AI and technical skills: learning faster, breaking down barriers
From PCB design to any new discipline: what has changed "before and after" AI
The design of a electronic board (PCB) It is a complex activity: it requires knowledge
in-depth knowledge of electronics, electrical engineering, telecommunications, signal management, power supply, regulations and
layout best practices. For years it was an area reserved almost exclusively for those who had
a lot of experience behind him.
Today artificial intelligence does not replace these skills, but it radically changes the way it is
possible approaching problems that are little known. PCB design is just one example:
the same argument applies to many other technical and non-technical sectors.
Before and After AI: Two Worlds Compared
Before AI
To tackle a new and complex field, such as the development of an electronic board, the path was
more or less always the same:
- search for basic tutorial on the internet to understand how the process works;
- development of some very simple first card to become familiar with the tools;
- moving on to more advanced tutorials, hoping to find a case similar to the real one;
- reading (often slow and demanding) of the component documentation main;
- autonomous interpretation of concepts taken for granted:
why insert that capacitor in that particular spot, why use a certain topology and not another,
what critical issues to avoid.
This process, however necessary and formative, was often very long and not always
sufficient: the tutorial explaining a specific case did not guarantee knowing how to manage a different project,
because there was a lack of that deep understanding that usually only comes with
years of practical experience.
Furthermore, when a specific doubt arose, the only way was:
- to see if “anyone else in the world” had had the same problem;
- read discussions on forums, blogs, technical documents;
- try to interpret often fragmentary answers, perhaps designed for a different context
from the real one.
After the AI
Artificial intelligence does not eliminate the need for study, reading documentation, and practice.
But it allows you to drastically reduce times and, above all, to fill the gaps
which normally only experience can cover.
In a process that includes AI, the following remain fundamental:
- the reading of the datasheet and application notes,
- watching tutorials and courses,
- direct practice on real projects.
The difference is that, along this whole journey, it's like having
an expert person is always available to ask questions:
- ask what the possibilities might be critical issues of a scheme, a layout or a choice
design; - get possible suggestions alternative components with pros and cons clearly explained;
- ask why a certain component is needed in a certain place, or what might happen if it were removed;
- have the diagram analyzed to identify weak points or typical errors even before
get to physical production.
All this is possible if we use AI in the right way: by providing
detailed context, specifications, constraints, reference documentation.
Without this information, AI risks generating vague or misleading answers.
AI as an accelerator of understanding
The most interesting element is not only the speed, but the type of comprehension that can be achieved.
Many concepts, in technical fields, are usually internalized after years: you understand at a certain point
“because that thing is done this way and not another way.”.
With AI, that “why” can be asked directly:
- Why add this component here?
- What is the practical difference between two schema solutions?
- What problems might I encounter if I choose this path?
This does not replace real experience, but it allows you to
get closer much faster to the way of thinking of someone who has already seen tens or hundreds
of similar cases.
Not just PCBs: a model that applies to many areas
The example of electronic boards is just one among many. The same principle applies to:
- new programming languages,
- design tools,
- legal, fiscal, organizational issues,
- business, marketing, and project management skills.
Before the only way was accumulate hours on manuals, courses, videos, forums,
hoping to find the right information at the right time. Today, this path is
supported by a system that can:
- quickly summarize complex concepts,
- adapt explanations to the current level of the learner,
- answer specific questions about specific cases.
A barrier that is lowered
AI doesn't automatically make everyone an expert, nor does it eliminate the need for study and practice. What it does do
it really is lower the barrier to entry.
If before there was a wall made of
thousands of hours of study difficult to find in the midst of everyday life,
Today that wall becomes much thinner.
This means that:
- Those who start from scratch can achieve significant results in a shorter time,
provided that we put it into play good will and perseverance; - those who are already experts can explore new areas or design alternatives more quickly;
- “Excuses” related to access to knowledge are becoming less and less credible.
Conclusion: fewer excuses, more possibilities
Artificial intelligence is not about turning everyone into instant specialists,
but to remove a significant part of the initial difficulty.
Today, between a person and the possibility of learning something new,
the distance is much shorter. The real difference, more and more,
they do it curiosity and the good will with which one decides
to use the tools available.
