There was a time when knowing how to send an email was a skill worth mentioning.
Something similar later happened with searching the internet, using a word processor, or working with a spreadsheet. None of those capabilities stopped being useful. Something else happened: they became so common that they were no longer enough to set us apart.
I think artificial intelligence will follow the same path.
Today, saying “I know how to use AI” can still distinguish one person from another. Soon, it may sound like saying “I know how to use the internet.” Great—we assume that. Now tell me what you know how to do with it.
I am not saying that learning these tools is pointless. The opposite is probably true: they will become so important that some degree of AI fluency will be expected in almost every knowledge job.
And that is precisely why they will stop being a complete explanation of our value.
Every new technology begins as an advantage
Many technologies follow a similar path.
At first, access itself creates an advantage. Few people have the tool, learning it is difficult, and organizations do not yet know where to use it.
Then comes a stage in which using it well genuinely makes a difference. Techniques and specialists emerge, and some people obtain results far above average. We are living through much of that stage with AI: some people are making their first request, while others coordinate agents, connect systems, design evaluations, and automate complete processes.
Eventually, the technology becomes embedded everywhere. Interfaces improve, costs fall, and the capability begins to operate in the background. We do not think about spell-check every time we write. We do not explain that we used a search engine to prepare a presentation.
With AI, systems will choose models for us, familiar tools will include generative capabilities, and many tasks will stop requiring an explicit prompt. The capability will remain, but it will become less visible.
When everyone has access to a sufficiently good version, value will move again.
The same tool does not produce the same result
Two people can use the same model, pay for the same subscription, and spend the same amount of time. They can still produce completely different outcomes.
One asks for a commercial strategy and copies a polished but generic response. The other knows the customers, notices a missing fact, challenges an assumption, tests two scenarios, and turns the result into a decision the team can execute.
The model is the same. Everything the person puts around it is different.
An International Monetary Fund working paper published in September 2026 explores a similar idea at the level of countries. Its argument is that access to technology and knowledge does not guarantee productivity gains by itself. What matters is the capacity to absorb that knowledge, adapt it, and turn it into real results.
The paper studies economies, not individual careers. Applying the idea to people and companies is an interpretation, not a direct conclusion from the research. But it leaves us with an excellent question:
What capacity do we have to turn available intelligence into something useful?
Access to an answer is not the same as knowing what to do with it. AI can democratize capabilities without automatically democratizing results.
The perfect prompt has a short shelf life
During the first years of generative AI, learning to write prompts looked like a new universal profession. Formulas, templates, and lists of words appeared, supposedly capable of unlocking extraordinary results.
Knowing the tool still matters. Providing context, requesting a clear format, dividing a task, and checking the result are useful practices. But the interface is learning too.
Models understand increasingly informal instructions. Products add memory, tools, and context automatically. Some systems rewrite our requests, choose which model to use, and plan several steps without showing us the entire process.
That means an advantage based only on knowing the exact phrase that works may disappear when the product incorporates that technique for everyone.
This does not mean specialties in agent design, evaluation, security, automation, or integration will disappear. Those fields require technical depth and will continue to evolve.
It means something simpler: knowing how to talk to a chatbot will become a basic capability, not a complete professional identity.
AI does not replace domain knowledge; it exposes it
A model can write with enormous confidence about an industry it does not truly understand. It can produce a flawless proposal that violates a regulation, ignores an operational exception, or simply makes no sense for that customer.
Someone with domain knowledge recognizes those failures. They know which fact is missing, which rule was never written down, and which part of the answer sounds good but would be impossible to apply.
We can put that information into the context, of course. But somebody had to know that it mattered, find it, organize it, and keep it current.
AI lowers the cost of producing a first answer. That can make the difference between knowing an industry's vocabulary and understanding how it actually works more visible.
If everyone can generate a financial plan, it matters more who understands the business behind the numbers. If everyone can produce code, it matters more who understands the system, risks, and users. If everyone can write a campaign, it matters more who knows the audience and can distinguish an eye-catching idea from a sensible commercial decision.
Technical and human skills are not competing
The World Economic Forum's Future of Jobs Report 2025 places artificial intelligence and data among the fastest-growing skills through 2030.
That confirms that learning AI matters.
The same report also highlights analytical thinking, creativity, resilience, flexibility, curiosity, lifelong learning, leadership, and systems thinking. It does not present a choice between technological and human skills. It presents a combination.
The easier it becomes to generate an answer, the more important it is to formulate the problem. The more content we can produce, the more important it becomes to decide which content deserves to exist. The more actions an agent can execute, the more necessary it becomes to establish limits and take responsibility.
Even calling these “soft skills” begins to sound strange. Listening carefully to a problem, negotiating a priority, explaining a decision, and building trust are not decorations around the work. They often are the work.
Seven capabilities that can make the difference
If using AI becomes a baseline requirement, where can differentiation come from? I see seven capabilities becoming increasingly valuable.
1. Domain knowledge
Understanding how a field actually works: its constraints, exceptions, incentives, and consequences. Not merely speaking its vocabulary.
2. Formulation
Choosing which problem is worth solving, separating symptoms from causes, and defining what a useful solution looks like. A system can answer a badly framed question with remarkable efficiency.
3. Evaluation
Checking facts, searching for contrary evidence, identifying assumptions, and not mistaking a convincing answer for a correct one. An abundance of outputs increases rather than reduces the need to judge them.
4. Judgment
Deciding what is good enough, which risk is acceptable, and what should not be done even when it is technically possible. Judgment is usually built by encountering real problems, not by reading a list of best practices.
5. Integration
Taking an idea all the way to a working process. Connecting data, people, tools, and decisions. Closing the final gap where so many impressive demonstrations become stuck.
6. Communication
Listening to what somebody needs, making uncertainty visible, explaining a decision, and building agreement. An output nobody understands or accepts rarely creates value, even if it was generated in seconds.
7. Responsibility
Being able to say: I understand what we did, I know why we did it, and I will review it when conditions change. The tool can execute part of the process; somebody still needs to own the outcome.
AI can assist us with all seven. None belongs exclusively to humans forever. The point is that the advantage does not live inside an isolated prompt, but in the complete combination.
An uncomfortable and useful question
There is a simple test for thinking about our own work:
If everyone in my profession had access to the same model tomorrow and knew how to use it, what would still make my contribution valuable?
This is not a question designed to frighten us. It is a way to discover where we should invest.
Perhaps it is a customer relationship built over years. An understanding of a complex system. The ability to teach. Aesthetic judgment. Experience from watching similar projects fail. The ability to connect teams that do not usually speak to one another. Or the persistence required to carry an idea to completion.
The same question applies to a company.
If every competitor can access similar models, what allows the organization to achieve a different result?
It may be data quality, process clarity, customer trust, difficult-to-copy distribution, or the speed at which its teams learn. The advantage will not be simply possessing AI. It will be embedding AI in a capability that a new account cannot reproduce overnight.
Do not skip the stage of learning the tool
There is a risk in this argument: using it as an excuse not to learn AI.
“Judgment is what matters,” we say, and continue working exactly as before. That does not work either.
To use a technology with judgment, we need to know it. We must understand what it can do, how to give it context, which mistakes it makes, what it costs, which information we should not share, and when another tool is more appropriate.
Basic fluency will be necessary precisely because AI will be everywhere. Without it, someone may be at the same disadvantage as a person today who cannot search for information or work with digital documents.
The distinction is this:
Learning AI gets you onto the field. It does not guarantee that you win the game.
The advantage will live before and after the answer
Knowing how to use artificial intelligence will not stop mattering. It will become infrastructure: expected, integrated, and increasingly invisible.
Differentiation will move toward what we do before and after the answer. Choosing the problem. Supplying context. Evaluating. Deciding. Communicating. Integrating. Taking responsibility for the result.
When available intelligence becomes abundant, judgment does not lose value. It becomes the place where we decide how to use that abundance.
So I would not stop learning AI. I would learn it while asking a more demanding question:
Which capability of my own am I amplifying with it?
Soon, many people will be able to generate a report, an image, an analysis, or a block of code. The difference will lie in who can turn that material into something true, useful, and trustworthy for somebody else.
If you would like to keep thinking with me about artificial intelligence, software, and work, I share new experiments and reflections on my YouTube channel.