WorkWhile CEO Bets AI Creates a Worker Shortage, Not a Job Shortage

The loudest arguments about artificial intelligence and labor have focused on how many jobs machines will take. WorkWhile CEO Simon Khalaf is planning around a different constraint: too few workers for the jobs employers need filled.

Khalaf’s contrarian argument sees a labor shortage ahead, a forecast based partly on what WorkWhile is seeing in its hourly labor marketplace.

He told PYMNTS CEO Karen Webster that demand for workers on the platform rose 22% from July to August and another 14% as of late September. The company, he said, now has more jobs than available workers.

“This job apocalypse is such a hoax,” Khalaf told Webster. “There are no job losses related to AI. I’ve provided all the counter data, and I’m letting you know today for your podcast that the U.S. is entering the great labor shortage era.”

His argument extends beyond WorkWhile’s own demand. AI infrastructure requires labor to build data centers, creating demand for trades and occupations that other industries also need. Khalaf cited forklift operators as one example, arguing that moving workers into data-center projects can leave eCommerce and distribution companies competing for the same labor.

For employers, scarcity collides with an existing expense: replacing workers who leave.

Khalaf said two-year retention in the United States is about 45%, while some segments served by WorkWhile experience annual attrition exceeding 100%. WorkWhile is applying AI to the problem by matching workers with employers and using previous experience to determine whether to send them back.

Khalaf said the company’s repeat work rate is above 95%, without requiring either the employer or worker to commit to another assignment.

“What our AI systems are doing is putting the right people in the right job that they will thrive at and love,” Khalaf elaborated. “If you go to a job and you like the environment, and the employer likes you, the algorithm is putting you back in that job.”

Khalaf ties the approach to the old maxim about doing what you love and never having to work again. For an hourly labor platform, the relevant measure is whether someone chooses to return.

WorkWhile is applying predictive models to attendance as well. Khalaf said its technology estimates whether a worker will show up or cancel and when that cancellation could occur, allowing another worker to be brought in through the on-demand marketplace. He said WorkWhile has reduced short-term absenteeism by more than half.

As for the ripple effect of a mismatch, “There’s the job loss, and then there’s income loss related to not being able to work a shift,” Webster illustrated. “It doesn’t take a job loss for an hourly worker to feel a lot of pain losing a quarter or a third of their income because they don’t have enough work.”

Matching Work With Household Cash Needs

Income volatility brings the labor problem into the household budget.

Khalaf compared workers’ cash flow needs with WorkWhile’s own working-capital requirements. WorkWhile pays people within 24 hours after a completed shift, while its customers generally pay the company about 35 days later. The company borrows to finance the intervening period.

“Our balance sheet in August wasn’t big enough to fund the growth,” Khalaf noted. “So we rely on working capital. We borrow to spend in those 34 days.”

Workers have their own gap between money coming in and obligations going out. Khalaf cited credit cards, personal loans and payday loans among the sources consumers may use when income and bills don’t line up. Webster added borrowing from friends and family.

WorkWhile is using its access to available work as another option. Khalaf said workers can give the company permission to view financial-account information through Plaid. Its models can identify a potential cash shortage and find additional shifts, overtime or hours that could generate income before an obligation comes due.

“If our AI models predict that they’re not going to have enough money to pay for that bill, we send them to work or overtime or extra hours to make that money, pay the bill and save the fee and the interest,” Khalaf added.

Khalaf connected the approach with WorkWhile Money and said 34% of workers had become debt-free roughly seven months after its launch.

WorkWhile’s role in some workers’ finances extends beyond occasional shifts. Khalaf said the platform is the sole or predominant source of income for roughly 40% of its workers.

Employers, meanwhile, are asking WorkWhile to move beyond on-demand staffing, Khalaf said, including applying its technology to full-time hourly work. Doing so raises questions about continuity, training and whether flexible workers arrive prepared to perform a job without requiring employers to retrain them for each assignment.

Webster asked how WorkWhile provides employers with workers who are trained, understand their processes and can deliver consistent service.

Khalaf said WorkWhile provides required training and certifications for jobs including food handling and forklift operation. The company also uses AI agents to condense training materials for workers.

WorkWhile is moving beyond matching an available person with an open shift. Khalaf described a model that uses data for repeat assignments, attendance, training and opportunities for workers to earn additional income.

Listen to the interview with Simon Khalaf to learn more about:

• Why Khalaf believes AI infrastructure spending will add to demand for some hourly occupations.
• How WorkWhile predicts cancellations and prepares to fill uncovered shifts.
• Why employers are asking the company to apply its technology beyond on-demand staffing.

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