, shareable, and boring.
That might sound small. It isn’t.
Most AI failures today don’t come from models being “dumb.” They come from systems misunderstanding each other, leaking assumptions, or silently losing context.
MCP treats interoperability as a UX problem, not a governance problem — and that framing might be the most important idea in modern AI infrastructure.
The future of AI may belong less to whoever builds the smartest model…
and more to whoever builds the calmest systems around them.
Question worth asking:
As models get smarter, will our real bottleneck be intelligence — or agreement?
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By sdntechforumEveryone’s talking about smarter AI.
The MCP conversation made me realize something uncomfortable:
our biggest AI problems aren’t intelligence problems — they’re coordination problems.
MCP isn’t flashy. It doesn’t promise smarter models or autonomous agents taking over workflows. What it does instead is make context explicit, shareable, and boring.
That might sound small. It isn’t.
Most AI failures today don’t come from models being “dumb.” They come from systems misunderstanding each other, leaking assumptions, or silently losing context.
MCP treats interoperability as a UX problem, not a governance problem — and that framing might be the most important idea in modern AI infrastructure.
The future of AI may belong less to whoever builds the smartest model…
and more to whoever builds the calmest systems around them.
Question worth asking:
As models get smarter, will our real bottleneck be intelligence — or agreement?