AI Will Create More Work Not Less

I'm tired of the AI job panic.

People keep saying this time is different. New jobs won't be created because AI can do so much more than previous technologies.

I just don't see how we're not going to have new jobs.

We're stuck in a zero-sum mindset about work. We believe there's a finite number of jobs available, so if AI takes one, it leaves fewer jobs for people.

That's simply not true. If anything, technology has always multiplied the kinds of work humans do.

Here's How It Actually Works

Work isn't tasks. Work is solving problems. Every time we build a tool that makes life more convenient, we discover dozens of new problems that didn't exist before.

The internet is a perfect example. It didn't reduce human work. It exploded into dimensions we couldn't have imagined. Social media managers, cybersecurity specialists, data scientists, UX designers, content moderators, digital marketers, app developers, cloud architects, SEO specialists, and a multitude of other roles that would have sounded like science fiction in 1990.

AI is following the same pattern, just faster and on a larger scale. It's making certain repetitive tasks much easier, but it's also creating issues where we can't tell whether what we're reading or seeing is real or fake. That's just the beginning of the problems AI will create while helping us solve others.

Several organizations saw this coming years ago. Back in 2019, Ford partnered with Deakin University and Griffith University in Australia to produce 100 Jobs of the Future. Cognizant published several reports as early as 2018 predicting jobs that seemed crazy at the time but now look remarkably accurate.

• 21 Jobs of the Future (2017)

• 21 HR Jobs of the Future (2020)

• 21 More Jobs of the Future (2018)

Three Areas I'm Watching

I'm particularly interested in three areas where you can see this happening right now.

The Aging Economy

By 2030, 1 in 5 Americans will be 65 or older, and by 2035, older adults will outnumber children for the first time in U.S. history. This creates demand for people who can design personalized AI assistants for memory loss, coaches who help families integrate home robots, and testers who ensure AI systems work for aging users.

Cybersecurity Threats

The U.S. Bureau of Labor Statistics projects cybersecurity employment will grow 33% from 2023 to 2033, compared to just 4% for all occupations. Companies are scrambling to hire security analysts who probe AI models for vulnerabilities, architects who prepare for quantum computing attacks, and experts who detect AI-generated fake identities.

Information Integrity

Deepfake fraud cases in North America exploded 1,740% between 2022 and 2023, with losses already reaching $200 million in the first quarter of 2025 alone. We need forensic specialists who authenticate media using AI watermarking, transparency officers who explain how AI models work, and trust and safety specialists who protect platforms from misinformation.

Two Patterns I Keep Seeing

From watching these areas, two things become obvious.

Solutions Create New Problems. Every technological solution creates new problems that require human expertise. Online shopping is convenient, but it has also created data breaches, return fraud, and supply chain complexity. Remote work tools like Zoom made collaboration easier, but introduced digital burnout.

Ride-sharing apps solved urban transportation, but created regulatory headaches and gig worker rights debates. AI will solve many problems, but it's already creating new forms of deception, bias, and unintended consequences.

The Messier the Problem, the More Human It Becomes. AI can diagnose cancer, but humans decide how to talk to patients about it. AI can optimize supply chains, but humans figure out the ethical implications.

AI can generate content, but humans determine what messages society actually needs. The most complex challenges require creativity, empathy, and moral judgment, fundamentally human skills that no algorithm can replicate.

Why We Need Universal Basic Learning, Not Income

Universal Basic Income assumes work will disappear, but we're actually heading toward more work than we've ever had.

Universal Basic Income treats this like a problem to solve, but work isn't just about earning money. Work offers people dignity, purpose, and meaning. People want to matter, not just survive.

What is someone supposed to do with $1,000 a month anyway? You can't live on that in most major cities, and more importantly, it doesn't help them develop new skills. The real challenge isn't giving people money to survive while robots do all the work. It's preparing people for work that doesn't exist yet.

What if we flipped the entire conversation? Instead of Universal Basic Income, what about Universal Basic Learning?

If the government wants to give each adult $1,000 a month, that could work if the money is explicitly meant for education and skill development. Education is expensive. By giving people money every month for learning, we're empowering them to grow and adapt.

On the corporate side, instead of asking whether we're going to have a four-day week, we should encourage each company to give each employee 4-8 hours a week to spend on learning.

Time is the real barrier. Even if people have money for education, they don't have the time or energy. By evening, most people are tired from work. Weekends are often filled with family obligations and personal responsibilities. Companies should provide learning time during work hours when employees can focus properly.

This shift in thinking extends to how we approach individual career planning.

We should stop asking what major someone should pursue in college or what their career trajectory might look like over the next 20-40 years. Careers are not one-time marriage decisions. We're operating on 2-5 year timelines now.

This isn't just about education policy. This mindset shift reveals the bigger opportunity we're all missing.

Stop Thinking Zero-Sum

The biggest mistake we're making is thinking about work as a fixed pie. It's not. Technology doesn't eliminate human work. It changes the nature of human work.

Every convenience creates complexity. Every solution creates new problems. Every efficiency creates new inefficiencies that humans need to solve.

Social media management didn't exist 20 years ago. Data science was barely a profession 15 years ago. AI prompt engineering wasn't a job title 5 years ago. These are entire industries built around problems that didn't exist before we made other things more convenient.

AI will follow the same pattern. It will eliminate some tasks while creating entirely new categories of work that we can't even imagine yet.

Stop preparing for a world with less work. Start preparing for a world with different work.

What new problems do you see AI creating in your industry? I'm collecting examples of jobs that didn't exist 5 years ago. Share your examples in the comments below.