Well, Actually: ChatGPT Advises Half the Going Rate, and That Is a Pedagogical Moment
Lilach Bullock prompted ChatGPT to price her consulting services. The model returned a figure approximately half her established fee. This outcome illustrates how training data skews toward publicly visible, and often discounted, benchmarks.
AI pricing recommendations regress toward a mean, bereft of context. You must treat any model output as a starting hypothesis, not a ceiling. Cross-reference against your current client roster and your actual closed deals.
Lilach Bullock, an AI Implementation Consultant, published this finding on her own site. She compared the generated quote to her real-world rate and identified a systemic undervaluation bias.
Step 1: Open ChatGPT. Prompt the model to recommend a price for your exact service within your specific industry and city. Record the figure. Step 2: Manually locate three competitor prices via public freelance marketplaces or professional directories. Step 3: Compare the datasets. If the AI figure undercuts the market, resubmit the prompt with your years of experience, niche specialization, and premium positioning appended. Note how the estimate shifts.