We have reached the moment white collar workers have feared for months. Has AI finally come for my job? Companies like Salesforce claim they need fewer human employees to do the work AI can tackle, after laying off thousands. Klarna claims the company was able to shrink its headcount by about 40%, in part because of AI. Duolingo said last spring it will stop using contractors for work that AI can handle. Overall, companies have announced a staggering 700,000 job cuts in the first five months of 2025, an 80% jump from the previous year.
The irony is almost poetic. For years, the tech industry assumed robots would come for factory workers first. Amazon’s leaked documents once suggested the company could replace half a million warehouse jobs with automation. Instead, just weeks ago, Amazon laid off 14,000 middle managers while planning to hire 250,000 seasonal warehouse workers for the holidays. The AI revolution, it turns out, is hollowing out the corporate ladder before it touches the warehouse floor.
The narrative around artificial intelligence and the job market is challenging for white-collar workers right now. Yet while Silicon Valley sends warnings over which desk jobs AI will consume next, we’re missing an equally important question about the future of AI: What about everyone else?
The AI application bubble nobody’s talking about
We are currently in an AI application bubble. The last few years of AI innovation have focused almost entirely on white-collar productivity: workplace efficiency tools, revenue-optimization platforms, and communication automation. Many of the major AI innovations from the past two years have been designed for someone working a 9-5 desk job from a laptop.
Meanwhile, the people who make up 60% of the American workforce are stuck completing manual onboarding processes, sorting through countless texts to find the right shift, calling in when they need a shift swapped, clocking in on physical time clocks, logging in to web-only portals, and waiting for biweekly paychecks. We’re talking about warehouse crews, janitors, delivery drivers, nurses, and game day parking attendants. These are the people who have been largely forgotten when it comes to how AI can transform their day-to-day jobs without risk of eliminating their roles. Every day, millions of shift-based workers keeping hospitals running, concerts staffed, and factories moving are dealing with broken, archaic systems. They’re waiting for shift confirmations, digging through emails for schedules, and calling managers just to ask, “When am I working next?”
By focusing almost all of AI’s potential on the white-collar economy, we’ve left out the workers who are irreplaceable. Building accessible, intuitive tools for non-tech-savvy users has the potential to narrow the global inequality gap while creating a more resilient foundation for technological progress and a more resilient economy.
Only 40% of American workers say they have a “quality job”
While office jobs dwindle, demand for human workers continues to grow. Restaurants need servers. Construction sites need carpenters. Hospitals need nurses. And in turn, the people doing these jobs need shift accessibility, work-life flexibility, and the ability to get paid quickly after shifts so they can continue to participate in the shift work economy and keep the world moving.
The human cost of not having a better way to work is striking. A recent Gallup and Jobs for the Future study found that just 40% of U.S. workers have what they define as a “quality job.” The rest face unstable schedules, limited growth opportunities, and financial insecurity. Not because they lack motivation or work ethic, but because the systems that support frontline work haven’t kept pace with the demands of modern life.
When workers play significant roles, have preschedules, and receive fair pay, they’re more engaged, more productive, and lead higher quality work lives.
What we learned building technology for Uber drivers
We know what’s possible when technology is actually designed for frontline workers, because we’ve lived it. While leading product development for the Uber for Drivers app, the two of us spent years focused on the driver experience. Drivers had to navigate complex processes: onboarding, completing background checks and vehicle inspections, selecting preferences, and receiving payments. Uber’s success was powered by a phenomenal self-service app that gave drivers the agency, control, and flexibility they needed in their lives.
That experience taught us that technology has the potential to dramatically improve frontline work, and the emergence of AI gives us an opportunity to do that once again. Tools like smarter scheduling systems that account for worker preferences and availability, AI-powered training programs that adapt to individual learning styles, communication platforms that actually work for teams that don’t sit in front of computers all day, and predictive systems that can optimize logistics and reduce physical strain. The technology exists. The investment, however, is still lacking.
Irreplaceable
The “Essential Economy” that we are talking about includes sectors like construction, manufacturing, transportation, etc., and represents $7.5 trillion in output per year, which is 27% of America’s GDP, equating to 52 million jobs and two million businesses. If you were to add healthcare, retail, and all public services—considered by many to be critical, hourly work sectors of the economy—the size jumps to $12 trillion of GDP, 95 million jobs, and three million businesses.
Without people to fill these roles, not only are essential services not being provided, but the US economy also suffers greatly.
With each technical revolution, we’ve always seen that collaboration with the technology yields better results than we can without it, or it can without us.
Instead, what if AI innovators asked, “How can we use AI to make these jobs better, safer, and more productive while also making workers’ lives easier?”
Consider a warehouse worker trying to swap shifts to attend a child’s school event. In most facilities today, this involves a series of text messages, phone calls, and manual approvals—a process that might take days and often fails. AI could handle this in seconds, reaching out to available workers who have relevant experience and required certifications, sharing shift details, and filling the shift. The worker doesn’t lose their job; they gain flexibility and dignity.
Consider a nurse who needs more hours as bills are adding up. He signs up with a new staffing agency so he can pick up extra shifts here and there. Today, onboarding entails manual back-and-forth with the agency and waiting days for assignments. AI can dramatically speed up his time to first shift, verifying his license instantly after he uploads it, offering digital onboarding tailored to the units where he’ll be picking up shifts, and matching him with shifts that work for his busy schedule. Instead of frustration and delays, the nurse begins with confidence and is able to start earning and covering his bills faster.
Applying AI to the roles that need it today
As the tech industry grapples with shifts toward white-collar jobs and AI’s role, we have an opportunity. The same sophisticated AI systems that can automate corporate reporting can be adapted to optimize shift schedules. The same machine learning that powers chatbots can improve safety protocols. The same natural language processing that summarizes emails can help workers with limited English proficiency better understand their rights and benefits.
The current moment of disruption in white-collar work is painful for millions of people, and that pain deserves recognition and resolution. At the same time, it also creates an opening to ask bigger questions about where AI should be applied and who it could serve.
The AI revolution isn’t going away anytime soon. This is our opportunity to choose how we use it, and who benefits.
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