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Nailah Williams discovered her path to a college degree in the most unlikely place: behind the wheel of her Uber. After years of jobs that forced her to choose between earning a paycheck and pursuing an education, she joined a program where Uber would cover her tuition for online classes at Arizona State University (ASU), where I teach. Created in 2018, the program covers tuition at ASU for drivers anywhere in the U.S. (or their beneficiary such as a child, spouse, or parent) who had completed at least 3,000 rides and met the rating requirements. With the newfound flexibility to work and study when she wanted, Nailah was able to complete a degree in urban planning while supporting herself and her family. Nailah isn’t alone. Across America, changes to how we work and learn are reshaping who is able to go to college. The Job-Education Problem According to the National Center for Education Statistics, 45% of full-time college students have a job, and around one in four of these are employed full-time. Historically, balancing work and school has taken a heavy toll. Past research found that the more students workedespecially beyond 1520 hours a weekthe more their grades, time spent studying, and graduation rates suffered. What happens when both work and education bend to fit students’ lives, instead of the other way around? To find out, my colleagues Spencer Perry, Basit Zafar, and I studied the unique partnership between Uber and ASU that funded Nailahs tuition. We analyzed data for hundreds of participating students and thousands of their classmates. The results were dramatic. Unlike in traditional jobs, participating students could take on more work hours with almost no impact on their grades (and vice versa). When students increased their study time by 10%, their work hours dropped by just 1% and their income, tips, and performance ratings barely changed. They passed their classes at about the same rate as a matched group of similar students attending classes in-person. Even more remarkable was who these students were. The initiative opened up ASUs online courses to a whole new population. Nearly half of participants were not in college before enrolling in the program. Their average age was 39, a full 14 years older than the typical ASU online student. They were more racially diverse, had higher financial need, and were more likely to be first-generation college students. Yet they harbored the same high expectations for their degrees and the resulting career and financial benefits. The Power of Flexibility The magic ingredient bringing college within reach for these students wasn’t just free tuition. It was flexibility. In a survey we conducted, program participants were three times as likely to say theyd enroll in college if work was flexible and classes were online than with traditional, rigid schedules. Our research points to a massive opportunity for more people to get a college education. As artificial intelligence reshapes the job market and economic uncertainty grows, millions of workers, young and old, need new skills and credentials. With newfound flexibility, students can earn while they learn, leveling up without taking on crushing debt. Universities that embrace this shift will capture new markets and expand their student populations. They should aggressively recruit gig workers and develop self-paced programs that can be done anytime, anywhere. They should create support systems for working students, from flexible on-campus jobs to childcare resources to time management coaching. The students are out there, driving for Lyft, delivering for DoorDash, or freelancing online, waiting for someone to make college work for them. Smart employers see the opportunity too. Companies like FedEx, Chipotle, Amazon, and Gap already offer tuition benefits. But the real winners will combine education support with genuinely flexible schedules and build partnerships with universities that allow their workers to plug into adaptable online learning. In our study, 30% of participants said they would have quit their job with Uber sooner if not for the partnership with ASUproof that these programs help attract and retain workers. The question isn’t whether flexibility will reshape education and employmentit’s whether institutions will embrace the change or be left behind. People like Nailah are ready and waiting for their opportunity. More partnerships like the one between ASU and Uber can help level the playing field and offer a path to success for students who have been shut out from higher education for too long.
Category:
E-Commerce
I began my career in neurosciencenot in business, not in engineering, not in HR. When I became head of product at GitLab, I hadnt managed a product team before. I didnt have the traditional credentials. But someone took a chance on me based on what I could contribute, not where I had worked. That moment changed the trajectory of my career. It also changed how I hire. At Remote, we focus on capability over pedigree. What someone can do matters far more than what their resume suggests. That mindset has always been useful. But with the rise of AI, its becoming essential. The shift were experiencing goes beyond productivity and automationits about how we define job readiness, recognize potential, and avoid replicating the exclusions of the past. AI is already changing how people work. But if we want it to improve how we hire, we must apply it deliberately. This shift is happening as attitudes toward traditional credentials are also changing. Amid rising tuition costs and mounting student debt, just 22% of Americans say a four-year degree is worth the cost if it requires loans, according to Pew Research Center. If companies keep leaning on degree requirements as a proxy for readiness, they risk missing a growing pool of skilled, AI-fluent talent who are proving themselves outside conventional pipelines. AI is changing who can contributeand how I view AI as essential. Its deeply embedded in my companys culture and how we function, and its ability to multiply talent has completely shifted how we, and many companies we support, function. Less talked about, however, is that it has also changed what it means to contribute. People with less formal training can do more, faster, if theyre equipped with the right tools and a clear mandate. Someone without a formal degree can use AI to complete tasks once reserved for experts such as analyzing data, drafting technical documentation, even writing code. A single parent in a rural town can contribute meaningfully to remote teams while spending each day with their children. The same tools that replace certain functions can also empower a much wider set of people to participate in the knowledge economy. That doesnt mean experience is irrelevant. It means the gap between being qualified on paper and being able to deliver in practice is narrowing, but our hiring systems havent kept pace. This shift demands a change in how we evaluate talent. If contribution no longer depends on pedigree, hiring systems built around degrees, brand names, and linear resumes start to fall short. Companies need to shift from resume screens to problem-solving prompts, or from interview panels to real-world trial projects. While the support for skills-based hiring has grown in recent years, a 2024 report from Harvard Business School and the Burning Glass Institute found that fewer than one out of every 700 hires in the past year were made based primarily on skills rather than traditional credentials. The appetite for change is clear, but until hiring systems catch up, companies will keep filtering out exactly the kind of talent they say they want. The resume is losing signal The temptation is to believe that AI itself will solve that problemthat it will automatically surface hidden talent. But thats a dangerous assumption. Left unchecked, AI hiring systems can replicate and even intensify existing biases. Algorithms trained on historical data may favor candidates who resemble previous hires based on education, geography, or background. In some cases, automated filters penalize career gaps or overlook nontraditional applicants entirely. If were not careful, we risk embedding these filters deeper into the systems we use to scale. Access to AI tools and fluency with them is not evenly distributed. Candidates from underrepresented backgrounds, non-native speakers, or people living in under-resourced regions may not have equal exposure or confidence with these tools. Equity isnt just moral; its operational To spot the best talent, we need hiring practices that reflect modern skills: adaptability, communication, and the ability to learn quickly. My company uses asynchronous workflows that mirror how our teams operate. We emphasize clarity of thought, responsiveness, and problem-solving in context. Our internal documentation and onboarding approach are designed to help people ramp quickly, regardless of background or time zone. Those practices make it easier to evaluate candidates based on how they work, not just how they present. Remote work has already proven that talent doesnt need to be colocated to contribute. Its also exposed where structural inequities persist. Access to reliable infrastructure, tool fluency, and global employment systems still varies widely. Equity doesnt happen by default. It must be designed. AI is redefining readiness AI may accelerate tasks and reduce the cost of execution. But it doesnt eliminate the need for talent. It raises the bar for how talent is integrated and who gets a fair shot. The best candidates may not come through traditional pipelines, live in a major city, or have a college degree. But they are ready to contribute. What companies need now are hiring systems that prioritize contribution over credentialism. That includes making AI training a standard part of onboardingnot a perk for the technically inclinedand ensuring that workflows reflect how teams operate. If your work is async, global, or fast-changing, the hiring process should test for those dynamics. Heres where I recommend employers start: Test for how people will work, not how well they interview. Use trial projects, async exercises, or written problem-solving prompts that mirror real workflows. And yes, let them use AI. Make AI training part of onboarding for everyone and treat AI literacy as a standard skill to level the playing field. Audit your tools and data for bias. Regularly review which signals your systems reward and whether theyre excluding qualified, nontraditional candidates. The best candidates may not look like your past hires, but you might be surprised where you find talent ready to deliver. Job van der Voort is CEO and cofounder of Remote.
Category:
E-Commerce
When was the last time a brand didnt just catch your eye, but moved youmade you feel something real? Today, AI can produce logos, taglines, and campaigns at lightning speed. Algorithms can replicate styles, test headlines, even mimic tone. But as branding becomes more automated, a deeper question emerges: Can machines truly connect with human experience? Or does meaningful branding still depend on uniquely human emotions like empathy, intuition, and lived understanding? After 15 years of building brands across continents and causes, Ive learned that the most powerful branding isnt about perfection. Its about presence. When we show upreally listen, engage, and understandbranding becomes a bridge to transformation. Empathy isnt programmable Consider Sonia, a single mother in Delhi, India, who handcrafts beautiful bags. Her skill was undeniable, but her work was invisible to the market. She didnt need a new product to attract customersshe needed a platform. We helped craft Saffron, a brand that honored her artistry and gave her a place in the conversation. What followed wasnt just commercial growth; it was a personal awakening. Branding turned her story into strength. AI cant do that. It doesnt ask how someone feels, or why their work matters. It optimizesbut it doesnt understand. Intuition creates belonging In Hanoi, Vietnam, a small café run by recent graduates struggled to stay open. They had quality coffee and a noble missionproviding jobs for youthbut no clear identity. We repositioned the space as Friends Coffee Roasters, a name that invited connection and warmth. The transformation was immediate. Customers showed up, reviews surged, and the café became a local favorite on TripAdvisor. A new name didnt just save a businessit saved a dream. Branding didnt just describe what they sold; it reflected who they were becoming. Culture is not universal Technology can scan trends, but it cant live inside a culture. That mattersbecause branding without context can flatten identity instead of elevating it. In the Villa Rica region of Peru, the Yanesha tribe cultivates organic coffee to fund community development. Yet selling unbranded bulk beans kept them trapped in poverty. Working with the tribe, we codeveloped Tierra Fuerte, a brand rooted in resilience and sovereignty. With it came more than just packagingit brought pricing power, dignity, and visibility. A similar challenge arose in Mongolia, where limited access to fresh produce was impacting health. Partnering with local stakeholders, we created Smart Berry to introduce strawberries grown in high-tech smart farm. The brand became more than a productit sparked a national conversation about wellness, youth aspiration, and modern agriculture. In both cases, cultural insightnot codewas the true catalyst. Final thoughts These experiences remind us: While AI is a tool, human intelligence is the soul of branding. The ability to read between the lines, to feel the emotional undercurrent, to design not just for markets but for meaningthose are still human strengths. When branding is approached with care, it can uplift. It can build local economies, support social missions, and shift narratives. It doesnt just sellit serves. And in a time when design tools are increasingly automated, what sets a brand apart isnt how quickly its builtbut how deeply it connects. Sooyoung Cho is CEO of the bread and butter brand consulting LLC.
Category:
E-Commerce
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