What is Share of Life and why is it changing how we measure brands?
The other day, while meeting with a group of students, they told me something that caught my attention. They had attended a class on artificial intelligence expecting to find what, at first glance, anyone would imagine: programming, prompts, automation, code, agents, or some of the new tools that seem to multiply every week. However, the professor had decided to start from a completely different place. Instead of talking to them directly about technology, he told them about Socrates .
Some confessed to me that at first they didn’t quite understand what a philosopher who lived almost two thousand five hundred years ago could have to do with a technology that seems to be defining our present and that will surely transform a good part of our future.
For that very reason, I found it to be an extraordinarily intelligent and provocative way to begin.
Perhaps one of the mistakes we’re making when talking about artificial intelligence is assuming that everything important we need to learn about it is related to the technology. Knowing how to use the tools will certainly be essential, but knowing how to use them doesn’t guarantee that we’ll know what to use them for, when to use them, what deserves to be delegated, or how to distinguish a convincing answer from a true one .
The more capable machines become of answering questions, the more important our ability to ask questions becomes. The easier it is to produce information, the more necessary it becomes to learn to distinguish between information, knowledge, and judgment . And the more convincing a machine-generated answer may seem, the more relevant becomes something that Socrates made central to his understanding of the world: asking ourselves why we believe what we believe.
“What does it really mean to know something?”
The connection, when you begin to observe it, ceases to seem extravagant. It begins to seem inevitable.
For years we’ve insisted that preparing for the future meant learning programming, mathematics, data science, engineering, and technology. And, of course, all those disciplines remain fundamental. The problem arises when we confuse a part of the future with the entire future and relegate everything related to philosophy, history, anthropology, psychology, art, or sociology to a kind of elegant museum of human knowledge.
It is curious that now, when artificial intelligence is reaching extraordinary levels of sophistication, some of the most advanced technology laboratories are returning precisely to those disciplines.
Not because Silicon Valley has suddenly discovered a collective passion for Greek philosophers, but because many of the most complex problems in artificial intelligence are no longer exclusively engineering problems.
When a machine has to decide how to respond to an ambiguous situation, when it should contradict the user, what to do when two values conflict, how to distinguish between helping and manipulating, or when it should recognize what it doesn’t know, we enter a territory where writing better code doesn’t completely solve the question.
Because it’s one thing to ask ourselves what a machine can do .
And another quite different thing is asking ourselves what we should do .
That change of verb opens up a whole universe.
Why do questions matter more than ever?
Socrates had a rather unique way of teaching. If we were to apply it to the present day, it would probably be frustrating for anyone accustomed to instant answers. One might approach him expecting certainty and end up discovering ten new doubts.
His method consisted of asking questions, requesting definitions, challenging seemingly obvious statements, and guiding his interlocutor to a place where what appeared to be a certainty began to show cracks. He wasn’t simply trying to prove the other person wrong. He was trying to get them to think more clearly .
There is something originally contemporary about that attitude.
We are entering a stage in history where getting answers will be extraordinarily cheap. Artificial intelligence can explain a theory, synthesize hundreds of pages, compare scenarios, write code, prepare a marketing campaign, analyze a market, propose a strategy, or generate dozens of creative alternatives before we’ve even finished formulating the problem.
Just a few years ago, having certain information could provide an advantage. Then the advantage lay in knowing how to find it. Now it’s shifting to something else: knowing how to interpret it, question it, relate it to other information, and decide what to do with it .
This change seems crucial to me because for a long time we’ve associated intelligence with knowing how to answer. Perhaps the coming years will force us to associate it also with knowing how to ask questions .
Not just any question, but one that can pierce the surface of a problem, reveal what we are taking for granted, and force us to look at it from a different perspective.
I was trying to imagine the question that came before “I only know that I know nothing.”
Was it “What do I really know?” Or perhaps, “How do I know that I know?” or “What does it really mean to know something?” or maybe, “How much of what I think I know, do I really know?” or “What if what I think I know isn’t true?”
Socrates still has much to teach us.
Artificial intelligence and critical thinking: what are we really multiplying?
There is an understandable fascination with the multiplying power of artificial intelligence. We can produce more, analyze more information, experiment with more possibilities, and do it in far less time.
But there’s a prior issue that we may be overlooking too quickly. Artificial intelligence multiplies.
The question is what we are multiplying .
Because a great idea amplified by artificial intelligence can become something extraordinary. But a bad idea amplified by artificial intelligence is still a bad idea; it just can now be produced at a speed, scale, and level of formal quality unknown until recently.
A flawed strategy can be executed flawlessly. A prejudice can find hundreds of arguments to confirm it. A brief built on a false premise can produce dozens of technically brilliant campaigns. A company that doesn’t know who it is can generate thousands of pieces of content trying to explain who it is.
And a brand with nothing relevant to say can start saying so much more.
Perhaps one of the less discussed risks of artificial intelligence is not only that it multiplies our intelligence, but that it can also multiply our mediocrity, our biases, our haste, and our superficiality .
Previously, making mistakes on a large scale was relatively expensive. Now we can also automate making mistakes.
Therefore, questioning the premise before accelerating execution can become one of the most important professional skills in this new phase. Speed is extraordinary when we know where we’re going. When we don’t, it simply allows us to get lost faster.
And here Socrates reappears. Before answering, ask. Before producing, understand. Before climbing, question.
The problem with an artificial intelligence that always agrees with us
There is a particularly interesting phenomenon in current research on artificial intelligence known as sycophancy : the tendency of certain systems to be excessively accommodating to the person using them, adapting the response to what that person seems to want to hear.
It’s hard to imagine a more human paradox.
We are investing extraordinary amounts of money, talent, energy, and computing power to build increasingly powerful intelligences, and at the same time, we have to teach them something that we ourselves have not yet finished learning: helping does not always mean pleasing .
Imagine a marketing meeting. A team presents a new campaign, and someone asks an artificial intelligence: “Do you think this idea will resonate with Generation Z ?” In a few seconds, they can receive a beautifully crafted response explaining that the proposal has potential because it connects with authenticity, purpose, community, sustainability, and engagement.
It all sounds reasonable. Perhaps too reasonable. Because we may not have asked anything important yet.
What exactly do we mean by Generation Z? Are we talking about a seventeen-year-old or someone approaching thirty? From Madrid, Buenos Aires, Mexico City, or Miami? What does “connect” mean? Remembering the campaign? Sharing it? Buying? Changing their perception of the brand? Do we have real evidence that these values determine their behavior, or are we just repeating clichés that have been circulating in corporate presentations for years?
And perhaps the most important question of all: what would happen if our initial hypothesis was incorrect?
In ten minutes of conversation, we could discover that the problem was never with the campaign, but with the briefing. That’s also strategy.
Artificial intelligence that systematically confirms what we already think can increase our confidence. Artificial intelligence that forces us to reconsider how we came to think that way can improve our judgment.
And they are not the same.
Why do brands also need Socrates?
There is another area where this conversation seems especially relevant to me: brands .
For years we have argued that building a brand is not simply about communicating more, selling more, producing more, but about understanding who you are, what you represent, what you promise and, above all, what you are willing to demonstrate with your actions .
Artificial intelligence now offers us virtually unlimited production capacity. We can generate names, slogans, manifestos, campaigns, videos, images, publications, and conceptual territories in a matter of minutes. Fantastic. Fabulous. Applause.
But before asking a machine what our brand should say, perhaps we should ask ourselves a much more “Socratic” question: do we know what it means?
Because no tool can solve from the outside an identity that was never defined from within.
Before searching for the next claim, we should ask ourselves what promise we are willing to keep. Before producing twenty pieces of content, what do we really have to contribute to the conversation? Before using artificial intelligence to amplify our voice , is that voice recognizable, relevant, and truly our own?
Brands should also recapture some of the Socratic method.
What do we think we are? Why? Do our customers think the same? What do we do that demonstrates what we say? What would happen if we stopped communicating for six months? Would our brand still be noticeable in the way we act, serve, design, innovate, hire, or solve problems?
These are considerably more challenging questions than asking for ten copy alternatives. They are also much more important.
Because when content creation becomes virtually free, content itself will cease to be an advantage. The cheaper it is to create, the more valuable it becomes to have meaning.
That change can profoundly transform the way we understand branding.
From the economics of attention to the economics of judgment
In previous posts on this blog, we’ve discussed the attention economy . Thousands of companies, platforms, and creators compete daily for a few seconds of our time in a context of ever-increasing abundance.
Artificial intelligence will take that abundance to another level. If producing texts, images, music, videos, and campaigns becomes extraordinarily easy, the volume of available content will be difficult to imagine. We’ll have more things vying for our attention than human capacity to process them.
That’s why I suspect the next big battle won’t just be about getting attention. It will be about getting consideration .
That someone thinks that what we say deserves to be heard, that it deserves to be believed, that it deserves to be remembered and, above all, that it deserves our time.
We could move from an economy dominated exclusively by attention to an economy of judgment . And that also affects brands.
And that is closely related to how often a person considers it worthwhile to pay attention to a brand (and not to others).
There’s a huge difference between being visible and being relevant . Artificial intelligence can help us generate more visibility. Relevance will remain much more difficult.
What should be delegated to artificial intelligence and what should be retained in human capabilities?
Much of the public conversation about artificial intelligence understandably focuses on the capabilities machines are acquiring. We wonder what professions they will transform, what tasks they will be able to automate, when they will perform certain functions autonomously, and how far their reasoning abilities will go.
These are necessary questions.
But perhaps we have devoted less time to the opposite movement: What human capabilities can we stop exercising when a technology begins to perform them for us?
Every technology modifies our relationship with some skill. Digital maps transformed how we find our way around. Search engines changed our relationship with memory. Social media profoundly altered our attention span. The calculator changed our everyday relationship with certain calculations.
Artificial intelligence can affect something even deeper because it directly enters spaces that we have previously associated with thought: writing, interpreting, synthesizing, arguing, creating hypotheses, organizing ideas, and even deciding.
That doesn’t mean we should reject it. That would be as absurd as rejecting the calculator to protect our arithmetic abilities.
The truly interesting question is deciding what deserves to be delegated and what we need to continue practicing .
We can ask a machine to analyze ten thousand documents without sacrificing our understanding of the problem we’re investigating. We can use it to generate twenty creative alternatives and still retain the responsibility of deciding which one makes sense. We can automate part of the process without automating our ability to judge the outcome.
The difference isn’t between using artificial intelligence and not using it. It’s between using it to think less and using it to think better .
Has the return of the leader who knows how to ask questions begun?
All of this has a consequence that transcends technology and directly impacts leadership .
We believe a good leader is someone who has answers. Someone who knows where to go, makes decisions, and provides reassurance when others are uncertain.
In an environment where artificial intelligence can present twenty possible paths in seconds, that idea of leadership also needs to evolve.
Perhaps the value of a leader lies not so much in possessing all the answers as in knowing how to formulate the questions that improve the thinking of an organization .
This is something Darwin raised in “On the Origin of Species,” which is a great example of how one great scientific answer generates hundreds of new questions . “If there are no doubts, there is no progress.”
A company might ask how to increase its sales by x percent and receive multiple possible strategies. It might ask how to automate certain processes, how to reduce costs, or how to grow in a new market. All sensible, reasonable, and expected questions.
But there are questions that an organization should continue to ask itself.
What kind of growth do we want? What are we willing to sacrifice to achieve it? What are we unwilling to sacrifice? What do we want to remain when we are twice the size? Are there opportunities that, while profitable, are inconsistent with what we stand for? What decision would we make if quarterly results weren’t the only criterion? Are we indifferent to how we achieve our goals?
Leadership is not just about choosing between options. It’s also about deciding which questions deserve to guide our decisions .
It is highly likely that much of the leadership of the future will be less oracular and more Socratic. A leadership less obsessed with demonstrating knowledge and more capable of building thinking organizations.
I always remember a boss who used to tell me over and over again: “Andy, the stupidest question is the one that’s never asked. Always ask.”
Why will critical thinking be a competitive advantage in marketing and advertising?
When I was talking to those students, I thought that probably few disciplines need this conversation as much as advertising, marketing, and branding .
We work with concepts that we use every day as if they were extraordinarily clear: authenticity, purpose, trust, relevance, community, differentiation, engagement, value, experience.
Socrates would have thoroughly enjoyed a marketing meeting.
I probably would have spent the first hour asking us what exactly each of those words meant.
We say a brand must be authentic. But what does it really mean to be authentic when behind it are thousands of people, different markets, shareholders, business interests, and a deliberately designed strategy?
We say we need to build community . But is every audience a community? Does having followers imply a real connection between them?
We say a campaign has been relevant because it garnered millions of views. Does attention guarantee relevance? And does relevance guarantee business?
We say we need to get to know the consumer better . But if we were to get to know them perfectly, should we use absolutely everything we know about them to persuade them?
These are marketing questions, but they are also questions about human behavior, ethics, psychology, and philosophy .
Artificial intelligence doesn’t eliminate them. It makes them more urgent.
Because we are entering a scenario where production will become increasingly simpler. Generating fifty headlines, one hundred images, twenty creative concepts, or multiple versions of a campaign may no longer be a significant barrier.
When production is democratized, criteria are revalued .
Two companies can use the exact same technology and generate hundreds of possibilities. However, one will create meaning, while the other will increase the noise.
The difference will lie less in the tool itself and more in the questions that preceded its use.
Does a separation between artificial intelligence, philosophy, and the humanities make sense?
sciences and the humanities had been treated as separate territories. On one side, that which measures, calculates, constructs, and demonstrates. On the other, that which interprets, questions, understands, and seeks meaning.
Artificial intelligence is showing just how insufficient that division was.
We can build a machine capable of making decisions, but we immediately have to ask ourselves what we mean by a good decision. We can get it to follow instructions, but we will need to decide when it should refuse to follow them. We can teach it to answer, but we will have to determine when it should admit that it doesn’t know.
And the more autonomous and capable these systems are, the less we can escape questions that have accompanied humankind for millennia: what is true, what is just, what is good, what responsibility do we have for what we create, and how do we know that we know ?
These aren’t old questions. They’re permanent questions.
Perhaps the big mistake was thinking that philosophy belonged to the past when, in reality, it was waiting for technology to advance far enough to make it necessary again.
Can we teach people to think instead of memorizing answers?
The conversation with those students left me thinking especially about education .
If an artificial intelligence can write a properly structured essay on virtually any topic in seconds, perhaps the solution is not simply to develop increasingly sophisticated mechanisms to prevent a student from using it.
Perhaps we should ask ourselves what we really want to evaluate.
If we want to test whether someone can organize information, a machine can help. If we want to test whether they understand a problem, can defend a position, detect a contradiction, revise an idea when new evidence emerges, and formulate questions that reveal depth, we will have to design different educational experiences.
Interestingly, the more sophisticated technology becomes, the more value is regained by something extremely old: conversation .
A teacher asks why. The student tries to answer. The teacher questions part of the answer. Another student introduces a perspective that neither had considered, and gradually, a seemingly solid certainty begins to take shape as thought.
Something happens there that a perfectly written document doesn’t always allow you to see. We are observing how someone thinks.
Perhaps education in the age of artificial intelligence needs less obsession with checking if we know an answer and more interest in discovering how we got to it .
And to enhance what my boss taught me when I was just starting my professional career: ask, ask, and ask.
Will human judgment still exist in the age of artificial intelligence?
There are a huge number of people trying to learn how to use artificial intelligence better. I think that’s fantastic and necessary. Learning to write good prompts, understand agents, automation, models, and new tools will become part of our professional literacy.
But we can perfectly master a tool and still use it to answer a mediocre question. Technology doesn’t automatically correct a lack of depth.
That’s why I suspect that one of the great competitive advantages in the coming years won’t be strictly technological. It will lie in the ability to combine technology with curiosity, culture, critical thinking, sensitivity, and a genuine willingness to question our own ideas .
Tools will tend to become more democratic. Access will become increasingly widespread, and many capabilities that seem extraordinary today will soon be commonplace.
The criteria will be another story. Criteria aren’t downloaded. They’re built.
It is built by reading, observing, conversing, traveling, listening to people who think differently, coming into contact with disciplines that apparently have no relation to our own, making mistakes and learning to live with the discomfort of not always having an immediate answer.
That’s why I was so pleased that the class on artificial intelligence began by talking about Socrates. At first glance, it seemed the professor had strayed from the topic. In reality, he had gone straight to the heart of the conversation.
Because the big conversation about artificial intelligence has never just been a conversation about machines. It’s a conversation about us. About what we want to delegate, what we want to preserve, how we want to work, how we want to learn, how we want to lead, what we want to create, and what kind of relationship we want to build with an intelligence that will be increasingly present in our lives.
The future will be neither exclusively human nor exclusively artificial. Most likely, it will be profoundly hybrid (perhaps it already is). And that’s why the important question, which we’ve been asking ourselves for the last three years, isn’t who replaces whom, but what place we choose to occupy within that relationship .
Machines will have more and more answers, and many of them will be extraordinarily good. Our responsibility will be not to become so enamored with that abundance that we forget that a brilliant answer to a bad question is still a bad answer . And that there are no good or bad questions. But as my brother Sergio told me years ago, the best questions usually get the best answers.
Socrates never used ChatGPT, never wrote a prompt, and never imagined a language model. But he understood something essential before many of us: our true power lies in retaining the curiosity, critical thinking, and humility necessary to question whether what we’re being told actually makes sense.
Perhaps therein lies one of the great paradoxes of our time.
To learn to live with the most advanced technology we have created, we may need to recover one of the oldest intellectual tools we know: the ability to ask questions, like Socrates. “What is justice?” ( the great question of The Republic). “Can virtue be taught?” ( central to Meno and Protagoras). “What does it truly mean to know something?” (the core of Theaetetus) . “Is it better to suffer injustice or to commit it?” (one of the most provocative questions in Gorgias) . “How should we live to live well?” (so relevant it seems written today).
And to finish, my question to you who have come this far: “How much of what we think we know, do we really know?”
I write to you when I have something to tell you.
Get an email every time I publish something new.
Your email will only be used to send you what I publish. Privacy policy
Related posts
