Equipping workers with insights about compensation

# Equipping workers with insights about compensation Source: [https://openai.com/index/equipping-workers-with-insights-about-compensation/](https://openai.com/index/equipping-workers-with-insights-about-compensation/) Wage information shapes important decisions: what jobs people apply for, whether they negotiate, and whether a particular career path is worth pursuing\. But unlike the price of most goods, the price of labor is often hard to find and difficult to interpret—especially for workers who are early in their careers, switching fields, or moving locations\. AI is a new type of labor\-market resource\. Rather than requiring a worker to search across multiple websites, interpret scattered salary pages, or ask a socially risky question, a model can synthesize wage information and return a benchmark in seconds\.**Workers are already using ChatGPT this way, sending nearly 3 million messages per day, on average in the US, asking about wages, compensation, or earnings\.** Our latest[research report⁠\(opens in a new window\)](https://cdn.openai.com/pdf/ChatGPT-and-the-price-of-work_report.pdf)looks into how Americans are using ChatGPT to close the wage information gap\. They most often come to ChatGPT for two kinds of help: translating pay into a usable benchmark, and understanding what a role, company, career path, or business idea might realistically pay\. Among labeled wage\-benchmarking messages, pay calculation accounts for 26% of questions, followed by specific role \(19%\), entrepreneurship \(18%\), specific role at a company \(11%\), and occupation or career questions \(11%\)\. We determined this through a privacy\-preserving analysis that uses automated classifiers and never involves a human viewing individual messages\. The pattern of those questions matters\. Occupation\-related wage searches are concentrated in fields like arts, design, entertainment, sports, and media; management; healthcare; transportation; sales; and business and financial operations\. Relative to employment, wage search over\-indexes in higher\-skill and less transparent occupations such as creative fields, management, healthcare, and computer and mathematical roles, suggesting demand is strongest where pay is harder to benchmark, more negotiable, or more important to career mobility\. We see a similar pattern in entrepreneurship\-related questions, which are concentrated in creative work and small service businesses—areas where there often is no posted wage benchmark\. Across industries, wage search rises where pay is more dispersed and where wages are higher\. In other words, workers seem to seek pay information most when getting the answer right matters more and when pay is harder to read\. That is why this matters beyond wage lookup alone\. Misunderstanding potential earnings can keep workers in lower\-paying jobs, undercut negotiating power, delay career moves, or discourage investment in education and training\. Better information can’t eliminate uncertainty, but it can make it easier to form a reasonable view of what work pays and therefore help people make better decisions\. To better understand how our models serve workers, the report also introduces WorkerBench, a new effort to evaluate ChatGPT on labor market tasks that are valuable to workers\. In this first benchmark, we evaluated GPT‑5\.4 against 2024 OEWS median wages at the national occupation and metro levels\. In the observed sample, the model is highly accurate: coverage is high, bias is small, and almost all numeric estimates fall very close to the benchmark\. Pay information is economically important, but often difficult or sensitive to obtain\. Workers are already using ChatGPT to navigate that problem, especially in the parts of the labor market where uncertainty is highest and the stakes are most meaningful\. Our goal is to keep improving how useful and reliable that help can be—moving beyond national benchmarks toward the geography, firm, level, and compensation questions workers actually ask every day\.