Let’s be honest—whenever someone mentions AI and jobs in the same sentence, most people’s brains immediately jump to two extremes: either we’re heading toward a utopian world where robots do all the work, or we’re facing mass unemployment and economic collapse. Both views are dangerously simplistic.
I’ve been watching this unfold from my corner of the product world, and what strikes me is how poorly we’re framing the conversation. We’re treating AI like some monolithic force, when in reality its economic impact is far more nuanced. Take manufacturing: according to a 2023 World Economic Forum report, while AI may displace 85 million jobs globally by 2025, it’s also expected to create 97 million new roles. The net gain? Positive, but the distribution? That’s where things get messy.
Here’s what most people miss: AI isn’t just automating tasks—it’s reshaping entire value chains. Remember when everyone feared ATMs would eliminate bank tellers? The data tells a different story. The Federal Reserve Bank of St. Louis found that while ATMs reduced the number of tellers per branch, they made branches cheaper to operate, leading to more branches and actually increasing total teller employment until the 2008 financial crisis. The real disruption came from online banking, not ATMs. Similarly, AI’s biggest impact won’t be on individual jobs but on business models themselves.
This brings me to what I call the 「productization of AI」—where technology meets human needs in unexpected ways. Look at what’s happening in healthcare: AI diagnostic tools aren’t replacing radiologists as much as they’re creating new hybrid roles where humans and algorithms collaborate. A study in Nature Medicine showed that AI-assisted radiologists detected 5% more cancers with 10% fewer false positives. That’s not job replacement—that’s job enhancement.
But let’s not sugarcoat the challenges. The transition will be painful for many. The Brookings Institution warns that workers with only high school diplomas face exposure to AI automation that’s ten times higher than those with bachelor’s degrees. We’re looking at a potential amplification of existing inequalities unless we fundamentally rethink education and retraining.
This is where product thinking becomes crucial. The Qgenius Golden Rules of Product Development emphasize starting from user pain points and reducing cognitive load. Applied to the job market, this means we need to design retraining programs that actually work for displaced workers, not just theoretical frameworks that look good on paper. Germany’s dual education system, which combines classroom learning with on-the-job training, offers a promising model we should study.
What fascinates me most is how AI is creating entirely new categories of work that didn’t exist five years ago. Prompt engineers, AI ethicists, machine learning operations specialists—these roles require human judgment that algorithms can’t replicate. They’re proof that while AI excels at optimization, humans still dominate at contextual understanding and ethical reasoning.
The real economic question isn’t whether there will be jobs, but whether we’re preparing people for the jobs that will matter. As product leaders, we have a responsibility to build systems that help humans and AI collaborate rather than compete. The future belongs not to those who fear technological change, but to those who understand how to harness it for human flourishing.
So the next time someone tells you AI will either save or destroy the economy, ask them: What problem are we really trying to solve here? Because in my experience, the most interesting opportunities lie in the spaces between the extremes.