What Could We Become Because of AI?
Luis Vicente García is a business coach, international speaker, and best-selling author. He is known for helping entrepreneurs and leaders elevate performance through mindset, motivation, and strategic leadership.
For the past few years, much of the conversation about artificial intelligence has focused on what AI will be able to do. What tasks will it automate? Which jobs will change? Which professions will be most affected? How much more productive can organizations become? Inevitably, what will machines eventually be able to do better than we can?
These are understandable questions. I ask many of them myself and with my students. Like many professionals, I am learning to use AI while simultaneously trying to understand how profoundly it may change the way we work, lead, learn, and make decisions. The speed of development makes that difficult. Just when we think we understand what these technologies can do, another capability appears and moves the boundary again.
But lately, I have found myself increasingly interested in a different question. Instead of asking only what artificial intelligence will become, perhaps we should also be asking what we might become because of artificial intelligence. That may require us to rethink the relationship itself.
From competition to collaboration
The first chapter of the AI conversation was largely framed as Human vs. AI. We compared capabilities. We asked which tasks machines could perform better, faster, or less expensively than people. We tried to identify which professions were vulnerable and which human abilities would remain difficult to replicate.
That conversation has not disappeared, nor should it. AI will continue to automate activities and reshape work. But millions of people are already discovering that another relationship is possible. Instead of competing with AI, we are increasingly working with it. The equation has moved toward Human + AI.
I experience this regularly in my own work. AI can help me explore a subject, organize information, compare alternatives, question an idea, prepare a conversation, analyze a document, or turn an initial thought into something I can examine more carefully. None of those activities requires me to surrender responsibility for the final result. At its best, the technology becomes a collaborator that allows me to work with more information, more alternatives, and greater speed than I could reasonably manage alone. But even Human + AI may describe only part of what is happening.
Addition suggests that we combine two sets of capabilities. The human contributes something, the technology contributes something else, and together they produce a better result. What interests me now is whether the relationship can go further. Could AI not only add capabilities to our work but also multiply capabilities within us? That is a different proposition.
From Human + AI to “Human × AI”
Most organizations understandably approach AI through the lens of productivity. Can we complete the task faster? Can we analyze more information? Can we automate part of the process? Can we improve quality, reduce costs, or increase output?
These benefits matter. But productivity measures what happens to the work. They do not necessarily tell us what happens to the person doing the work.
Suppose I use AI to produce a report in thirty minutes that previously required four hours. My performance has improved dramatically, and the report may be better. But have I become more capable? Perhaps. Perhaps not.
Now imagine that instead of asking AI simply to produce the report, I use it to examine my assumptions, challenge my interpretation of the evidence, identify what I may have overlooked, present an argument against my preferred conclusion, or ask me questions that force me to explain why I believe what I believe. The immediate output may still improve, but something else can happen during the process. I may improve as well.
This is what I mean by moving from Human + AI to Human × AI. The opportunity may not lie only in combining human and machine capabilities. It may lie in using the interaction to amplify our own capacity to think, question, explore, learn, create, and decide.
That possibility changes the conversation because it asks us to evaluate AI not only by what it produces for us, but also by what working with it may develop within us.
More answers require more discernment
There is, however, an important complication. Greater access to intelligence does not automatically produce better decisions.
AI can provide information, generate alternatives, construct arguments, identify patterns, summarize complex material, and offer recommendations at extraordinary speed. As those capabilities improve, the challenge may increasingly shift from obtaining an answer to determining what to do with the answers we receive.
This is where discernment becomes increasingly important. Discernment is more than finding out whether an answer is technically correct. It requires asking whether the answer is relevant to the situation, what assumptions it contains, what context may be missing, which consequences have not been considered, what deserves further verification, and whether we are even asking the right question.
The interesting paradox is that AI itself can help us develop some of that discernment. We can ask it to challenge our conclusions rather than confirm them. We can request alternative interpretations. We can ask what evidence would invalidate our position or which stakeholders might see the situation differently. We can use it to expose ourselves to arguments we would not naturally consider.
But eventually, we must also exercise discernment over what the AI produces. The technology can help expand the field of consideration. It cannot relieve us of responsibility for deciding what deserves our confidence and what we should ultimately do.
Perhaps, then, one of the most valuable ways to use AI is not simply to obtain better answers, but to help ourselves become better questioners of the answers we receive.
A multiplier does not decide what it multiplies
There is another side to the idea of Human × AI that we should not ignore. Multiplication is not inherently positive.
If I approach AI with curiosity, it can help me explore further. If I approach it with a desire to test my thinking, it can help me discover weaknesses in an argument. If I bring expertise, it can allow me to apply that expertise across a much wider field of information and possibilities. But the opposite can also happen.
If I begin with a poor assumption and use AI only to reinforce it, I may end up with a much more sophisticated version of the same mistake. If I seek confirmation rather than understanding, AI can help me construct persuasive arguments for what I already believe. If I routinely delegate thinking rather than use the technology to stimulate thinking, I may become more productive while gradually becoming less capable of doing some of that thinking myself.
This is why I am increasingly interested not only in the power of the multiplier, but in what we bring to the multiplication.
Technology can amplify capability, but it can also amplify dependency. It can broaden curiosity or satisfy it too quickly. It can expose us to different perspectives or help us defend our existing ones more convincingly. The outcome depends partly on the technology, but also on the habits, judgment, intentions, knowledge, and character of the person using it. The more powerful the multiplier becomes, the more important those underlying human qualities may become.
Performance is not the same as capability
This distinction may become especially important for organizations. Today, many measures of AI adoption focus understandably on performance: time saved, tasks automated, costs reduced, productivity gained, output increased. Those measures tell us whether the technology is improving the economics or effectiveness of work.
But perhaps we should eventually ask another set of questions:
Are the people using AI also becoming more capable?
Are they understanding problems more deeply?
Are they asking better questions?
Are they learning faster?
Are they recognizing assumptions they previously missed?
Are they improving their judgment?
Can they do something today that they could not do six months ago, not merely because an AI system can do it for them, but because working with that system has helped them develop?
There is a simple thought experiment that helps me distinguish between the two. Imagine that AI disappeared tomorrow. What would remain in us from having worked with it?
If all that remains are the reports, presentations, analyses, and other outputs we produced more efficiently, then AI improved our performance. That is valuable.
But if something also remains within us, a better way of asking questions, greater knowledge, a broader perspective, stronger judgment, or a new capability, then something more significant has occurred. The technology has contributed not only to our performance but to our development. That difference deserves much more attention.
Beyond multiplication
This leads me to an idea I am still exploring. If Human + AI represents collaboration and Human × AI represents amplification, perhaps there is another possibility beyond both: Humanᴬᴵ.
I do not mean this as a mathematical formula or as another prediction about human and machine integration. I use it simply as a way of thinking about a different relationship with artificial intelligence.
Human × AI asks: What can I do better because I work with AI?
Humanᴬᴵ asks a more demanding question: What might I become because I work with AI?
The distinction is subtle but important. The first is principally about extending capability in the moment. The second is about your personal development over time.
Could sustained and intentional interaction with AI help us become more curious because we can explore questions further? Could it strengthen our thinking because we regularly expose our assumptions to challenges? Could it improve our discernment because we learn to compare perspectives rather than accept the first plausible answer? Could it accelerate learning by allowing us to engage continuously with explanations, questions, simulations, feedback, and alternative points of view?
I do not think we yet know the full answer, and we might never know. There are obvious risks in the opposite direction. Dependence can weaken capability, convenience can discourage effort, and constant access to answers can reduce the productive struggle through which understanding sometimes develops. That is precisely why the question matters.
The transition from Human + AI to Human × AI, and perhaps eventually Humanᴬᴵ, is not something technology will accomplish for us. It depends on how we choose to use it.
Developing the human side of the equation
This question connects with something I have been exploring for some time through what I call Human Architecture™: the underlying human foundations from which our visible behaviors and capabilities emerge.
The connection with AI now seems increasingly important to me. Technological capability can develop extraordinarily quickly. Human capability does not automatically develop alongside it. Judgment, curiosity, adaptability, responsibility, trust, courage, and purpose require experience, reflection, practice, and intentional development. AI does not eliminate that need. It may make it more important.
If artificial intelligence becomes a powerful multiplier of human capability, then we should care deeply about the human side of the equation. What are we multiplying? What habits of thinking are we reinforcing? What capabilities are we strengthening? What are we delegating that we should perhaps continue practicing ourselves? What are we now capable of developing that would have been much harder without these technologies?
These questions move the conversation beyond productivity. They ask us to consider AI not simply as a tool for accomplishing more, but potentially as a tool for becoming more capable.
What could we become because of AI?
We will continue asking what AI will become. We should. Its capabilities, risks, economic consequences, and impact on work deserve serious attention. But that is only one side of the relationship. We should also begin by asking what we might become because of it.
Could we become better thinkers because we have something capable of challenging our thinking whenever we ask? Could we become more curious because the cost of exploring a question has fallen dramatically? Could we develop greater discernment as we learn to navigate an environment filled with increasingly sophisticated answers? Could experts extend their capabilities into areas that were previously beyond their reach? Could people learn, reflect, and develop in ways that were previously available only with access to resources, experts, or experiences they did not have?
None of these outcomes is guaranteed. The same technology could make us more dependent, more passive, or more willing to accept plausible answers without understanding them. The direction is not determined by the technology alone. Perhaps that is why the most important question about artificial intelligence is not only what it will allow us to do.
It is what we will choose to become because of it. The deepest value of AI may ultimately be measured not only by what it helps us produce, but by what working with it helps us develop within ourselves.
Read more from Luis Vicente Garcia
Luis Vicente Garcia, Business Performance, Leadership and Success Coach
Luis Vicente García is a business performance coach, international speaker, and best-selling author with over 35 years of experience in leadership, motivation, and strategic growth. A former CFO and CEO, he now empowers professionals through Incrementum Academy and his signature concept, Motitud, the fusion of motivation and positive attitude. Certified by Brian Tracy and Jack Canfield, Luis helps entrepreneurs and leaders unlock their full potential. He writes regularly for global platforms and is a recognized voice on mindset, productivity, and leadership transformation.
