The 1 Big Compensation Decision Founders Should Never Hand to AI

AI has quickly made itself useful across hiring. It can sift through résumés, draft a job description in seconds, and match a role to market benchmarks. For a small company that never had a compensation team, that’s a genuine unlock. But things get thornier when AI has a say in what the actual salaries that companies pay their employees should be. And that’s the bigger question that founders now have to grapple with—whether they should let AI set the number itself. That pressure is only growing, and this year it found a sharp edge in the law. On October 8, 2025, California outlawed a hiring shortcut that employers had leaned on for years. When Governor Gavin Newsom signed SB 642 into law, effective January 1, 2026, the state redefined what a “pay scale” in a job posting is allowed to be: a good-faith estimate of what an employer expects to pay a new hire. The point, as the law firm Morgan Lewis noted, was to stop employers from posting “meaningless pay scales simply to be in compliance.” So the old dodge is dead. Post a range wide enough to protect yourself, say from $80,000 to $200,000, and you’re not being cautious. You’re non-compliant. California isn’t alone, and these rules don’t only apply to big companies. As of early 2026, more than a dozen states now require employers to disclose pay, and the thresholds reach startups, not just giants. In Colorado, a single employee is enough to trigger disclosure, while New York City draws the line at four; states like California, Illinois, and Washington set it at 15. For founders who have always set pay based on their instincts, that guess is now a public document, and the person most likely to read it closely is the employee who already holds the job. And not just them: everyone doing similar work will see the number too, and measure themselves against it. The problem was always there Improvised pay stays invisible while it works, and it works right until two people doing the same job discover they earn thousands apart, or a strong hire leaves for a competitor who bothered to benchmark the role when you didn’t. Jonas P. Johnson, who works on compensation modeling at ERI Economic Research Institute, a firm that has built salary data for more than three decades, says instinct-based pay produces one of two problems, and they aren’t equally fixable. Underpaying firms struggle with turnover, while overpaying firms struggle to keep prices competitive. Most founders assume overpaying is the safe mistake. Johnson says it’s the harder one to escape. Underpaying is the easier hole to climb out of, because raising pay tends to slow the turnover it caused. Overpaying is the real trap, since you can’t take money back once people are earning it. “Employees won’t accept pay cuts,” Johnson says, so a company that has drifted above market has to hold raises below average for years to come back down, all while trimming costs to survive. The case he sees most is the firm that bought out a competitor and inherited its payroll, absorbing someone else’s guesswork and making it their own. How one raise becomes a chain reaction Transparency rules do more than reveal the gap between what you pay and what the market pays. They reveal the distance between your newest hire and your most loyal one, which is the first comparison employees make. The way it unfolds is easy to trace. When market rates climb, you post a higher range to attract someone new, and that range is now visible to the person who has held the job well for three years, and to their peers in similar roles. Fresh money at the top squeezes the space between new and existing staff, and Johnson warns it “can lead to turnover among existing employees unless the organization re-scales the entire salary structure.” A single number in one posting can force you to reopen everyone’s. Compensation professionals call this pay compression, and it hits morale before it shows up in a budget. Jason Greer, an employee and labor relations expert, says a compressed structure creates “zombie employees” who “show up, clock in, and do the minimum.” They haven’t walked out because they have learned what a newer colleague earns and have reduced their effort. His advice is to understand your local market before circumstances force the issue, because “you might be global, but to your employees, you’re local.” Why the average raise misleads you Guesswork is seductive because there’s always a tidy number to reach for. Heading into 2026, most surveys cluster raises between 3.2 and 3.5 percent, down from post-pandemic highs near 4.4 percent. But the average describes almost no one. Some jobs see no salary growth year over year, Johnson says, while others climb 8 percent. Pay tends to lurch rather than climb, and his clearest example comes from the start of the pandemic, when compensation for forklift operators had been flat for years and then jumped 22 percent in two months as supply chains seized. Budget off the blanket figure, and you underpay the very people you can least afford to lose. The one call you shouldn’t hand to AI None of this complexity means a 30-person company needs the machinery of a Fortune 500. A large firm leans on formal pay grades to keep several hundred jobs consistent, because at that scale nobody can price every role by hand. A 40-person company has maybe 20 distinct roles and can price each against the market directly, without collapsing them into bands. The point isn’t to build an elaborate structure, since none of this needs a complicated setup. You just have to know what a job is really worth before putting a number on it. What makes that reachable now is that the grunt work has gotten cheap. Matching your internal roles to market data once meant hiring a consultant for weeks. Today, the same first pass takes about a day using AI tools. But Johnson is clear that speeding up the work does not mean handing over the decision. Software can widen the field of comparable jobs and flag where your pay drifts out of line, yet “all final decisions should be made by humans,” he says, because an AI model will always return a confident number even when the data behind it is too thin to trust. Workforce strategist Terri Gallagher pushes the point even further, arguing that compression isn’t a pay problem at its root, but instead “a workforce strategy issue.” The number you choose shows what you think the work is worth, and no tool can decide that for you. The answer, then, isn’t to keep AI away from pay. Let it do the first pass, gather the comparisons, and show you where you’ve drifted. Then make the final call yourself, because that judgment is the one part of the job that was always yours. The expensive answer For most of the past decade, “we’ll figure out compensation as we grow” was a reasonable thing to say. The cost of having no system stayed hidden; a little attrition here, a bruised ego there, all of it absorbed into the churn of a growing company. Transparency law takes that hiding place away. The guess is a public document now, the compression is plain to the people it costs, and the tools to do the work properly have never been more affordable. A founder who treats all this as a compliance headache will keep patching it one uncomfortable job posting at a time. But the one who treats it as a reason to finally learn what each role is worth will find the law was never the real issue. The real issue was always whether they knew what they were paying for. EXPERT OPINION BY KOLAWOLE ADEBAYO