I formalized the human in the AI energy equation
Nobody had connected human-in-the-loop, task decomposition, and energy consumption into one model. So I wrote a paper that does.
Felipe Cardoso, April 2026
Over the past few months I've been digging into something that's been bothering me since I started running LLMs locally on my PC. The question was simple: why does a 3 billion parameter model fail so much when running on its own, but when I sit next to it and guide it through the task, breaking things down, checking each output, rewording what failed, the results improve dramatically?
Anyone who's used Copilot, Cursor, or any local LLM has noticed this. But I wanted to go beyond just "noticing it." I wanted to know if you could measure it. And if you could put it in an equation.
You can. And I wrote a paper about it.
The problem nobody connected
The academic literature on LLMs treats inference as an autonomous process. The model receives input, generates output, someone measures joules per token. If it got it wrong, it regenerates. That's the cost.
When researchers study "human-in-the-loop", they focus on quality. The human as a corrector that improves accuracy. When they study task decomposition, they focus on performance. Breaking the task up makes smaller models perform better. And when they measure energy, they assume the model runs on its own.
What I realized is that nobody had connected all three. Nobody had asked: if the human decomposes the task, validates each step and reformulates what failed, what's the impact on energy consumption? Does the human reduce waste? And if so, how do you formalize that?
What I did
I created HAIL, Human-Augmented Inference for Lightweight Models. It's a mathematical framework that places the human programmer as an explicit variable in the energy cost equation of LLM inference. Not as an external observer, but as part of the system.
The core idea is an error decay function:
δ(H) = (1 − H)^γ
Where H ∈ [0,1] is the level of human intervention (0 = model running alone, 1 = full orchestration) and γ captures how effective that orchestration is, meaning how good the human is at steering the model.
When γ > 1, the first human interventions already have a disproportionate effect. You don't need to control everything, you just need to intervene at the right points. It's pair programming, not micromanagement.
The paper also introduces QDH (Quality-per-Dollar-Hour), a metric that measures output quality per dollar of hardware per hour. Because if you're running a model on an RTX 4070 instead of a datacenter A100, talking about "absolute quality" without considering cost tells you nothing useful.