The framework is right in principle. The validation step is where it quietly breaks down at scale. When AI executes across hundreds of lines or thousands of decisions, the human validator is being asked to do the most cognitively expensive part of the loop at a pace the loop was never designed for. Intent is clear. Protocol is defined. But validation assumes the human has the time and context to genuinely compare output against intent. At volume, that assumption doesn’t hold.
I really appreciate this take. You’re right that the validation step is where things start to strain once the surface area gets big. When the model is making hundreds of tiny decisions, the human validator ends up doing the hardest part of the loop at the fastest pace. Intent is clear. Protocol is defined. But validation quietly assumes the human has the time and context to compare output against intent. At volume, that assumption doesn’t always hold.
I’ve been thinking a lot about how to handle that problem without throwing out the whole pattern. Over on LinkedIn I’ve been working through something I call the Expression Layer, which is basically a way to break the work into smaller, typed units so the model has less room to guess. Instead of validating raw output, you validate whether the model stayed inside the structure the protocol created. It spreads the load instead of putting everything on one checkpoint.
It seems to help with scale, at least from what I’ve seen so far. Humans still make the final call, but they aren’t being asked to manually inspect every branch. They’re validating the shape of the work, not every leaf.
I’m curious if it would be useful to start talking about the Expression Layer here too. It feels connected to the scaling question you raised, and it might be the right moment to bring that thinking over from LinkedIn.
The Expression Layer idea is exactly the direction this needs to go. Breaking validation into typed units rather than asking one human to hold the entire surface area in their head is the structural fix I was pointing at but didn’t have the vocabulary for.
I explored the exhaustion side of this in a piece I published this week, specifically how code review at scale is burning out the human in the loop. You’re describing the solution. I was describing the symptom. Would genuinely value your take if you get a chance.
The framework is right in principle. The validation step is where it quietly breaks down at scale. When AI executes across hundreds of lines or thousands of decisions, the human validator is being asked to do the most cognitively expensive part of the loop at a pace the loop was never designed for. Intent is clear. Protocol is defined. But validation assumes the human has the time and context to genuinely compare output against intent. At volume, that assumption doesn’t hold.
I really appreciate this take. You’re right that the validation step is where things start to strain once the surface area gets big. When the model is making hundreds of tiny decisions, the human validator ends up doing the hardest part of the loop at the fastest pace. Intent is clear. Protocol is defined. But validation quietly assumes the human has the time and context to compare output against intent. At volume, that assumption doesn’t always hold.
I’ve been thinking a lot about how to handle that problem without throwing out the whole pattern. Over on LinkedIn I’ve been working through something I call the Expression Layer, which is basically a way to break the work into smaller, typed units so the model has less room to guess. Instead of validating raw output, you validate whether the model stayed inside the structure the protocol created. It spreads the load instead of putting everything on one checkpoint.
It seems to help with scale, at least from what I’ve seen so far. Humans still make the final call, but they aren’t being asked to manually inspect every branch. They’re validating the shape of the work, not every leaf.
I’m curious if it would be useful to start talking about the Expression Layer here too. It feels connected to the scaling question you raised, and it might be the right moment to bring that thinking over from LinkedIn.
The Expression Layer idea is exactly the direction this needs to go. Breaking validation into typed units rather than asking one human to hold the entire surface area in their head is the structural fix I was pointing at but didn’t have the vocabulary for.
I explored the exhaustion side of this in a piece I published this week, specifically how code review at scale is burning out the human in the loop. You’re describing the solution. I was describing the symptom. Would genuinely value your take if you get a chance.
https://inferenceprotocol.substack.com/p/the-human-in-the-loop-is-burning-out