For most of the last two decades, the technology industry carried an unspoken promise: things would change quickly, but opportunity would grow along with the change.
Software evolved. Platforms disappeared. New disciplines appeared almost overnight, and whole careers were built on technologies that had barely existed a few years earlier. For people in games, software, VFX, digital media, and interactive entertainment, reinvention was part of the job. If a tool became obsolete, you learned another. If a company closed, you moved to another studio. If your role changed, you updated your portfolio, learned a new workflow, called your network, and kept moving.
That cycle still exists, but today it feels fundamentally different.
A Correction, Not Just a Disruption
Across North America and Western Europe, technology workers have lived through several years of layoffs, hiring freezes, restructuring, consolidation, studio closures, and fiercely competitive job markets. Over the same period, artificial intelligence went from an interesting research topic to something companies are building into development, operations, customer service, design, marketing, and nearly every other part of the business.
It's tempting to link the two and conclude that AI is eliminating technology jobs. The reality is more complicated.
Many of these layoffs started before generative AI reached its current level of adoption. Companies hired aggressively during the pandemic, when demand for digital services surged. Then growth normalized, interest rates rose, and investors began rewarding efficiency over expansion. Many organizations found themselves larger than they wanted to be. Departments were consolidated, products were canceled, management layers were flattened, and acquisitions created overlapping roles.
AI arrived in the middle of that correction, and it is now speeding up a much larger conversation about what a modern workforce should look like.
The Real Question Is Productivity
That distinction matters. The question is no longer just whether AI will "take jobs." It's how AI changes the amount of work one person can do, and how that changes the way companies think about staffing.
A developer who can prototype in hours what used to take days creates value. So does an artist who can explore twenty ideas in the time it once took to explore five, or a producer who can automate parts of documentation, scheduling, research, and admin work.
But more productivity raises another question: what does the company do with it?
One organization may build more ambitious products. Another may increase output or shorten development cycles. Another may decide that a smaller team can now do what a larger team did before. That's where the conversation gets uncomfortable.
Automation isn't new, and technology has always been about efficiency. What's new is how fast intelligent automation is reaching knowledge work and creative work, including jobs many people assumed were insulated from it. Automation used to mean repetitive physical labor or simple administrative processes. AI is now showing that parts of programming, visual development, writing, research, analysis, design, and management can be accelerated as well.
That doesn't mean those professions disappear. It means the definition of value within them starts to change, and that's where many workers are struggling.
When the Old Advice Stops Working
Five years ago, career advice in technology was fairly simple: keep your skills current, build a strong portfolio, network, learn the newest software, be willing to relocate, and stay flexible.
Those things still matter, but they no longer guarantee the same results. A highly experienced professional who loses a job may now compete with hundreds of other experienced professionals for a single opening. Companies still want senior-level experience, but many have cut the number of senior positions. Someone moving into a new discipline may be up against people who have already spent a decade in it.
Entry-level opportunities are changing too, and this may become one of the most important consequences of AI that the industry has to face.
Every senior developer, art director, engineer, designer, and executive started somewhere. They did smaller tasks, made mistakes, learned how production really works, watched experienced people solve problems, and slowly developed judgment of their own. Many of the tasks AI handles best are exactly the ones traditionally given to junior employees.
Automating that work brings obvious short-term efficiency. Underneath it sits a long-term question: if we remove too many of the opportunities people once used to gain experience, where will the experienced people of the future come from?
You can't keep removing the bottom rungs of a ladder and expect people to reach the top. Companies that understand this will likely have a real advantage in the years ahead. Talent development can't just mean hiring experienced people from somewhere else. Eventually, someone has to create them.
"Learn AI" Isn't Enough
This is also why telling displaced workers to simply "learn AI" feels incomplete. They should learn it. We all should. But knowing one AI application is unlikely to give anyone lasting career security. The tools are changing too fast, and the platform that seems essential today may be replaced by something better in six months.
The more durable skill is learning to work alongside changing technology: knowing which parts of your job can be accelerated, which parts need judgment, and which skills become more valuable as routine work gets easier.
For creative professionals, taste matters more now, not less. When everyone can generate an image, recognizing the right one is what counts. When everyone can generate code, what counts is knowing whether that code is scalable, maintainable, secure, and appropriate. When everyone can produce content, what counts is knowing what's worth producing.
AI can generate possibilities very quickly. It's far less useful at understanding a company's particular context, the personalities on a team, a customer's needs, an organization's politics, or the long-term consequences of a creative decision. Experience still matters there. The professionals who become most valuable may not be the ones who produce the most work. They may be the ones who can combine technology with judgment, communication, leadership, and deep domain knowledge.
Same Pressure, Different Systems
This shift is happening on both sides of the Atlantic, though not in the same way. North America generally has more flexible labor markets, so companies can restructure quickly. Western European countries often have stronger worker protections, longer consultation processes, and different social safety nets.
The underlying pressure is still similar. Companies are expected to do more with less, technology is moving fast, investors expect efficiency, and workers are asked to keep reinventing themselves. The skills companies need often aren't held by the people whose roles are disappearing.
That produces one of the strangest contradictions in today's technology economy: a business can lay off thousands of skilled professionals and, at the same time, complain that it can't find people with the skills it needs.
Both can be true. The problem isn't always a shortage of talented people. Sometimes it's a shortage of the exact skill, in the exact place, at the exact pay level, at the exact moment a company needs it. Those gaps used to be easier to bridge. With technology changing this fast, they can grow much wider.
Adaptation Is a Shared Responsibility
Workers do have a responsibility to adapt, and I believe that strongly. We should keep learning, understand the tools changing our industries, build professional relationships before we need them, and understand the business behind the work we create.
Most of all, we need to get better at explaining the value of what we do. "I created this" carries less weight every year. It's more powerful to say, "Here was the problem, here's how I approached it, here's what changed because of my work, and here's why it mattered to the product or the organization."
But adaptation can't rest on the individual alone. If companies truly believe AI will transform their workforce, then preparing employees for that transformation should be part of leadership. Training people after their jobs disappear is far less useful than developing them before their roles become obsolete.
Companies should look internally for employees who can move into emerging disciplines. They should treat mentorship as organizational infrastructure. Leaders should understand where automation really improves a team, and where removing human knowledge creates problems that may not show up until much later.
Efficiency matters. So do institutional knowledge, trust, and experience. So does the person who remembers why a system was built the way it was, why a customer behaves a certain way, or why the team tried an idea three years ago and learned it didn't work. Those things rarely show up on a spreadsheet, but they can be enormously valuable.
Opportunity, With Honesty
None of this means we should be pessimistic about the future of technology. Quite the opposite.
We're entering a period in which individuals and small teams can build things that once required huge organizations and large amounts of capital. People with ideas but without deep technical backgrounds are getting tools that help them prototype software, games, businesses, and experiences. That democratization could set off an extraordinary wave of entrepreneurship and creativity. Someone who loses a technology job today may find that the same tools disrupting their profession let them build a company of their own.
That possibility is exciting. But we shouldn't confuse possibility with simplicity. For someone supporting a family, paying a mortgage, keeping healthcare, or trying to recover after a layoff, hearing that this is an exciting moment of technological transformation feels very different from living through it.
Both realities deserve acknowledgment. AI is an extraordinary opportunity, and it will also cause disruption.
The technology industry will keep evolving, as it always has. People will adapt, new jobs will emerge, and entirely new industries will be created. But adapting today takes more than downloading the newest software or adding another skill to a résumé. It takes understanding where the industry is going, identifying where human value remains strongest, building relationships, developing judgment, and being willing to rethink what a career can look like.
The technology industry isn't disappearing. It's being rewritten.
The question isn't whether people can evolve with it; history suggests many will. The harder question is how many people we're willing to leave behind while that happens, and whether the companies building the future will recognize that their greatest technological advantage may still depend on the people who know how to use that technology wisely.
