Twenty-five years ago, a young man graduated from high school and began working in the forest industry. It was honest, physically demanding work that seemed capable of providing him with a secure future.
Then an injury made it impossible for him to continue.
At almost any other point in history, that injury might have permanently narrowed his prospects. But this was the beginning of the Internet age. Governments were investing in technology training to prepare people for what was being called the new economy.
He enrolled in a basic programming course and discovered an extraordinary aptitude for technology.
Soon afterward, he joined a small software startup. It was the sort of company where everyone did more than their job description. There were systems to design, problems to solve and applications to build—often with limited resources and no roadmap.
He became one of the company’s most valuable contributors.
He was not simply good at writing code. He could understand a complex operational problem, see how all its pieces fit together, and architect a system that made the whole organization work better. He helped build applications, infrastructure and intellectual property that contributed significantly to the company’s growth.
The startup became an industry leader. It eventually went public and was later acquired by a private equity firm.
For almost 25 years, he enjoyed a career that was financially rewarding and intellectually challenging. Technology had given him a second chance after his injury. It had transformed a high school graduate from the forest industry into an accomplished systems architect and software developer.
Then technology changed the equation again.
Following the acquisition, the company was reorganized. New development tools, automation, and artificial intelligence made it possible to produce software with fewer programmers. Teams were consolidated. Positions disappeared.
After devoting almost his entire working life to one company, he suddenly found himself without a job.
The cruel irony is difficult to miss: the man who had spent his career building systems that made organizations more efficient became a casualty of the next wave of efficiency.
More Than a Lost Job
This is not simply a story about unemployment. It is a story about identity.
When someone has worked for the same organization for nearly 25 years, the job becomes more than a source of income. It provides structure, relationships, professional status and a sense of purpose. The person knows where he fits, what he contributes and why his knowledge matters.
Then, almost overnight, the institutional world he understood disappears.
He may possess decades of experience, but much of it was accumulated inside one company, one product environment and one professional network. He has not needed to write a résumé, market himself, attend networking events or explain his value to strangers for a quarter of a century.
Suddenly, he must do all of those things while competing against younger candidates, global talent, offshore development teams and AI-enabled workers who can produce routine code at extraordinary speed.
The challenge is not that he has no valuable skills. The challenge is that the market may no longer purchase those skills in the same form.
Programming Is Not Disappearing—But It Is Changing
It would be too simple to say that artificial intelligence is eliminating all software careers.
The evidence reveals a more complicated transition. The International Labour Organization estimates that approximately one in four jobs worldwide has some exposure to generative AI, but it concludes that the transformation of jobs is more likely than their complete elimination.
The distinction between a programmer and a software developer is becoming especially important. The U.S. Bureau of Labor Statistics projects that employment for computer programmers—people primarily responsible for writing and modifying code—will decline by 7 percent between 2025 and 2035. It specifically identifies AI and the automation of repetitive programming tasks as contributing factors.
Yet the same agency projects employment for software developers to grow by 10 percent during that period. Developers who analyze user needs, architect systems, manage complexity, address security risks, and connect technology to business outcomes are still expected to be in demand.
That difference may contain the key to this man’s future.
His greatest value was probably never his ability to type lines of code. It was his ability to understand a problem, envision a solution, and build a system that worked.
AI can generate code. It can accelerate testing, documentation and prototyping. But it still requires experienced people to determine what should be built, how different systems should interact, which risks matter, and whether the final product actually solves the customer’s problem.
The opportunity, therefore, may not be to compete with AI as a traditional programmer. It may be to use AI as an experienced systems architect who can now accomplish work that once required an entire development team.
From Employee to AI-Enabled Expert
For most of his career, his experience was packaged as a job.
Now it may need to be packaged as a solution. Instead of presenting himself as a programmer seeking another programming position, he could position himself as someone who helps organizations.
The new economic unit may not be the number of hours he can program. It may be the business result he can produce.
A project that once required ten programmers might now be completed by one highly experienced architect using AI-supported development tools. That creates a threat to the ten traditional positions—but potentially a remarkable opportunity for the person capable of directing the technology.
The difficult part is making that transition.
The OECD’s analysis of 12 million Canadian job postings found that positions explicitly requiring AI expertise accounted for less than 1 percent of total postings during the period studied. It also found that companies were increasingly looking for experienced professionals rather than entry-level AI candidates.
This suggests that simply adding “AI” to a résumé will not be enough. The stronger proposition is the combination of experience, judgment, industry understanding and practical command of AI tools.
The End of the Permanent Skill Set
His story also exposes a larger weakness in how we think about careers.
For much of the last century, people were encouraged to acquire a profession, build expertise, and then use that expertise throughout their working lives. Education came first. Work followed.
That model is breaking down.
The World Economic Forum estimates that nearly 40 percent of the skills required in the workplace will change by 2030. It reports that 41 percent of employers expect to reduce their workforces where AI can automate tasks, while 77 percent plan to invest in upskilling.
The lesson is uncomfortable: expertise is no longer a permanent destination. It is a temporary advantage that must continually be renewed.
That does not mean that decades of experience have become worthless. Quite the opposite. Judgment, pattern recognition, resilience and the ability to anticipate consequences usually come only with time.
But experience must now be combined with adaptability.
The workers most at risk may not be those who lack intelligence or technical capability. They may be those whose considerable expertise is trapped in an obsolete role, an aging platform, or a company that no longer exists in its former form.
Who Is Responsible for the Transition?
We should also resist placing the entire burden on displaced workers.
It is easy to tell someone in his fifties or sixties to “reinvent himself.” It is much harder to do so while dealing with the psychological shock of losing a long-term career, the financial pressure of unemployment, and the realization that the market may not immediately understand the value of his experience.
Companies that benefit from automation should invest meaningfully in retraining and redeployment before eliminating positions. Governments should move beyond introductory technology courses and help experienced workers convert existing expertise into new commercial capabilities. Professional communities should create practical pathways for displaced workers to find projects, mentors, partners and customers.
Retraining should not mean telling a veteran technologist to start from zero beside a recent graduate.
It should mean helping him add AI capabilities to the knowledge he has spent decades developing.
His experience has not disappeared. His intelligence has not diminished. His ability to understand complex systems remains valuable.
What has changed is how that value must be presented, applied, and sold.
Technology has taken away the certainty of his old career.
The question now is whether he can use it to build the next one.
#ArtificialIntelligence #CareerTransition #FutureOfWork #HumanJudgment #Adaptability #Technology
