Invisible's Labor-First Unit Economics

Invisible behaves less like software with near zero delivery cost and more like a managed labor network where software lifts the spread between what clients pay and what workers cost. In practice, a customer buying annotations, evals, or back office processing is funding wages first, then Invisible captures margin by routing tasks to the right agents, reducing rework, and automating the repetitive steps around human judgment.1 2 4 5

• The unit economics are easiest to see in AI training work. Invisible has been described as charging roughly $30 to $45 per hour for RLHF tasks while paying raters about $15 to $20 per hour, so gross margin starts with that labor spread before management overhead and quality losses. Better workflow design directly widens that spread.2
• This is why orchestration matters so much. Invisible breaks large jobs into smaller steps, assigns them across a 3,000 plus person workforce in 35 plus countries, and uses its platform to monitor output quality. Every task completed without manual escalation or redo lowers delivery cost and improves margin.1 2 4
• The closest comparables show the same accounting logic. In human data and labeling, headline revenue often includes large pass through payments to contractors, so durable economics come from net revenue and margin, not topline alone. That makes Invisible more comparable to Scale, Handshake, and Prolific than to pure software vendors.3 4 5

The next step is turning more of that labor margin into software margin. As Invisible shifts from one off staffing style work toward repeatable enterprise AI workflows, the winners will be the firms that can keep humans on the highest judgment tasks while software absorbs routing, QA, and the simpler parts of execution.1 2 5