Competency Trap

Those who do something often get better at it. This applies to individuals, teams, and entire corporations. The competency trap describes the flip side of this. One sticks to what one has mastered and never even tries the unknown. The reason is not stupidity but experience: the proven approach demonstrably works well. Only later does it become apparent that a different approach would have been better – but one never practiced it long enough to notice.

Why market leaders fail because of their own strengths

The term originates from organizational research in the 1980s and has gained renewed attention through the AI wave. It explains a pattern that recurs again and again in economic history. A company dominates its market, misses a technical upheaval, and disappears. From the outside, this looks like inertia. In fact, the company usually acted very rationally – just with too short a time horizon.

A well-known example is Kodak. The company was the world champion in the film business and earned money on every roll of film sold. The digital camera was even co-developed there. But every comparison spoke in favor of film in the short term: better image quality, higher margins, well-established production. By the time digital photography caught up, others' lead could no longer be overtaken.

For investors, this pattern is interesting because it is difficult to read from balance sheets. A company caught in the competency trap often looks especially profitable shortly before its collapse. It cuts costs precisely where the future would lie. It is important to distinguish this from mere stubbornness: what is missing here is not the will, but experience with the alternative.

The mechanism of experience and feedback

Behind this lies a feedback loop, that is, a cycle that reinforces itself. One chooses a method, gains experience with it, and gets better. Because it then delivers better results, one chooses it again next time. The alternative remains unpracticed and performs poorly in every test. Its true potential remains invisible because no one invests in it long enough.

In AI research, the same problem goes by the name exploration-exploitation dilemma. A learning system must constantly decide: do I use the option with the best result so far? Or do I try an unknown option that might be even better? Those who only exploit end up with a mediocre solution and get stuck there. Those who only explore never arrive at a stable result.

An everyday comparison captures this well: you’ve been going to the same restaurant for years because it’s reliably good. Two streets away there might be a better one – but you’ll never find out. In practice, this problem is addressed with fixed testing budgets. Companies deliberately set aside a share of money, time, or personnel for approaches that don’t yet pay off.

The competency trap in tech news and in one’s own learning

In current reporting, this idea appears almost daily, usually without the technical term. When analysts ask whether an established search engine or software company is sleeping through the AI transition, they mean exactly this pattern. The debate about carmakers and electric drivetrains follows the same logic. Decades of experience with combustion engines are an advantage – as long as the market stays with combustion engines.

The same applies within the AI industry. Those who rely heavily on a particular model architecture or chip manufacturer build knowledge, tools, and personnel around it. As a result, switching becomes more expensive every year, even if the alternative is technically superior. Experts call this binding effect the lock-in effect.

The principle also holds on a small scale. Those who always use the same learning method because it has proven itself may well be missing a more effective one. Incidentally, a common misconception is that the competency trap is an argument against specialization. It is not. It is an argument for always putting a small share of resources into experimentation alongside the proven approach.