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A recent image captured the cultural moment: a laid-off technologist taped a QR code to a Manhattan streetlight so strangers could find his résumé. Around the same time, top computer science students with perfect 4.0 GPAs had been contacting UC Berkeley professor James O’Brien worried about having no offers, no interviews, no roadmap. He called it an irreversible trend. As a Berkeley grad myself who has worked in finance, crypto and AI, I have mentored students facing these anxieties about where to take their careers. It’s pretty clear that what is happening is very different from the boom-bust cycle of previous tech downturns. IT sector unemployment grew from 3.9 percent to 5.7 percent in a single month earlier this year, and Mark Zuckerberg has already said AI will replace mid-level engineers in 2025. Yet we have massively overallocated our youth to roles like software engineering and the diagnostic aspects of medicine that AI will replace en masse. But this is a story about revaluation. Culture is built on interpretation. Creativity, taste, performance and empathy are the parts of work that resist optimization because they aren’t measurable. The value of human presence doesn’t vanish when machines learn to replicate output. Human as the Premium Automation moves fastest through tasks designed to be predictable, which is why standardized roles are tightening. But if you write songs, program festivals, run a gallery, design menus, coach performers or build communities, you operate in a category that machines can support, but not substitute. Work like this carries a human premium, as valued because audiences want the maker, the story and the craft more than an isolated AI-generated output. Take the handwritten letter as an example of this phenomenon. Email made it look obsolete, but then, precisely because it became rare, a handwritten note gained meaning. We see the same pattern with vinyl and intimate shows. As generative tools make the common abundant, individuality becomes the premium. Editor’s picks This human premium also maps directly to what I call ‘AGI-resistant’ work: roles that will remain valuable, scalable and ethical even as automation accelerates. We are potentially years, not decades, from achieving artificial general intelligence that can handle virtually any cognitive task. The advancements toward that goal are already reshaping the labor market. Differentiation has become crucial. Entertainment as a Case Study When analyzing the state of the job market, entertainment is an interesting case study. The industry has proven relatively resistant to AI, with actors like Emily Blunt, Whoopi Goldberg and Melissa Berrera speaking out against the potential movement of AI into acting when AI-generated actress “Tilly Norwood” gained recent virality. That reaction matters because entertainment shows how markets respond when automation reaches culture. It’s one of the few sectors where value depends on presence, interpretation and identity above all else. Entertainment’s durability stems from three drivers I study often in my AI work: boredom, loneliness and scarcity. Those forces explain why presence, interpretation and identity hold pricing power. People crave authentic connection and unrepeatable moments that no model can reproduce. The Rolling Stone Culture Council is an invitation-only community for Influencers, Innovators and Creatives. Do I qualify? Related Content The same logic governs hospitality, education and fitness: technology can optimize production, but it can’t reproduce attention in the room. Jobs that engineer participation and price proximity to human time will keep their value; roles built solely on repetition will not. I’m not saying back-end developers need to pivot into the arts. The point is to differentiate around what AI can’t replicate. The Scale and Urgency This shift isn’t something that can be managed gradually. It’s systemic. Globally, we’re watching the largest reallocation of labor since the Industrial Revolution. In India, youth unemployment already exceeds 16 percent. In the U.S., software engineers, analysts and other high-skill roles are starting to feel early displacement pressure. Over the next three to five years, automation will push technical workers to prioritize human context as the differentiator. The scale of this transition calls for an equally ambitious response. As I’ve written about before, the next wave of meaningful innovation will happen in the physical world, in systems that make society more adaptive: renewable infrastructure, energy grids, transportation, biotech and education. Policymakers should view this moment less as a risk and more as an opportunity to rebuild capacity. A modern “innovation deal” could employ millions to modernize physical and digital infrastructure: projects that integrate human oversight, creativity and design with AI efficiency. In the near term, this means renewable grids, energy storage and climate-resilient transit. In the longer term, it includes frontier initiatives, like space infrastructure, synthetic biology and AI-assisted manufacturing, where human judgment and adaptability still drive outcomes. The point is simple: AI will absorb the repetitive layers of work, but it also exposes how much of our economy depends on distinctly human problem-solving. Investing in these domains is how we preserve meaning, resilience and participation at scale. Start Incentivizing Now This revaluation of work starts with how we build skills. Knowing what I know now, I would have encouraged my college self to take more probability and statistics classes. Quantitative literacy still matters, but pairing it with human-centered disciplines such as psychology, design, communication and community-building is what creates long-term resilience. Those are the skills AI can’t internalize. Trending Stories I don’t have all the answers, but no one does. Those Berkeley students with 4.0 GPAs and zero offers deserve better than false promises about “practical” degrees. It’s obvious that we cannot keep preparing young people for careers that won’t exist in five years. In my view, governments and institutions should start rewarding education and industries that develop these human-premium capabilities — fields where interpretation, judgment and empathy define output. The longer we wait to realign incentives, the more abrupt the correction becomes. This is about evolving with AI, not resisting it. We’re not being replaced. We’re being revalued — toward the parts of our work only we can perform.
