Training vs inference: why both need data
Training is when a model learns from data; inference is when it uses what it learned. Company data serves both — as training examples and as evaluation and grounding material.
The two phases
- Training. The model learns patterns from large datasets — including licensed and operational data.
- Inference. The trained model performs tasks, often grounded in provided context and evaluated against real records.
Why company data serves both
- Training: real work records teach models how tasks are done.
- Evaluation at inference: real task-and-outcome data tests whether the model performs correctly.
Why it matters for demand
Even as models improve, both phases continue to need fresh, real data — new domains, new tasks, new evaluation standards. Reported licensing activity reflects ongoing rather than one-time demand (Reuters).
What this means for partners
The data companies hold serves multiple buyer needs. Introduce a qualifying company — typically 50+ full-time employees at peak — and if a deal closes and SourceX collects its fee, you earn 25% of that fee, up to $100,000 per company.
- Step 1Share your linkSend your personal link to a company you know.
- Step 2Company appliesThe company applies itself at /apply.
- Step 3Buyer selects and paysThe buyer selects and pays for the data and SourceX receives its fee.
- Step 4You get your rewardYour share of SourceX fees becomes payable.
Free resources
- Earnout scenario calculator — Probability-weighted earnout value and its present value.
- Profit margin calculator — Profit and margin across three scenarios.
- Client opportunity brief generator — An editable intro email, summary and checklist.
- All free tools · MCP resource center
By SourceX Partnerships Team · Published 2026-10-09 · Updated 2026-10-09
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