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ONE DAY IN a distant, nightmarish future, perhaps the humans will be groveling at the feet of the AI overlords. “I’m sorry you’re upset,” it might say. “But listen, I tried to get you a bigger paycheque.”
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And it seems it would be right. A new report from Payscale wanted to understand why there has been a “widening disconnect” around salary expectations, and found some evidence that AI has a chronic tendency to overestimate market rates for salaries, quietly pushing the 20 per cent or so of jobseekers who use tools like Gemini or ChatGPT to research salaries to unknowingly drive up their salary expectations.
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“Seventy per cent of employers have noticed a rise in employees using AI to shape salary expectations,” it found. “But using generative AI as a barometer of salary expectations is creating new tensions: 27 per cent of AI-using employees say it inflated their expectations compared to other sources, and 38 per cent of employers agree AI tools are driving salary demands higher than ever before.”
It’s good to be a bit skeptical about this sort of thing. It might be a case of both sides externalizing responsibility to generative AI, when what we’re actually seeing is traditional tension between employer and employee over salaries. It’s easy for an employer to believe an employee wants more money because ChatGPT told her she deserves more; it’s also easy for an employee to believe deeply they are underpaid because the fancy search engine machine told them so.
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But however the data should be interpreted, research has shown this is a tendency that AI models have. A 2023 Employee Benefit News report found that ChatGPT specifically overestimated significantly when compared to anonymized payroll data.
The avenues for employees to educate themselves on salary expectations are expanding,” said Payscale compensation strategist Ruth Thomas. “Employees are still gaining knowledge from traditional sources like family and friends and industry salary guides, but AI and social media are driving up salary expectations without the verified data and role context needed to inform compensation.”
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