For most of the last century, professionalism was considered a valuable asset. People received education confirming their qualifications and allowed experience to accumulate for decades. For example, a mechanical engineer in 1985 was largely the same specialist in 1995. The entire career structure was based on one quiet assumption: that accumulated, absorbed experience retains its value long enough to be worth acquiring.
Artificial intelligence is shattering this assumption, and in the author's opinion, we are not being honest about what this means because the true version is uncomfortable. The common view is that the shelf life of skills has become shorter, that things simply become obsolete faster. This is true, but it underestimates what is happening. Previous waves of automation eliminated manual and routine labor—tasks that were easy for us to delegate.
However, the current wave is fundamentally different. It targets 'expert' skills that can be absorbed: analysis, drafting, diagnostics, modeling, structured reasoning. That is, exactly what qualifications are meant to certify. When you get a diploma, you confirm that you have absorbed a transferable set of knowledge. This category is now the most automatable, as the model can instantly absorb the same volume of knowledge with almost no additional cost and will never forget a single word of it.
Thus, this is not about skills degrading slightly faster. It is a story of a machine learning an expert's work simultaneously with them, and then performing it for free. In this light, reskilling begins to seem less like staying ahead of the curve and more like running on an increasingly fast treadmill.
A reflective response to this situation is 'more learning.' One must constantly learn, accumulate certificates, and regularly retrain. This is a message that the entire system—employers, educational institutions, people like the author—has every reason to repeat, and therefore it is important to state plainly that the simple version of this assertion is untrue.
Because the phrase 'just learn faster' does something unfair. It takes the problem created by technological pace and employer priorities and places the entire burden of its solution—time, money, anxiety—on an individual. It implies that if you fall behind, it is because you did not try hard enough, even though the ground has been moving all along. And this hides the uncomfortable fact: most acquired knowledge is not retained.
The completion rate of self-paced online learning is known for its low statistics. Transferring knowledge from the classroom to real work is even worse. More learning by itself could never have been the solution to such a complex problem.
None of this means that learning has stopped mattering. It means that we have optimized the wrong level of learning. If a machine can now store expertise, the value of human acquisition of that expertise shifts. It moves from the expertise itself to everything surrounding it: knowing which problems to prioritize; the ability to make judgments to distinguish a good answer from merely plausible one; possessing the trust and context that allows expertise to be applied within a complex organization; the ability to work alongside systems that know as much as you on a narrow topic. A machine can store knowledge. It cannot yet be held responsible for what to do with it.
This is indeed a more complex task than creating a curriculum of facts and procedures. Transferring perishable expertise—the kind that AI is currently commercializing—is what our institutions do well. Creating a robust human layer around it—judgment, insight, the ability to hold uncertainty and still make a decision—is what almost no one does yet. This gap needs to be closed.
This is why the author disagrees with the trendy assertion that diplomas and deep certifications have lost their relevance. You cannot exercise judgment in a field you do not understand; taste is built on foundations, not moods. The deep foundation provided by a serious program allows you to control a machine that speaks fluently but lacks wisdom. The function of this foundation changes—it is not a gate you pass through once and drift downstream, but the base upon which you stand, continuously rebuilding everything above it.
For practitioners, the practical takeaway is less comforting than the advice 'learn to learn,' but more useful. Assume that the specific expertise you are proud of is time-limited. Invest in the layer above it—in judgment, trust, the ability to make sound decisions when the answer is unclear—because this is the part the machine will soon not take over. And beware of those who offer endless courses as the sole solution.
Knowledge is becoming almost free for the first time in history. What remains rare is the human ability to decide what to do with it—which problem deserves the machine's attention, which answer to trust, which risk to take. This ability has always mattered. It was just packaged together with the expertise that had to be earned through hard work. This package is breaking apart. People and institutions that understand that only half was the goal will do more than just adapt. They will create the one advantage that does not expire.
