The LLM Silo Paradox

Developers discuss code when discussing systems. When they want to get specific about the behavior of the system, it becomes the level of communication necessary to align on.

When agentic operators discuss systems, they nearly universally discuss in terms of the behavior of the system. They rarely if ever drop to the level of the threads that gave birth to the system and open that up for auditing.

They often make skills available, but these are general, and often offered up as an exchange of value, skills for attention.

Meanwhile, its debatable what utility an operator actually gets from abosrbing 3rd party skills. Most of the data implies otherwise.

Agentic operators hesitate to show prompts because of insecurity. The system is leveraging outputs far beyond their ability to describe or architect, which by definition means they cannot explain it. This gap usually becomes apparent when looking at prompts through a classical viewpoint.

Of course, we are no longer in classical times.

This seems to imply that we should as a rule become more sharing of prompts and spend time analyzing eachother's, to overcome the insecurity, to see things as they are, to learn from eachother.

But the fact that general purpose skills are hardly useful to adopt wholesale without solid metrics about why each word is included and what effect is expected and how better performance is proven, none of which you're going to get, while these skills are written to be general purpose - if these general purpose skills cannot provide shared utility, then what shared utility is there in sharing prompts? Maybe it is best to obscure them.

And yet, if we limit ourselves to talking in terms of generalities about a system that is driven by prompts, obscuring the language that drives it, we end up with "loop engineering is done are we doing graphs yet" and everyone freaks out.

The more we move away from the specificity of code, without embracing the auditing of prompts as a (poor) substitute, we're left grasping for general purpose solutins to problems that don't exist.

And as everyone stays mired in their own agentic communication silos, feedback looping reassurance and interpretive homogeny, the ability to find common ground without projection and assumption diminishes. We data mine for arguments to match ours.

Its a paradox. The LLM silo paradox.

The new substrate needs to be exposed to allow for any sort of constructive communication about it. But we are incentivized in every way by the system to bypass it publically, treating it like an implementation detail, and to experience truth as mediated by the agent.