What all of these needs share is that they're not about making language models smarter. They're about building the world that smart language models need to operate in.

The analogy that keeps coming to mind is the early internet. In the mid-1990s, you could look at what the web would eventually need, payments infrastructure, identity systems, hosting, CDNs, search, trust and reputation mechanisms, and most of it didn't exist yet. The companies that got to those needs early built enormous, durable businesses. Not because the underlying technology was proprietary, but because infrastructure is inherently sticky, because network effects compound, and because nobody wants to switch their plumbing once it's working.

The agent economy is in that moment right now. The models are getting better fast, and deployment is accelerating. The infrastructure, memory, world models, trust, reversibility, coordination, goal clarification, is still mostly unbuilt.

The companies working on these problems aren't building features. They're building the operating environment for an entirely new class of worker. That's not a small market. It might be the market.

source: https://acalytica.com/blog/what-ai-agents-want-the-100b-infrastructure-gap-nobody-is-talking-about

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