AI is helping unprepared people appear to be software experts

AI has made it much easier to create something that looks deep. A well-written pitch, a running POC, an architecture diagram with the right terminology, an execution plan with the “correct” steps. In just a few hours, someone can produce materials that previously required real experience and hard-earned expertise.

This is not an attack on AI. AI is a tool, and good tools improve productivity. The problem is something else. It has become inexpensive to appear competent, and in software engineering, a wrong decision is still expensive. Expensive in rework, expensive in recurring bugs, expensive in instability, and expensive in trust between teams and companies.

What changed is not only how solutions are built. It also changed how they are sold. And many organizations still make decisions as if a convincing pitch were evidence of long-term sustainability.

1) The new risk is not failure. It is persuasion without solid foundation

For years, the biggest risk was not being able to build. Today, in many scenarios, the risk is the opposite. Being able to present with excellence without having the depth required to sustain delivery.

AI makes the narrative coherent. It organizes reasoning. It provides vocabulary. It makes the discourse confident. But this creates a dangerous effect: companies start confusing clarity with truth. They confuse a POC with viability. They confuse a well-drawn architecture with one that actually solves real problems.

Anyone who has lived through real software knows where the bill eventually arrives. It arrives in the “what happens when things go wrong?”, in domain exceptions, in integrations with legacy systems, in audit and traceability requirements, in scaling limits, in operational costs. That is the core point.

AI does not create domain expertise. It creates the appearance of expertise.

And when the decision-making process fails to separate those two things, it becomes vulnerable to the pitch.

2) Why this becomes expensive. The damage is not only technical, it is cultural

Bad decisions rarely hurt on day one. At first, they look like a win. The cost appears weeks later, when the team tries to turn a convincing idea into something that survives in the real world.

Then delays show up because the integration was not “just an API.” Bugs reappear under different names because the domain was oversimplified. Friction grows between product, engineering, QA, and operations because the promise became an obligation. Instability increases because no one thought about observability, incidents, and operational ownership.

Over time, the greater damage appears. The organization learns the wrong lesson: “These initiatives never become real products.” The business loses confidence.

The culture starts treating innovation and change as a waste of time.

That is why the problem is not just “a bad solution.” It is the accumulation of multiple poor decisions that slowly erode trust and real execution speed.

3) The correction. A simple filter to protect technical and business decisions

The answer is not to distrust everything. The answer is to evolve the filter. If AI accelerated the creation of proposals, companies need to accelerate their ability to distinguish between presentation and something that is truly sustainable in the medium and long term.

The most effective adjustment is to add a short, explicit step that almost no one formalizes today: domain and expertise validation. This is not a step to evaluate the POC. It is a step to evaluate whether real knowledge exists behind the pitch.

Before investing time and money, conduct a practical review to see whether the solution actually stands. It is essentially a “that sounds good, but what happens in practice?” conversation. An objective discussion to validate what usually does not appear in the pitch: edge cases, trade-offs and constraints, integration impact, and what changes when this moves from pilot to daily operation.

You ask questions that a beautiful presentation cannot answer on its own:

• “On a daily basis, where does this typically break from the ideal flow?”
• “What would you intentionally leave out now to avoid exploding complexity?”
• “What internal systems will this need to integrate with, and what happens if one of them goes down?”
• “When this becomes routine and volume increases, what fails first?”

The person may even use AI to prepare, and that is great. But when real domain expertise is missing, the answers become generic and start to contradict themselves as soon as you ask for a concrete example or go one layer deeper.

This kind of validation takes little time and eliminates a huge number of initiatives that only had surface appeal. More importantly, it protects good initiatives, because they do not need to compete against slide brilliance. They compete on sustainability.

AI has increased the ability to sell ideas. To keep organizations healthy, we now need to increase the ability to decide well. In software engineering, the difference between success and frustration almost always comes down to one simple question: Does this still stand when it meets reality?