Why do AI builders want reasoning steps and not just answers?
Because feedback on each step can train a model better than feedback on the result alone. A 2023 study compared "outcome supervision, which provides feedback for a final result" with "process supervision, which provides feedback for each intermediate reasoning step", found that process supervision "significantly outperforms" it on problems from the MATH dataset, and released 800,000 step-level human labels (Lightman et al., Let's Verify Step by Step, arXiv). Earlier work in 2022 showed that prompting large models with a chain of thought, "a series of intermediate reasoning steps", improves their reasoning (Wei et al., arXiv).
Where do companies already hold reasoning traces?
In any record where people explain a decision while making it: code review comments, root-cause analyses and incident write-ups, deviation notes against an SOP, approval memos, and escalation threads in support tickets. An AI training startup's chief revenue officer told Business Insider that data showing how work is actually done is valuable "because it provides critical context on how decisions are made and products are built" (Business Insider, Oct 2026). One buyer of agent-training data told MIT Technology Review he looks for archives covering situations "from disagreements to tricky judgment calls" (MIT Technology Review, Oct 13 2026).
What do reasoning traces mean for what you get paid and control?
They are written by your own experts in the course of their work, so they already exist in your systems. No new data collection is needed. Judgment-heavy records also tend to mention clients, colleagues and personal remarks. fearofgod.ai de-identifies the data before it goes to buyers. Your company's name stays confidential until the agreement is fully signed, and a completely anonymous partnership is also possible. Your company can be paid more than once, through renewals and repeat licences.