L1D Outcome Data★
What actually happened after the model acted — the ground truth for RLHF, evals and post-training.
L1D Outcome Data is a sublayer of L1 Data in the Supply Chain of Intelligence™ (SCOI) by Anand Arivukkarasu. It is one of the structurally defensible sublayers (Defensible Triangle) where moats actually compound. If your product has no closed-loop outcome capture, your model is stuck at the quality of the base model. Building outcome capture is a product decision, not an ML one.
What actually matters at L1D
- Outcomes are what let you leave the human-labeling treadmill.
- Most AI products under-instrument outcomes because it requires end-to-end product thinking.
- Outcome data is where competitors most obviously cannot copy you.
The startup lens
If your product has no closed-loop outcome capture, your model is stuck at the quality of the base model. Building outcome capture is a product decision, not an ML one.
Vertical lens — how this plays across categories
Log booked-revenue attribution back to specific model actions.
CSAT + reopen rate as the training signal.
PR merge, revert and test-pass rates as reward.
How to defend L1D
- Product-side outcome instrumentation as a P0 feature.
- Reward-model pipelines fed by real outcomes, not proxies.