After almost three decades of building companies, one of the things I still find hardest to accept is “thinking small.”

I’ve been lucky in life. I ran into mentors who took me by the hand along the road that got me here. Many of them taught me more than the university ever did. These were people who could see the future without breaking a sweat. Sometimes they handed me great lessons, and sometimes a well-earned smack on the head. I owe them a great deal. As Sir Isaac Newton put it: “If I have seen further, it is by standing on the shoulders of giants.” That legendary line applies to my mentors word for word.

Today I want to remember and thank two of them in particular: Augusto López Valencia and Nicanor Restrepo Santamaría — two towering figures of Colombian business. To both of them I want to say, THANK YOU!

From Augusto López I learned never to leave for tomorrow what could be done today. I remember that in some of the board meetings I was privileged to be invited to, I watched tasks get assigned to him — and the astonishing part was that before the meeting was over, he’d already finished them. His execution was flawless. Augusto didn’t manage time; he simply bent it to his will. When you were with him, you just knew you had to get to the point, deliver the data, skip the backstory — if he needed the story, he’d ask for it.

Nicanor taught me to plan, but one of the things that impressed me most about him was his humility. He was like a big kid, like a boy scout — always ready! I had a few chances to spend time with him in his working world, and beyond that I was his diving buddy on countless occasions off the island of Providencia.

One morning, when he’d invited me to his office at the headquarters of Suramericana de Seguros, he said to me in a playful tone: “Come on, let’s sit in the famous boardroom of the Antioquian syndicate.” While we were swapping stories about our underwater adventures, the speaker on his desk crackled to life, and out of the little black box came the voice of his secretary, Anita Duarte (whom I also remember with great fondness — Anita, thank you for being so kind to me). In the middle of our conversation, Anita’s sweet voice said: “Doctor, you have a call on the line…” To which he replied: “Tell them to wait a few minutes.”

We kept talking… and after a while, the speaker buzzed again. It was Anita once more. “Doctor, don’t forget you have a call…” He answered again: “Tell them to wait…” We carried on chatting like a couple of street kids sharing a soda and a loaf of bread — and I say that more about myself than about Nicanor, whose decency and presence were beyond compare.

Finally the phone rang one more time, and this time Anita said: “Doctor… it’s the Minister of…” And he, in a firm voice, answered: “Tell the Minister I’m with a gentleman just as important as he is…”

I was stunned after hearing that. I told him: “Don’t worry, I can wait.” He apologized, picked up the phone, and dispatched the call in a couple of minutes. What Nicanor taught me that morning I didn’t understand until years later: in the attention economy, deciding who you make wait is deciding who you are.

The Sentence I Didn’t Understand

Nicanor and Augusto shaped my life for the better. I carry them deep inside me; I feel a profound affection and a gratitude to life itself for having known them. They were extraordinary teachers to me. But today, when I look back, I think that maybe the biggest thing they taught me was something they repeated every time I brought them a project:

“You’re not thinking big enough… come back when you’ve got something bigger.”

Somewhere between frustration and disbelief, I walked out of their offices many times. I couldn’t grasp what these heavyweights were talking about. Only later did I come to understand what they meant. Neither of them was asking me for more ambition. They were asking me to think on a different operating system.

Brazil, or the Country Where I Learned What Scale Means

Life placed me in Brazil, a marvelous country I love and respect deeply. It was there that I learned what scale really means. Brazilians themselves say that Brazil is home to “as coisas mais grandes do mundo” — the biggest things in the world — and they’re not exaggerating.

In that geography I had the chance to work for one of the largest security companies on earth, and I was blindsided more than once.

I remember once heading out to meet a man in a town barely two hours from São Paulo. My sales agents had told me this fellow ran a fleet of more than five thousand trucks. So I psyched myself up to deliver the pitch of my life — we needed that account to close the quarter. I armed myself with a solid slide deck and my sharpest outfit for the meeting.

When I got to the address, there were no offices, no marble, no receptionists, nothing at all. I arrived at a parking lot the size of a soccer stadium, and in the middle of it stood a prefab shack, out of which came a man in Hawaiian flip-flops — chinelos, as they call them in Brazil. Honestly, I thought he was the lot’s watchman, so I told him I’d come to meet Fausto, and with colossal humility he said:

“Come in and have a seat over there,” pointing at a table.

A little plastic table with cheap plastic chairs. He turned around and said: “Quer um cafezinho?” — Want a little coffee? I nodded. And without another word, he said: “How can I help you? I’m Fausto…”

Not even in a comic-strip gag would I have fallen flatter on my back. Plop!

A few weeks later they sent me to a meeting in Brasília. This time I’d be meeting with the legal counsel to the Minister of Finance, Dr. Wilfrido Augusto Marques. I arrived on a Friday and figured I’d use the weekend in the Brazilian capital to learn a bit more about the place. I reached the Planalto — a city designed on a drawing board by President Juscelino Kubitschek and architect Oscar Niemeyer a few decades ago. Honestly, it’s a city built like it’s made of Legos; the scale of it is staggering.

That weekend, Dr. Wilfrido invited me to spend the day at his farm. Like any hillbilly, I didn’t picture anything extraordinary — we paisas from the Colombian mountains are used to farms, I told myself. But as we drove out there, the proportions started to sink in. The man’s little farm held 90,000 head of cattle, plus kilometers upon kilometers of corn and soy. He had his own airport, warehouse after warehouse, robotic roaming irrigation systems, and an animal-genetics lab that no university in Brazil could match. To top it off, Dr. Wilfrido boasted that the president of China had come to visit him to study his cattle operation.

Now, past all the color of these stories — what did Augusto, Nicanor, Fausto, and Wilfrido have in common?

Every one of them, always, told me the same thing: think big.

But here’s what matters: none of them looked big. That was the first crack in the way I understood the world.

Thinking Big Is Not an Intellectual Problem

What I only came to understand many years later is that thinking big is not an intellectual problem.

Most people believe the limit lies in intelligence, in education, or even in talent. But no — the real limit is in the emotional and cultural capacity to hold an idea that doesn’t yet exist.

Thinking big produces fear and vertigo, and neither of those emotions is rational; they’re biological responses. We humans evolved over tens of thousands of years in small tribes, where standing out too much could be dangerous. In anthropological terms, belonging mattered more than standing out. The group guaranteed food, protection, and survival. Whoever strayed too far from the average could be cast out — and on the savanna, expulsion was a death sentence.

Even though today we live surrounded by satellites, artificial intelligence, and global financial markets, the deep brain still runs on many of those same primitive rules. We’re carrying 200,000-year-old hardware trying to run software from the year 2026.

That’s why, when someone says they want to build something enormous, the natural reaction from those around them is almost never support. What usually shows up is suspicion:

“You’re crazy,” “that’s impossible,” “be realistic,” “start small,” “you’re such a dreamer.”

Society rewards prudence because prudence lowers collective risk. But here’s the interesting part: nearly every great transformation in history was initially considered absurd.

Thinking big means breaking the psychological pact of the average, and that makes people deeply uncomfortable, because it is, technically, an act of tribal disobedience.

The Invisible Cage

As I matured, I came to understand that Augusto and Nicanor weren’t simply talking to me about business. They were talking about mental scales. They were trying to pull me out of an invisible cage — a cage built by years of industrial-era schooling, economic fear, and a culture of survival.

The Latin American education system — and honestly, much of the Western one too — was designed during the Industrial Revolution to train administrators of existing structures, not creators of new realities. We were trained to answer questions, not to think conceptually. We were taught to work by minimizing risk and error, not by expanding the range of the unimaginable. We were taught to study in order to land a good job, rather than to build large-scale systems. School shaped us to solve the problems of the past, and that had enormous consequences for our mental models. Most people don’t fail because they think too big; many fail because they never gave themselves permission to try.

What Brazil Taught Me About Power

I remember perfectly something that hit me hard while living in Brazil. In Colombia — and I say this with respect and affection — we often confuse sophistication with capability. We’re impressed by big offices, elegant buildings, fine ties, and ornate speeches. But Brazil taught me something completely different: real scale often operates in silence.

Fausto had five thousand trucks and ran them from a cheap plastic chair, wearing flip-flops. Wilfrido managed an agro-industrial operation the size of a small country and talked about animal genetics with total ease while you were still trying to wrap your head around the size of his farm. That’s where I understood something fundamental: people who think big usually stop needing to look big.

That completely changes how you understand power. Because real economic power rarely makes noise. It’s too busy executing.

I think there’s another reason people struggle to think big: we grew up surrounded by false representations of success. We were sold the entrepreneur as a spectacle — Instagram with rented jets, podcasts full of canned one-liners, photos with borrowed cars — when the truth is that the great builders often operate from discipline, repetition, and a brutal ability to see scale where others barely see chores.

Whoever has to look rich isn’t rich yet. Whoever has to look powerful isn’t powerful yet. What’s truly scarce is what doesn’t yet need to be announced.

The Emotional Cost of the Long Horizon

Thinking big requires tolerating long horizons, and long horizons collide head-on with modern culture’s addiction to instant gratification.

Today we live in a dopamine economy. Everything is designed to produce immediate reward: social media, real-time metrics, likes, fast consumption, short attention cycles. But building something truly big is profoundly anti-dopaminergic. It demands years of invisibility, it demands planting without applause, it demands holding on to a vision when no one else understands it yet. Sometimes, when you don’t fully understand it yourself anymore.

That’s probably one of the most painful reasons so many people abandon their ideas too soon: they can’t stand the psychological void between the vision and the validation. Because thinking big carries an enormous emotional cost. You start to feel strange. You start to feel out of sync with those around you — sometimes even with your own family. The conversations change, the worries change. While some are worried about surviving the month, others are trying to figure out how to build something that will transform an entire industry within ten years.

It’s not about arrogance. What happens with people who think big is that they live on different timescales.

The Relationship with Time

Over the years I discovered something else fascinating: people who think big develop a different relationship with time. Most people use time to survive, while great entrepreneurs use time to multiply systems. That difference sounds subtle, but it reshapes entire economies — think of Elon; everything he builds is at mega-scale.

When you watch the great entrepreneurs, scientists, architects, or strategists of the world, you find a recurring pattern: they can all endure very long stretches of uncertainty without abandoning the vision. They don’t do it per se, or out of motivation. They do it because of their psychological structure — they have a different way of relating to the future.

There’s an old Japanese principle in The Book of Five Rings by Musashi, the samurai who wrote the definitive manual on strategy — a man I quote often. He says something like this: “the strategist’s gaze is twofold.” There’s sight, which is what’s right in front of you, and there’s perception, which is what’s happening beyond. Then he adds a line that’s haunted me for years: you must see the distant as near, and the near as distant. That is exactly what Augusto and Nicanor were trying to teach me without my knowing it. The urgent problems of the quarter, far away, way off in the distance. The twenty-year vision, here, right here, in my hands, tangible. Most people live it backwards.

Today I understand Augusto and Nicanor better. They weren’t trying to tear down my ideas; they were trying to expand my frame of reference. When someone tells you “you’re not thinking big enough,” what they’re really telling you is something far deeper:

“You’re still measuring your life by the size of your fears, not by the size of the possibilities.”

I’ve discovered that thinking big doesn’t necessarily mean building gigantic companies or becoming a billionaire. Thinking big can also mean building a life outside the mental average imposed by your surroundings. It can mean daring to imagine different futures. It can mean breaking inherited narratives. It can mean giving your imagination permission to operate without apologizing for it.

Because in the end, civilizations advance thanks to people who were capable of holding on to ideas that at first seemed ridiculous.

There was a time when crossing oceans was ridiculous, when flying was ridiculous, when reaching the Moon was ridiculous, when the internet was ridiculous, when building a rocket-launch base in Urabá — a remote corner of Colombia — was ridiculous. Many of the things that will transform the coming decades probably look ridiculous today too.

Maybe that’s why thinking big costs us so much. Because it forces us to see the present as something transitory, and not as a permanent structure. It forces us to accept that the world can reconfigure itself far faster than our emotional culture is prepared to process.

The final trap, the one it took me decades to see

The leap in scale — the real one — is not something the world grants you. It’s not a permission a boss signs off on, nor a blessing handed out by a university, nor a validation that arrives by certified mail.

It’s a permission you sign for yourself, in silence and usually with no witnesses.

Augusto, Nicanor, Fausto, and Wilfrido had different scales, different geographies, different languages. But they shared one single thing: they had given themselves permission first. Before the market, before society, before their families, before even the evidence made them notable.

That, I think, is the most important lesson all those encounters left me:

The size of our life ends up determined by the size of the questions we dare to ask ourselves.

It isn’t determined by our starting circumstances, by our family name, or even by the capital in our pocket. It’s determined by the scale of imagination we’re able to sustain when everything around us is begging us to think smaller.

Nicanor is gone now, but his voice still plays inside me like a speaker that never switches off — like that intercom of Anita Duarte’s interrupting the conversation, reminding me, every time I catch myself thinking small, of a single sentence:

“Come back when you’ve got something bigger.”

Ever since, I’ve learned something simple: next time, I always come back with something bigger.

The Inheritance That Matters

Today, after almost thirty years in this career, I understand something I couldn’t see before: I’m on the other side of the bridge. My mentors gave me scales; my obligation — not commercial, not professional, but anthropological — is to pass them on to those coming up behind me.

Because the inheritances that truly transform civilizations aren’t financial — they’re cultural, like a myth recited across generations through oral transmission. A father can leave his child a company, and the child can run it into the ground within a decade if he didn’t also inherit the operating system that built it. A teacher, on the other hand, may leave his disciple not a single peso, and still hand him the most expensive thing in the world: an expanded frame of reference.

That’s what Augusto and Nicanor did with me; that’s what Fausto and Wilfrido did without knowing it — each from his plastic chair and his country-sized farm.

That’s what I try to do today with every conversation, every text, every company I help build, every young entrepreneur who writes asking for advice. Not to give them money, not to give them contacts, but to give them something far more valuable: A suspicion, a crack, and a useful discomfort.

The suspicion, so they’ll understand their life could run on a different scale. The crack, so the light of a future that isn’t here yet can get in — the weak signals. And the discomfort of knowing that what they’re doing today probably isn’t worthy of what they could be doing.

If there’s one thing I’ve learned in these years, it’s that companies don’t transform because of isolated geniuses — in fact, I’ve made it a rule never to work with divas. Businesses transform when we create chains of mental transfer. Through generations of entrepreneurs who dared to hand the next one a scale bigger than the one they received.

Augusto and Nicanor received scales, expanded them, and passed them on to those of us who, by some divine luck, got to spend time with them. I’m expanding them right now, in this very text, before your eyes, and I’m passing them on to you — so you’ll do the same.

Ask yourself whether you’re thinking big enough, and who are you going to pass the scale you received on to? Who are you going to say, with the same firmness with which it was said to me: “Come back when you’ve got something bigger”?

Which restless young person are you going to give the loving smack on the head they need to break out of the mental cage they don’t yet know they’re locked inside? Which child, which nephew, which employee, which partner are you going to plant the suspicion in — that the average is too small for them?

Because thinking big is not an individual act; it’s an act of generational rebellion. It’s the discipline of dying knowing you left at least five people thinking on a scale different from the one they had before they met you.

Today, thirty years on, I don’t just come back with something bigger — I come back to hand it to someone else. That’s the chain, and it’s the only real way the country will change. It’s through teachers who dare to tell their disciples they’re thinking small, and disciples with the courage to listen, go home uncomfortable, and come back years later with something bigger. That’s the DNA of FUTOPIX.

What will endure is the mental scale you leave traveling in the heads of others, long after your name has been carved into a headstone.

Thank you for reading and sharing!

GabrielBedoya.com

DISCLAIMER

This document is for informational, educational, and futurist-analysis purposes only. It does not constitute financial advice, an investment solicitation, or a recommendation to buy, sell, or participate in any instrument, company, project, or asset.

If anyone uses the content of this material to solicit money, investments, or financial participation, treat it immediately as a possible scam.

Always protect your personal and financial information.

🔮 Keep exploring FUTOPIX

GAIA 2100, From Homo Sapiens to Homo Humanoides

Designing the Future from the Future

Harvesting the Future…

The World’s Business

I was still in short pants when I put together my first PANINI World Cup album. I remember it like it was today: counting the minutes until my mom needed something from the corner store and sent me off to buy it, then asking if I could keep the change (where I’m from we call it la devuelta). Between every kilo of panela — those blocks of raw cane sugar — and every bottle of milk, I’d slowly fill in the little stickers of the album (in some places they call them monas, barajitas, cromos, stickers, and so on; for those of us born in Medellín, they were the “caramelos,” the candies). Now that I think about it, the leftover change never actually got me to a complete album — the things were expensive from day one. That only happened years later, when my buying power changed.

You can’t talk about the PANINI World Cup album without going straight to sensory and olfactory memory: the smell of ink, the glue smeared all over your clothes, because back then there were no self-adhesives. You stuck them down with ordinary paste, that gummy, tacky stuff we used for arts-and-crafts homework at school. The kids from a better neighborhood could afford Colbón, a PVA glue I lost track of and honestly don’t know if it still exists.

The World Cup album was always awaited with a kind of fever, the source of celebrations and of a whole underground neighborhood economy: the swap meets and the recital in front of the “dealers” going, “got it, got it, got it…” — always with a third eye open, because there was never a shortage of the bully who’d show up, slap the stack out of your hands, the famous tumbis, the snatch-and-run, and then, well, cry about it!

Maybe my whole crybaby-entrepreneur streak started right there? Who knows…

The World Cup album was more than a hobby, it was a full-blown passion, a ritual, almost a liturgy. Whoever completed the album felt like a world champion too — he was the Maradona of the block. I remember the corner stores, the parks, the school gates all filling up with “ranfañeros” (an exotic word for the guys who resold the figures), who’d work out of big boxes and make a killing, selling the scarce stickers at bitcoin prices.

Years have gone by and the World Cup album fever came back to me, making me relive the vertigo and the anxiety of chasing down the figures. I’ve had some contact with soccer schools here in the U.S., where I live, and I can tell you the World Cup fever is more alive than ever, that the album tradition has carried on from generation to generation. Just as my dad passed it to me, I see parents and kids alike making pilgrimages so they don’t get stuck without the figures of their idols. Before you assume this is an old-timers’ thing, know this: the frenzy of the PANINI collection is more alive than ever.

The Billion-Dollar Industry Behind the Stickers

But what deals get cooked up inside the Pandora’s box that is the business of collectible figures, stickers, and cards? Let’s open that box and see how, behind a seemingly innocent business, there hides an industry with more teeth than a dogfight.

PANINI was born in 1961 in Modena, Italy, thanks to the initiative of the four Panini brothers, who started selling collectible images of Italian soccer players out of a family newsstand where they distributed newspapers. What began as a local stationery operation turned, over time, into a multinational. In 1970 the PANINI brothers signed a contract that would change their destiny forever. And they signed it with none other than FIFA, who granted them the rights to publish the first official sticker album for the 1970 Mexico World Cup.

Today, Panini S.p.A. is an Italian joint-stock company, headquartered in Modena, owned by Panini Management and Fineldo (the family holding of the Merloni dynasty, tied to Indesit). For 2024, PANINI reported profits of €1.9 billion, more than 1,000 employees, and operations organized into four divisions: Collectibles, Publishing/Comics, Panini Digital, and The Licensing Machine.

Four Ways to Print Money

1. The main engine (stickers + trading cards)

This is the economic heart of PANINI, and it runs on the following logic:

Soccer album stickers: an extremely high-volume, low-unit-cost product with excellent margins. Distributed in newsstands, supermarkets, and convenience stores across more than 150 territories. Its economics rest on “compulsive impulse”: in the U.S. a pack costs two dollars, holds 7 stickers, and completing the album takes, on average, buying 1,400 images. During World Cup peaks, the Modena factory alone produces between 8 and 10 million packs a day.

Licensed trading cards: this is PANINI’s premium product, with a higher unit value and margins that vary by segment (retail vs. hobby premium). In the U.S., Panini America (based in Texas) produces the Prizm, Optic, Select, Mosaic, National Treasures, Immaculate, and Flawless brands for the NBA and the NFL.

2. Comics and the quiet diversification

This is the least-discussed division, but strategically important. PANINI produces roughly 8,000 titles a year across comics, manga, graphic novels, and children’s books, aimed mainly at European and Latin American markets. In Italy, PANINI is the official publisher of Marvel comics and of Mickey Mouse (Disney). This gives it recurring income that doesn’t depend on the sports-events cycle, smoothing out the volatility baked into a business built around a World Cup every four years.

3. Panini Digital, the hybrid bet

This is the new battlefield PANINI has moved into, with apps like Panini Sticker Album (where users can keep a digital album running parallel to the physical one), digital trading games, and stats-capture software. Through Panini Digital, the company uses voice-activated software to capture soccer statistics, which it then sells to agents, teams, media outlets, and video-game makers. The app built for the Russia World Cup reached more than 1.5 million users.

4. The Licensing Machine, the intellectual property

Panini LM buys and resells licenses for characters and intellectual property. It acts as an agent for individuals and newspapers seeking rights to comics and celebrities. It’s a very-high-margin business, but low-volume.

Manufactured Scarcity

The CEO of Panini America, Mark Warsop, puts it this way: “manufacturing scarcity means striking a delicate balancing act between producing limited numbers of high-value cards while keeping the perceived value in every pack, so that the mom buying stickers at Walmart and the collector spending $500 at a hobby store both feel they’re getting the best for what they paid.”

That duality is the key to the whole business. A $2 pack sold to a kid at a corner store in Hialeah and a $1,500 Hobby Box sold to an adult collector in Dallas share the same production machine, the same licensing network, and the same design team. The model arbitrages between two completely different psychologies: the child’s game of trading and the adult’s investment in scarcity, using the same infrastructure for both.

The World Cup Factor

The World Cup album is PANINI’s most iconic product, and it runs on a logic of once-every-four-years peaks:

The last World Cup, in 2022, generated more than €2 billion in profits.

PANINI locked in an exclusive deal with FIFA that extends their historic partnership through 2030, giving them exclusive sticker and card rights for the 2026 World Cup, the 2027 Women’s World Cup, and the 2030 World Cup.

PANINI officially launched the 2026 World Cup album on April 28, 2026, in a ceremony at Wembley Stadium, presenting the largest collection in the Italian company’s history: 980 stickers covering 48 national teams across 112 pages.

The Operational MOAT

PANINI owns a physical distribution network across 150+ territories worldwide. Through it, the company can put its products directly into customers’ hands. That matters enormously in markets where Amazon and the big retailers aren’t the main channel — above all in Latin America, Southeast Asia, parts of Africa, and Eastern Europe. The company produces out of Modena for nearly the whole world, except in the U.S. and Latin America, where it has two factories: one in Texas and the other in Brazil.

The Disruptors

Jean-Marie Dru, founder of TBWA, used to say that the problem with the modern economy is that, somewhere in the world, in some garage, there’s a nerd with a computer trying to disrupt your business model — and that applies to PANINI to the letter.

Last week, those of us who belong to the PANINI religion went cold when we heard the president of FIFA, Gianni Infantino, say at a conference that starting in 2030 PANINI will no longer make the World Cup album, and that from that year on, our album as we know it will disappear and be handed to the multinational FANATICS. If the news landed on us collectors of nostalgia like a bucket of ice water, imagine how things must be inside PANINI.

This news isn’t new, even though it was only just announced at Infantino’s conference. The idea that PANINI was on the ropes had already been simmering deep inside FIFA. On top of that, there’s a years-long legal war between PANINI and Fanatics. Seen this way, the intrigue is running on three fronts at once: the loss of PANINI’s American licenses, the legal litigation, and the possible sale of the company.

The Lost Licenses

PANINI built its dominance of the North American market in 2009 with a brilliant move. Taking advantage of the fact that the NBA had canceled its contracts with Topps and Upper Deck — and even though both companies had expressed interest in continuing to print basketball cards — PANINI saw that both rivals were undercapitalized, seized the opening, and came to the negotiating table with cash in hand, which ultimately left those two competitors out of the game. In under three years, PANINI became the second-largest producer of sports cards in the world.

There’s a brutal strategic lesson here: more often than not, the big opportunities show up when the giants are going through liquidity problems. PANINI didn’t win on vision alone; it won because it arrived at the right moment with the capacity to execute and capital on hand. In fiercely competitive industries, timing can matter more than the product itself.

In January 2022, Fanatics bought Topps and proceeded to offer the leagues a far bolder move to get PANINI out of the way. This time it didn’t just put more money on the table — it also offered them equity in the business, leaving PANINI practically shut out of the United States market. In short, it paid PANINI back in the same coin PANINI had used in 2009.

The fascinating thing about this play is that the battle stopped being a simple licensing negotiation and became a war of vertical integration. Fanatics understood that making products wasn’t enough; you had to control the entire ecosystem: licenses, athletes, distribution, digital platforms, breakers, events, and user experience — and that changed the board completely.

This is the timeline of Panini’s fall:

August 2021: Fanatics announces exclusive deals with MLB/MLBPA (starting in 2026), NBA/NBPA, and NFLPA for the future.

October 2025: Fanatics/Topps officially takes control of the NBA licenses.

April 2026: Topps, owned by Fanatics, becomes the exclusive licensee for NFL cards.

After April 2026, Fanatics will hold exclusive licenses for the NBA, NFL, MLB, Premier League, F1, and WWE.

For Panini America, this means its NBA and NFL product lines — which carried much of its growth for 15 years — will now vanish.

And this is where the real problem surfaces: when a company leans too hard on outside licenses, it never really controls its own destiny. You can have the best design, the best manufacturing, and the best distribution, but if another player shows up with more capital and better financial incentives, the whole castle can start crumbling in a matter of months. This is a lesson I’ve lived in my own skin, which is why, whenever I’m about to invest in any new business, I always ask myself: on which side is the handle of the pan?

The Legal War

PANINI hasn’t rolled over quietly. In August 2023, PANINI filed a lawsuit against Fanatics. The core of the legal argument is that Fanatics conspired with the leagues to create an openly illegal monopoly.

The judge on the case stated in her report that “there was a conspiracy among the NFLPA, the MLBPA, and Fanatics to license the intellectual property of both players’ associations exclusively.”

She likewise held that PANINI had “adequately alleged the facts” supporting its claim that Fanatics enjoys “monopoly power” because it can set prices and shut out competitors as it pleases.

This judge’s ruling will let PANINI pursue antitrust liability, including a possible injunction “against implementation of the long-term exclusive agreements” to produce and market trading cards. But in no scenario will it get the licenses back.

The fight for control of the sticker market seems to run much deeper than we imagined. It looks more like a geopolitical battle than a market dispute.

It’s said that Fanatics allegedly bought the print shop PANINI used to make its products and, with it, slowed PANINI’s production down.

PANINI accuses Fanatics of threatening to blacklist its employees so they could never work in the industry again, unless they quit immediately and joined Fanatics.

The legal battle also details how Fanatics allegedly leveraged its monopoly power to coerce distributors, retailers, and local card shops into accepting higher prices and restrictive terms, while raiding PANINI’s staff and pressuring star athletes to sign exclusive autograph deals.

Fanatics also filed a countersuit alleging that PANINI used abusive employment practices to keep its ex-employees from joining Fanatics. The company denied the claims and fired back, accusing its competitor of launching a “prolonged, unlawful, and deceptive campaign of unfair business practices, pressure tactics, and unfair competition,” in an attempt to force Fanatics to pay a giant sum so that PANINI would terminate its licenses early in 2022.

From here on, this FUTOPIX stops being about stickers. Because what we’re really watching is a modern war for control of cultural ecosystems. Licenses, platforms, manufacturing, talent, athletes, distribution channels, and intellectual property are all being absorbed into a logic of brutal consolidation where the winner isn’t trying to compete: it’s trying to centralize power and seize total control of an industry with practically no competitors left.

Even though PANINI could win a great deal of money in damages, the pursuit of a remedy — when monetary damages aren’t enough — likely won’t be granted, according to lawyers who specialize in this kind of business.

In other words: Panini will probably get a check, but again, it won’t get the licenses back.

What’s Next for PANINI

This is the most recent twist, and maybe the most decisive. PANINI is exploring strategic options, including a possible sale. Citi Bank has been picked as financial advisor to Panini Group to evaluate the process. PANINI’s strategic review follows close on the heels of the death of CEO Aldo Hugo Sallustro in April 2024, who led PANINI for more than 30 years while overseeing the company’s expansion into the United States.

Speculation points to global investors like Advent, Charterhouse, and CVC as potential buyers. And here’s where the truly important subtext appears.

The death of the longtime CEO automatically set off the succession conversation. Sallustro had sustained the company’s Italo-Argentine culture for three decades. His death leaves a vacuum of strategic vision at the worst possible moment: loss of American licenses, competitive pressure, and structural transformation of the industry.

The most natural buyer would be Fanatics, which would fully consolidate the sports-collectibles sector under a single roof: Topps + Panini. That would mark the formal close of the historic duopoly that dominated the industry for decades.

But the timing doesn’t look accidental. The 2026 World Cup will probably be an extraordinary year for PANINI. The company could once again top €2 billion, driven by the event alone. That maximizes the asset’s valuation, brutally, ahead of any possible acquisition. The sale rumors landing right before the World Cup aren’t a coincidence — they’re the peak of the company’s narrative and financial value. Meanwhile the fight for control of the market and the talent in this industry will grind on.

Fanatics has hired several PANINI staffers, and PANINI has tried to block the exodus. This matters far more than it seems, because the manufacturing, design, and card secondary market know-how isn’t trivial. PANINI built two decades of expertise in the U.S.: designers, parallels specialists, autograph procurement experts, and athlete relationships.

If Fanatics manages to extract that talent, it doesn’t just win the licenses — it also wins the operational capacity to execute them. Without that talent, the licenses would be just paper, and there lies one of the great secrets of the creative industries: companies don’t compete only for customers or contracts; they compete for accumulated knowledge.

The Last Sticker

Maybe this story was never really about the figures. It was never only about printed stickers, foil packs, or soccer. Deep down, it was always talking about us. About the kids who ran to the newsstand with sweaty coins in their pockets, about the endless afternoons swapping stickers on the sidewalks, about the anxiety of tearing open a pack hoping the hard one would finally show up, and about that small collective ritual that managed to connect entire generations long before social media existed. Because while today’s tech world talks obsessively about engagement, communities, and immersive experiences, PANINI had been doing exactly that for decades out of an Italian stationery shop. Maybe that’s one of the most beautiful ironies in this whole story: one of the most successful social platforms on the planet never needed the internet to exist. It ran in parks, schools, corner stores, recess yards, and dining tables — it ran wherever we humans interacted.

That’s why PANINI’s real asset was never the licenses, nor the printing machines, nor even the premium trading cards worth hundreds or thousands of dollars. The real asset was something much harder to build and, above all, much harder to buy: the collective emotional memory of several generations. That explains why the Fanatics-PANINI story leaves so many collectors with a strange feeling. Because although, financially, the market logic looks obvious, culturally it feels as if someone were trying to buy a piece of childhood. But childhoods don’t work like licenses. Licenses get signed, renegotiated, sold, and lost. Cultural rituals survive far longer; nobody can acquire, through a bank transfer, what took decades to build inside the emotional memory of millions of people.

Fanatics can end up with contracts, athletes, platforms, and entire markets, but there’s something it probably will never fully be able to buy: the feeling of walking into a corner store and finding, for the first time, the World Cup album sitting on the counter. That moment belongs to another place, another speed, another kind of economy. An economy where there was still room for wonder, where time seemed slower, and where a kid could spend whole weeks thinking about a single hard-to-get sticker. Because back then the figures weren’t competing against thousands of simultaneous stimuli; they were the very center of the conversation. The album traveled tucked under your arm like a badge of identity, and every completed page felt like a small personal victory. Completing a World Cup was also a way of growing up.

Maybe that’s why this possible ending feels so symbolic. Because deep down, the story of PANINI is also the story of a much larger generational transition: the shift from a capitalism built on physical rituals and collective memory toward one dominated by platforms, consolidation, and algorithmic control of consumption. Before, we collected in order to trade; today many collect in order to speculate. Before, the figures passed through the hands of children; today some end up encased in vaults as financial assets. Before, the album was an excuse to get together; today it often ends up turned into content for social media. And yet, against all logic, I’ve witnessed that the ritual is still alive. Kids still open packs, parents still help stick down the stickers, schools still fill up with improvised swap sessions, and millions of people still feel that little hit of dopamine when a hard-to-get figure finally appears.

That should tell us something enormous about human nature. Because in an ever more digital, automated, and synthetic world, we still need physical objects that connect us emotionally with others. We still need symbols, rituals, and shared stories. And there, probably, is the great strategic — and also deeply human — lesson of this whole story: competitive advantages based solely on exclusive contracts are far more fragile than they look, while advantages built on collective memory, nostalgia, generational rituals, and shared culture can survive even when the companies, the owners, and the rules of the market change.

Because in the end, the figures were never just figures. They were tiny time capsules. Small emotional archives of who we once were. Maybe someday, when our children or grandchildren find one of those forgotten albums inside an old box, they’ll open it slowly again, smell the aged paper, and instantly understand something no digital platform will ever fully replicate:

There was a time when the whole world fit inside the World Cup album — honestly, what an extraordinary era we got to live through!

Thanks for reading and sharing!

GabrielBedoya.com

DISCLAIMER

This document is for informational, educational, and futurist-analysis purposes only. It does not constitute financial advice, an investment solicitation, or a recommendation to buy, sell, or participate in any instrument, company, project, or asset.

If anyone uses the content of this material to solicit money, investments, or economic participation, treat it immediately as a possible scam.

Always protect your personal and financial information.

🔮 Keep exploring FUTOPIX

The War of Symbols: How Brands, Memes, and Politicians Hack Your Mind

Digital Antonomasias

Beyond Political Empathy

On a night in early January of 1610, an aging mathematics professor in the city of Padua — a man whose academic passions were mostly a way to dodge his enormous debts, the demands of his three children born out of wedlock, and a job that bored him to the marrow — sat in the dark corners of his study. There he hunched over a tube of leather and brass and pointed it at the sky. Honestly, you couldn’t call the thing a telescope; it was more of a jury-rigged, home-made astronomy kit, and with it the old professor probed his own curiosity and the edges of the night sky.

That night, to his deep surprise, he found three points of light lined up beside Jupiter. In his field journal he jotted them down as “new stars.” Four nights later he scanned the sky again, and this time there were four — but the truly astonishing part was that they moved, which left him chewing on why they’d behave so strangely, so unlike other heavenly bodies. Only by January 13th, after a great deal of head-scratching, did he grasp that they weren’t stars at all. They were celestial bodies orbiting something that was not the Earth. The discovery blew his mind: the Earth was not the center of anything! It’s just another rock in space, spinning around something else...

Galileo did not discover the moons of Jupiter; he simply saw them move. That sighting would change his life and his fate forever, for better and for worse. The professor’s real discovery came only afterward, when he set about convincing everyone else that what his “telescope” was showing up in the heavens was truer than what Aristotle had written down many centuries before him. It took the Church one hundred and twenty years to accept what a fifteen-ducat tube had already proven — one hundred and twenty years of inertia and threats, hundreds of generations raised on the idea of a universe that did not exist.

I’m writing these notes in May of 2026, after two much-needed weeks off, to tell you that a North American company — from the state of Iowa, no less — signed a contract with the Department of Energy to extract lunar dust, to be paid at twenty-five million dollars a kilogram. All of this is happening in a world where there are already machines that write better than almost every copywriter on your LinkedIn feed, where agentic artificial intelligence just turned the technical barrier to founding a company into a weekend errand, where supply chains are being re-nationalized in real time — and where we, the ones who claim to “design the future,” keep raising our kids for an Earth that is the center of nothing, arguing, with ridiculous seriousness, over whether to move to Lisbon, whether to pick up a second passport, or whether whining about the swamp of regulations makes us entrepreneurs or just crybabies.

That’s the question of this FUTOPIX. I don’t know which technologies are coming — that barely matters anymore; technology is like taxes, it always shows up. After the recent spectacle of the NASA | Artemis mission, the question is: under which of the four moons are we raising — maybe without ever deciding to — the generation that will replace us?

Galileo christened his four moons with an embarrassingly courtly name: the “Medicean Stars,” in honor of his patrons. But the worst of it was that the Church silenced him; he had to swallow his own discovery on pain of being fried alive. Later, other astronomers — Kepler, Marius — ended up rechristening them with the names we know today: Io, Europa, Ganymede, Callisto. Justice would have been served if at least one of them had been named Galileo. Four figures from Greek mythology, four lovers whom Jupiter seduced, abducted, or transformed.

Let’s sit with this for a second. The four moons that split Western cosmology in two are, mythologically, four victims of a god who wanted them for himself. This isn’t a thing to trivialize; it’s the exact question that Helium-3 is putting to us. Each of the four moons I’m about to describe would love to be brought down to Earth, like any lovesick troubadour’s serenade.

The Most Expensive Dust in the Solar System

Three liters of lunar dust — that’s the quantity the U.S. Department of Energy (DOE) bought in 2025, to be delivered before 2029 by an Iowa startup called Interlune, which already has a prototype lunar excavator capable of processing a hundred tons of regolith per hour. Don’t get confused: these aren’t tons of ore, they’re liters of an isotope known as Helium-3, trapped in the lunar dust and in a few corners of Earth, deposited there over four and a half billion years of solar wind hammering the Moon’s atmosphere-free surface.

So why such a market price — twenty-five million dollars a kilo (a liter)? It’s no joke; it’s real, hard cash on the barrelhead. Helium-3 is the most expensive commodity in the solar system that has already had a first invoice cut for it — it already has confirmed customers, signed contracts, and order lines. Space mining stopped being science fiction the moment it stopped being discussed in sci-fi settings and started being discussed in the federal offices of the American government.

But here’s where the real action starts for entrepreneurs — and where the anthropological questions come in, which in the end are the only ones I care about: why are there five economies today looking at exactly the same dust, why did they simultaneously decide it was worth going to fetch, and why did they build five different architectures to do it? We anthropologists always love to start our analysis by reading the context. What an object gathers around itself — technology, capital, mythology, law, ambition — says more about the historical moment than the object itself. What Helium-3 is gathering is a fork in the modern economic spectrum, the kind that sometimes slips by unnoticed but happens only a handful of times per millennium. Let’s look at what I mean:

The United States treats Helium-3 as a commodity. Interlune + Vermeer + DOE. Purchase-order logic. A market.

China and Russia treat it as a geo-strategic position. The International Lunar Research Station, running parallel to the Artemis Accords. Supply-chain-control logic.

Japan treats it as a problem of technical elegance. Magna Petra + ispace: a 2026 mission that doesn’t excavate but captures the isotope by disturbing the surface. People getting creative.

The European Union treats it as a legal artifact. Moon Agreement 2.0 — you have to sign a treaty before you fly a mission. Logic of legal mediation, regulation, very European.

India treats it as a non-aligned option. Chandrayaan-3 already set hardware down at the lunar south pole. It plays its own destiny without signing on with any bloc.

There’s a sixth customer almost no one names, and it’s the one that will decide the real demand curve: quantum computing. The dilution refrigerators that cool qubits to millikelvin need mixtures of Helium-3/Helium-4. If nuclear fusion never arrives, quantum will — and it will be quantum, not energy, that drains the reserve. Helium-3 isn’t just the possible fuel of the next energy era. It’s also the cold required for the next co-evolution of intelligence.

More or less: five economies, six distinct demands, one single Moon. This is exactly what Galileo saw — more than one gravitational center in the system. Economies, as I said in the last edition and repeat here because it’s the thesis of FUTOPIX, don’t fail for lack of resources; they fail from misreading the signals. Reading the signals starts with accepting that every civilization is interpreting them, and the four moons are the four different futures in which one of these readings will hit the jackpot: Helium-3.

The Signals

Before we move into the four moons, let’s look at what’s already orbiting the competitive field of lunar exploration. These are verifiable facts as of April 2026.

Interlune + Vermeer already have their hundred-tons-per-hour lunar excavators ready. The DOE bought three liters for delivery before 2029 — a real customer, not a lead, not a prospect.

Magna Petra + ispace launch a no-excavation isotope-capture architecture in 2026. Japanese elegance against American muscle: a different philosophy, competing for the same dust.

ILRS (China + Russia) are consolidating an architecture parallel to Artemis. Two legal systems on the same Moon — it’s what the South China Sea was in the 2010s, scaled up to space.

The EU is pushing Moon Agreement 2.0. We already know these folks want to regulate before they extract. It’s the civilization that feels more at home writing the rulebook than playing the game. Because to play, you have to pay — the famous “ticket to play,” which in other parts of the world we call el CVY, or “¿Cómo Voy Yo?” — “What’s in it for me?”

India: Chandrayaan-3 already touched down on the lunar south pole in 2023. It has proven hardware and the capacity to play without signing on with anyone.

Quantum: if Google, IBM, or a Chinese player cracks qubit error correction at scale, demand for Helium-3 will overflow.

As always, the signals are unmistakable. What isn’t unmistakable is what they mean for ordinary mortals. In FUTOPIX TAKE 4 I called this movement the “cultural twist“: every technological trend is mediated by the society that absorbs it. Here the impact will be brutal — five economies, five ways of seeing the world, five bets on the future. The dust will stay the same; it’s the economy looking at it that decides which model of moon we’re going to inhabit.

The Dust That Will Reconfigure the Space Economy

There’s something deeply misleading about thinking of space exploration as an extension of geography. The truth is it isn’t. We’re not “going” to the Moon the way you cross an ocean. We’re rewriting the conditions under which a civilization can exist, and in that process, Helium-3 shows up not as one more resource but as a piece of infrastructure for this whole ontology — a material that won’t just feed machines, but will redefine what a society can imagine as possible.

Because if Buckminster Fuller taught us anything, it’s that when you change the energy base of a system, you don’t improve the civilization — you replace it.

For millennia, humanity has been trapped in an energy regime based on combustion: burn to survive. Burn wood, then coal, then oil. Every energy leap expanded our capabilities, but it also defined our limits. Politics, war, the economy, even the architecture of our cities, are direct consequences of how we access energy. In that sense, Helium-3 isn’t valuable because it’s expensive, but because it promises a completely disruptive break — clean energy, no combustion, no waste, no significant thermal friction. Energy that doesn’t destroy the environment that contains it.

But that’s where the shallow readings fall short. The real challenge isn’t the energy; it’s what happens afterward. When energy stops being the bottleneck — for quantum computing, among other projects — other systems will emerge as the constraint. That’s where the second layer of Helium-3 appears, quieter, more strategic: its central role in the architecture of extreme cold. In a world where quantum computing is starting to walk out of the lab, as Michio Kaku anticipated, the problem is no longer just to calculate, but to sustain impossible physical conditions. Temperatures near absolute zero, quantum stability, state coherence. Helium-3 stops being fuel and becomes a condition of possibility for a new co-evolution of intelligence.

This is where space exploration reveals its true nature. Not as a race for territory, but for the infrastructures that will support new forms of production.

The Moon, then, will stop being a romantic destination and become the logistical node of a larger system. A place where the absence of an atmosphere — once a problem — turns into an advantage. Where the solar wind, which for billions of years deposited Helium-3 in the regolith, becomes, in retrospect, a process of strategic accumulation. As if the solar system had been quietly stockpiling this inventory with no one to claim it.

Here comes the anthropological dimension underlying all of this: each economy isn’t competing for Helium-3, it’s projecting onto it its own way of understanding the world.

The United States turns it into a market because its founding mythology is one of exchange. China turns it into territory because its narrative is one of systemic control. Europe turns it into a treaty because its history is one of mediation. Japan turns it into a technical problem because its culture prizes precision. India turns it into an option because its identity is non-alignment.

What we’re watching is not a space race. It’s a cultural framework amplified by technology. In that mirror, Helium-3 works like a prism, because it forces us to answer an uncomfortable question: what kind of civilization do we want to be when scarcity stops being the organizing principle?

This is where a lot of models fall short, because they assume that energy abundance automatically produces well-being. But as Alvin Toffler warned, the problem has never been the availability of resources, but the speed at which societies can adapt to new conditions.

If tomorrow we solve energy, and we don’t solve humanity’s needs, we’ll be exposed.

Space exploration, then, is not a project of territorial expansion the way many want to frame it. It’s an experiment in production, one that forces us to confront our own cultural structures in an environment where the rules change faster than our institutions.

Helium-3 becomes the new modern gold rush — not because it’s the fuel of a new economy, but because it’s the future catalyst of technological co-evolution.

When Galileo saw the moons of Jupiter, he didn’t just discover objects; he discovered that the center was a relative thing, that what looks fixed is only a limited perspective on the universe.

Helium-3 is doing the same. It’s showing us that the Earth is no longer the operational center of our civilization. That the critical decisions — energy, computing, technological sovereignty — are beginning to shift off our planet. For the first time in history, we’re designing systems that don’t depend on terrestrial conditions to exist, and that’s the next frontier.

This will change everything. It will change how we educate ourselves, because we’re no longer forming individuals for a planet, but for a multi-planetary system. It will change how we do politics, because borders stop being geographic and become orbital. It changes how we understand work, because the link between effort and survival dissolves once energy stops being scarce. Above all, it will change how we understand the future — because the future is not a linear projection of the present, but a space of radically open options.

In that context, Helium-3 is not only the target of the new space race. It’s the excuse we needed to launch it. That excuse is what’s forcing us to build the capabilities we need to step outside our historical frame, to stop thinking like a terrestrial civilization and start operating like a spacefaring one.

Like every transition of this kind, it won’t be uniform or peaceful. There will be friction, conflicts, asymmetries. Some of the “moons” will be more attractive than others; some will win, others will vanish.

But what’s no longer up for debate is the direction of the movement. Helium-3 is, perhaps, the first tangible sign that the transition has already begun.

Don’t Ask Anyone to Bring You the Moon — Someone Might Actually Do It

For centuries, the sky was a limit. A place to observe, to project myths, to look for answers we couldn’t touch. Today, that same sky is turning into a layer of infrastructure we couldn’t even imagine before. Space exploration no longer answers to curiosity; it answers to the need to redesign our civilization from its foundations. Helium-3 emerges in this context not as a distant promise, but as a concrete sign that we’re entering a new phase: one where energy, intelligence, and territory stop being confined to Earth.

What’s truly transformative isn’t the possibility of extracting resources from the Moon, but the capacity to reorganize the logic under which we operate as a species. When energy stops being scarce, the rules of the game change: value will no longer lie in possessing, but in interpreting; no longer in competing for finite resources, but in building systems that amplify possibilities. Helium-3 is not just fuel, it’s context. A context that forces governments, companies, and entire cultures to rethink their place in a system that no longer has a single center.

Because we’re not facing a race for the Moon, we’re facing a test of how well we read the future. Some will see an economic opportunity in that dust, others a geopolitical advantage, others a technical problem.

Only a few will understand that what’s really at stake is the way we choose to co-evolve. Helium-3 won’t change the world on its own; it will change those who know how to read it.

DISCLAIMER

This document is intended strictly for informational, educational, and futurist-analysis purposes. It does not constitute financial advice, an investment solicitation, or a recommendation to buy, sell, or participate in any instrument, company, project, or asset.

If anyone uses the content of this material to solicit money, investments, or economic participation, treat it immediately as a possible scam.

Always protect your personal and financial information.

Gabrielbedoya.com

🔮 Keep exploring FUTOPIX

The Return to Fundamental Thinking

Order in the Heavens

Anthropophysics

Life Always Gives You a Rematch…

There are moments in an entrepreneur’s life that feel as if the universe, after years of refusing you a favor, finally decided to pay it back with interest. The strange thing is that these moments don’t arrive with rockets, fanfare, or cymbals. No headlines, no trumpets. They show up quietly, on some ordinary day, when you’re sitting in front of a screen and suddenly realize that something that took you decades to solve, you can now do alone, in a single afternoon, with a cup of coffee beside you. The most beautiful part is that you don’t solve it halfway — you solve it better, faster, and with an autonomy that used to be unthinkable.

That was the day I understood that Claude Code wasn’t just another tool. It was a biographical turning point — especially for those of us who came up with the bare minimum in coding: a little dBase, some Visual Basic, that kind of ultra-basic stuff.

To understand why this moment hit me so hard, I need to take you back twenty-six years, to a time when we dreamed big with almost no resources, and when the gap between what we wanted to build and what we could actually build was measured, literally, in the number of programmers you could find, pay, and hold on to.

1999: Ideas Avanzadas, or the Art of Trying to Run on One Leg

In 1999, when the world was barely shaking off its Y2K hangover and most people still looked at the internet as if it were a mirage, three long-haired dreamers in Colombia decided to found Ideas Avanzadas, the first digital-survey company in Latin America. A lot of people have asked me why I have a boxer’s nose. The answer: so many companies slammed the door in my face — insisting that this “online surveys” thing was useless — that they ended up flattening my nose, though never my spirit. At Ideas Avanzadas we were pioneers of digital surveys, though at the time we didn’t even have the perspective to know it; we were simply doing something nobody around us was doing. Other people invented the word “pioneer” later, once we all understood that the trail we cut had turned into a highway.

Soon after, we made another leap: we moved the whole business over to mobile surveys, using some extraordinary devices called HP Companion PDAs, or Palm Pilots. Getting them to work was a royal pain in the… back then there was no internet on mobile devices — we were only just easing into the trend — but we made them work anyway. For younger readers who never saw one: picture a smartphone, but dumb… before phones were ever smart, an ink-gray screen and a stylus that looked straight out of a spy novel were the height of cool. With those gadgets we hit the streets to capture public opinion, consumer behavior, voting intention, and so on. We were digitizing a process that had been anchored to paper, pencil, and manual transcription for decades. I still remember the survey-world orthodox crowd trashing our initiative like a farmer railing against the arrival of the tractor.

In 2010 we upped the ante and founded the first MVNO in Colombia (Mobile Virtual Network Operator, for the uninitiated — a mobile carrier that owns no network of its own but rides on someone else’s). There, too, we fought the developers to make them understand that user experience mattered as much as code architecture, and that two links and three buttons weren’t enough to deliver it. Then came more projects, some more aggressive than others, among them GoDigg, a cloud platform to democratize bitcoin mining, and ALT, an app to democratize alternative investments.

Laid out like that, in a single paragraph, it looks like an elegant trajectory. The truth is that every one of those projects was trench warfare against the same seven-headed beast: dependence on developers.

The Wound: The Dev Who Understands Code but Not Business

Here comes an uncomfortable confession that many startup founders will recognize in silence: in twenty-five years of building digital products, my greatest pain was never the market, the competition, or the capital. My greatest pain was always the developers.

Not developers as people, let me be clear, because I’ve worked with brilliant minds. The pain was structural, almost cultural. Finding someone who could program wasn’t particularly hard; the market was full of competent programmers. The scarcity — the real scarcity — was finding someone who could program and understand the business. Someone who could look at a user flow and see, at the same time, the technical architecture and the monetization model. Someone who understood that behind every requirement there’s a commercial hypothesis, that behind every feature there’s an opportunity cost, and that behind every line of code there’s a cultural conversation with a user who doesn’t exist yet.

That profile, that unicorn, didn’t exist in sufficient numbers. When one appeared, they were wildly expensive, fleeting, and usually gone to Silicon Valley before you could get them to sign a yearly contract. It happened to me over and over: I’d find brilliant kids, train them, teach them everything I knew, and just when I thought we were ready to fly, other companies would show up with juicier checks (good for the kids), but those were salaries I, as an entrepreneur, simply couldn’t match. I won’t dwell on it, because in other settings I’ve already been called a crybaby for it.

So founders like me became professional preachers. We spent hours, days, weeks explaining the business to the dev, explaining to the dev why the investor was pushing, explaining to the investor that the user was the heart of the business, explaining to everyone what it meant to create value. We were translation nodes in a network where nobody spoke the whole language. The cost of that translation — in time, money, mental fatigue, and lost opportunities — is impossible to measure, but I promise you one thing: it was the highest tax I paid in my entire career as an entrepreneur.

If there’s one thing I’ve learned as an anthropologist observing the world of entrepreneurship, it’s that every organization is built around its main point of friction. If your friction point is capital, you build a fundraising machine. If it’s technical talent, you build a hiring machine. If it’s execution, you build an operations machine. In my case, for a quarter of a century, I built gigantic machines to mitigate the friction between the idea and the code. Today, from one day to the next, that friction isn’t shrinking — it’s COLLAPSING!

If I’d Had Claude Code Twenty Years Ago, I’d Probably Be a Billionaire Today

I’ll say it plainly, even if it sounds arrogant: if I’d had Claude Code twenty years ago, I’d probably be a very rich man today. Not because my ideas were better than anyone else’s. Not because I had a superior read on the market. But because the bottleneck that strangled my execution for a quarter of a century would have vanished. Every MVP that waited six months for a dev to free up would have shipped in two weeks or less. Every iteration that stalled because the senior programmer left for another company — or because it was a holiday, Easter week, New Year’s, Columbus Day, Secretary’s Day, whatever — would have kept moving without friction. Every pivot we didn’t make because “we’d already invested too much in this architecture” would have happened at the speed of a conversation.

The cost wasn’t only economic — it was stress and existential dread. How many ideas died on the whiteboard or in the Excel sheet, how many projects stayed stuck at the wireframe or the mockup, how many pivots never happened because technical debt and human dependence made every change cost too much. Startups don’t die from a lack of ideas. They die from the distance between the idea and its execution. For twenty-five years, that distance, for me, was called developers. Today, that distance has shrunk to zero.

What really changed is an unprecedented cultural rupture — not just a productivity gain, as so many like to pretend.

It’s from this reflection that I want to ask you to climb one more step with me. What’s happening with Claude Code is not a productivity increase. That language is too small, too corporate, too lukewarm to describe what’s actually going on. A productivity increase is when Excel replaced the calculator. Claude Code is something else — it’s a full paradigm shift in the division of labor.

Let’s go back to fundamentals, as we so often invite you to do at FUTOPIX. What is programming, in its most minimal form? Programming is translating human intention into instructions a machine can execute. For roughly seventy years, that translation required a specialized intermediary — the programmer — who mastered a cryptic syntax and had become the gatekeeper between whoever had the idea and the idea built.

That figure, that bottleneck, was born around 1950 with the first programming languages and consolidated into a professional caste across the entire second half of the 20th century. When Bill Gates and Paul Allen founded Microsoft, when Jobs and Wozniak founded Apple, when Zuckerberg founded Facebook, they did it because they themselves were part of that caste. Those of us who couldn’t code depended on those who could. That dependence organized the entire digital economy.

What Claude Code is doing — and this has to be said with the clarity of a scalpel — is dissolving the programmer caste’s monopoly over the idea-to-code translation. It’s not just replacing it; it’s dissolving it, diluting it, turning it into an optional matter.

Let’s think about this with historical perspective. When Gutenberg’s printing press appeared in 1450, it didn’t make the scribe’s work faster — it ended scribework as a monopoly on the production of knowledge. When the sewing machine appeared in the 19th century, it didn’t improve the seamstress’s productivity — it transformed the relationship between the individual and the making of their own clothing. When the automobile was democratized, it didn’t make coachmen more efficient — it replaced them as an economic category.

Claude Code is to programming what the printing press was to scribework. It isn’t a tool that helps the programmer; it’s a technology that reorders the entire relationship between the one who dreams up a product and the product built.

The Samurai Metaphor: The Sword and the Companion

In my personal library I keep, with special fondness, a book I reread every so often: The Book of Five Rings by Miyamoto Musashi, Japan’s most legendary samurai, written in 1645 when he was sixty years old and had won more than sixty duels without losing a single one. In that book, Musashi teaches a technique he named Ni Ten Ichi Ryu: one school, two swords.

The idea is radical. While every samurai of his era fought with a long sword held in both hands, Musashi wielded a long sword in one hand and a short sword — called the companion — in the other. It wasn’t for show. It was a strategic philosophy. With two swords, one man is worth ten. With two swords, you don’t die with a weapon still sheathed.

Let me say it directly: Claude Code is the modern entrepreneur’s companion sword. You’re still the long sword — the one that sets the direction, that carries the intention, that holds the sense of the business, that knows the customer, that understands the market, that has lived the sleepless nights wondering whether the revenue model holds up. That remains your main weapon, and nobody can replace it for you. But now you have a second sword, a companion sword, that moves with superhuman agility on the very terrain where you used to freeze: the translation of your vision into executable code.

Musashi writes something that today resonates with almost unsettling precision: “From one thing, know ten thousand things. When you attain the Way of Strategy, there will not be one thing you cannot see.” With the two swords, the solo founder sees what used to require a team of twenty. The power asymmetry that once favored huge corporations with infinite budgets is evening out. The individual with vision and Claude Code has, today, more execution capacity than many companies that were billing a hundred million dollars ten years ago.

You to You Squared

Another book that has kept me company for years is The Quantum Leap Strategy by Price Pritchett. Its thesis is simple and brutal: quantum leaps don’t happen by working harder within the same system; they happen when you change the system. It’s not more of the same, faster — it’s a radically different way of operating.

Pritchett puts it this way: you to you squared. It’s not that you improve by 20%, 30%, 50%. It’s that you multiply your capacity by an exponential factor because the new system removes a constraint you used to take as inevitable.

This — exactly this — is what Claude Code offers the contemporary entrepreneur. It doesn’t make you program faster. It makes you program things you couldn’t program at all before. It pulls you out of the category of “entrepreneur who depends on developers” and moves you into the category of “entrepreneur sovereign over their own stack.”

When Pritchett says “act as if you had complete faith; go for the quantum leap with everything you’ve got,” you could read it as cheap motivational self-help. Today, I read it as a technical description of what’s possible. If someone had told me five years ago that I, at my age, with no team of developers, could design, prototype, and launch functional applications working alone with an AI as my companion, I’d have said they were on drugs. Today I do it on Saturday mornings, between one cup of coffee and the next.

The Most Solid Source of Independence

Let me allow myself a sentence I know will sound excessive, but which I write with the conviction of someone who has spent twenty-five years in this industry: Claude Code is the most powerful source of individual independence that has appeared in my professional life.

When I say independence, I don’t mean it in a vague or romantic sense. I mean something very concrete, almost geometric: Independence from other people’s schedules. I no longer have to wait for a developer to “free up” to move my idea forward. My idea moves forward when I have time to think it.

Independence from startup capital. Many of my earlier projects died because the cost of building the MVP was prohibitive before I could validate the hypothesis. Today the cost of an MVP is measured in days of personal work, not in investment rounds.

Independence from the geography of talent. I no longer need to be in Medellín, Bogotá, Mexico City, Miami, or San Francisco to have access to a world-class technical team. My technical team fits inside my laptop.

Independence from internal politics. No need to negotiate with the CTO, no need to fight with the lead developer, no need to convince the team that the feature is a priority. If you decide it’s a priority, it’s a priority.

Independence from the rhythm of the venture-capital cycle. I don’t have to raise a round to validate an idea. I validate it myself, alone, with my companion sword.

The deepest of them all: independence from mediation. My idea no longer has to pass through another person’s interpretive filter. I think, I speak, and the code appears. The gap between signifier and signified — the same gap I’ve described in earlier FUTOPIX issues as the very heart of anthropology — closes in the direct experience of the founder-builder.

This is the kind of independence that historically existed only for the artisans who built their own objects by hand in the workshop at home. What Claude Code offers is the digital version of that craftsmanship, but with industrial capacity. It’s the return of the maker, the builder, the tinkerer, raised to the tenth power.

The Rebirth of the Founder-Builder

I want to propose a cultural thesis, because FUTOPIX wouldn’t be FUTOPIX if we didn’t look at the phenomenon through an anthropologist’s eyes.

For seventy years, the digital economy was organized around the separation between the one who thinks up the product and the one who builds it. The product manager dreamed, the developer executed. The founder sold the vision, the CTO made it real. The designer drew the screen, the engineer implemented it. This division of labor worked, but it also created enormous distortions: loss of fidelity between intention and execution, cultural friction between teams, cumulative delays, and a hierarchical dependence where real power ended up residing with whoever controlled the code.

What we’re living through now, with Claude Code and tools of the same family, is the return of the founder-builder — that figure who existed in the early days of personal computing, when Steve Wozniak soldered his own boards and Bill Gates wrote BASIC at night. That figure disappeared as projects grew too big and complex for one person. Now it’s coming back, but with a superpower Wozniak didn’t have: an AI as a constant companion that amplifies technical capacity without diluting business vision.

The founder-builder of 2026 is not the lone programmer of the ‘80s. It’s a new hybrid: someone who thinks about the business, converses with the AI, validates with the user, and closes the loop without ever leaving their own head. The feedback cycles that used to take weeks or months now take hours. The speed of learning shoots up. And the speed of learning — let’s never forget it — is the only truly sustainable competitive advantage in an exponential world.

What This Means for Those Who Read Me

If you’re reading this FUTOPIX and you’re an entrepreneur, an executive, a student, a curious mind, a dissident from corporate tedium, or any other member of this tribe of wide-awake dreamers, I want to leave you three ideas to carry with you:

First: if you have an idea you’ve been carrying for years because you couldn’t figure out how to build it, this is the moment. The door has just opened. The technical barriers that paralyzed you for a decade are no longer there. What used to require landing a co-founder CTO now requires learning to converse strategically with an AI. That can be learned. It isn’t trivial, but it can be learned.

Second: your advantage is no longer the code — it’s the question. In a world where anyone can produce code with AI, the difference will be made by whoever knows how to frame the right questions, whoever has the sharpest business instinct, whoever understands the customer most deeply, whoever has aesthetic and ethical judgment about what’s worth building. The human fundamentals — empathy, judgment, vision, taste — are more valuable than ever, precisely because technical execution is becoming a commodity.

Third: the window won’t stay open forever. When every entrepreneur in the world has access to these tools, the competitive advantage will have been absorbed by the market. Whoever adopts them now, whoever masters them in the next twenty-four months, will operate with a brutal time asymmetry over those who adopt them in 2028 or 2029. In moments of technological transition, the first twenty-four months are worth the next ten years. Ask the people who bought domains in 1995, who joined Facebook in 2008, who mined Bitcoin in 2011.

A Final Confession

I’m entering the maturity of life. I’ve built, closed, and sold companies. I’ve made money and lost it. I’ve hired dozens of developers and suffered the departure of just as many. I’ve lived through every stage of the entrepreneurial cycle, from the euphoria of launch to the silence of failure. And yet, never in my professional life have I felt as free to build as I feel today.

It’s not nostalgia for the nineties. It’s not teenage excitement over a new technology. It’s the calm certainty that, for the first time in almost thirty years, the distance between my imagination and my execution depends almost entirely on my own will.

This is what Claude Code means to me. I don’t see it as a tool, nor as an assistant. I see it the way you see a traveling companion who gives you back something you thought you’d lost forever: the ability to build alone, at my own pace, by my own rules, without asking permission, without waiting for anyone.

The long sword is still mine. The companion sword has just arrived — and with both in hand, as Musashi taught, one man is worth ten thousand.

If this FUTOPIX stirred something in you — a sleeping idea, an old rebellion, a project shut away in a drawer — I invite you to take it out and put it on the table. The technology you were waiting for to build it has already arrived.

The rest is up to you.

It’s time to reclaim the sovereignty to build.

It’s time to think big without begging for execution.

It’s time for the founder-builder of the 21st century.

DISCLAIMER

This document is for informational, educational, and futurist-analysis purposes only. It does not constitute financial advice, an investment solicitation, or a recommendation to buy, sell, or participate in any instrument, company, project, or asset.

If anyone uses the content of this material to solicit money, investments, or economic participation, treat it immediately as a possible scam.

Always protect your personal and financial information.

Gabrielbedoya.com

🔮 Keep exploring FUTOPIX

The Dynamo Paradox

From Dr. Google to Dr. GPT

Anthropology and Personal AI Agents

From the Piggy Bank to Your First Contract With Time

In anthropology we know all too well that some objects aren’t objects at all — they’re time portals. Cultural interfaces that connect us to an earlier version of ourselves, to cosmogonies and to passages of history that hand us back a whole set of imaginaries. For me, one of those portals is the old metal coin bank from the bygone Caja Social — a little metal chest that looked like a tiny portable vault, absurdly heavy for a child, with that dry, almost ceremonial clink every time a coin dropped inside. And the interesting part is that the “chink” wasn’t just metal hitting metal. It was a promise, a primitive way of understanding the future.

I’m certain many of you reading this know exactly what I mean. When I was just a kid, my mother gave me my first piggy bank, a steel coin bank where I stashed the “change” I got for running errands for her and my aunts and uncles. At home, every one of my siblings had one. It wasn’t optional — it was part of the family ritual, the way our parents drilled into us early on an idea that seemed unquestionable at the time: you had to save for the future.

That was our first contract with time. So I went from the little chest to the red house, and from there to the ceramic piggy. Coin after coin, I built up my savings — though I have to confess the discipline was never constant; plenty of times I dodged saving to treat myself to a few indulgences. In the end, what mattered was building the habit, because from the coin bank we leapt to the first bank account, and from there to the term deposit (yes, I had one of those too). The savings passbook and the term deposit weren’t just the financial instruments of the era — they were the symbol of discipline, foresight, and preparation for the uncertain. The mantra was clear: you had to work, earn money, put it away, and wait for something to come along to spend it on.

But as the years went by, that map stopped being enough — and eventually became almost irrelevant.

When Saving Stops Being Enough

A couple of days ago, a good friend asked me a seemingly simple question: how do we teach our kids to save? As tends to happen at FUTOPIX, the question didn’t stay on the surface. It threw me straight back to that metallic past, to the dry sound of coins dropping into a bank — but it also forced me to confront something deeper: the suspicion that the very ritual we inherited no longer does the job we think it does. My answer caught him off guard, because it wasn’t the one he expected.

I told him that today it’s no longer important to teach our kids to save. Not because saving was a mistake, but because it was the right answer to a system that no longer exists. It was a tool designed for an environment where money was relatively stable, where time worked in favor of whoever hoarded it, and where discipline was enough to preserve value. The world stopped running on those rules.

What our kids really need isn’t to learn how to store money, but to understand how it behaves. They need to learn to position it, to read its flows, to spot where it expands and where it erodes. They need to learn to invest — not as some financial sophistication, but as a basic capacity for inhabiting the economic system that is already emerging.

This is where the real generational tension shows up. While many parents still think about teaching their kids to protect value, the system has already evolved into one where value only survives if it moves. We’re moving from a world where money was a static object, toward a world where money is a programmable system. It’s in that transition where everything gets more complicated, because the map we inherited stops matching the territory our kids are facing.

Saving, as we’ve traditionally understood it, rested on an implicit premise: that money could hold up over time without significantly deteriorating. Today that premise no longer holds. Inflation acts as a silent, constant, almost imperceptible erosion that wears down the value of money sitting still. But beyond inflation, there’s an even deeper dynamic: capital that moves, that participates, that plugs into growth systems is being rewarded out of all proportion compared to capital that stays static.

In other words, money stopped being a deposit and became a flow. When something is a flow, you can’t understand it through the logic of passive accumulation. You have to understand it through the logic of circulation, of speed, of strategic positioning. Saving meant containing, stopping, preserving. Investing means releasing, exposing, letting money interact with systems that grow it under certain conditions.

That’s where the discomfort begins for a lot of people. Because teaching kids to save was simple — it was a moral narrative: discipline, patience, sacrifice. Teaching them to invest, on the other hand, means accepting uncertainty, talking about risk, admitting that not everything is controllable. It means stepping onto terrain where even we are still learning. But dodging that conversation doesn’t protect our kids — it just leaves them without tools to operate in the new economic system.

Because they aren’t going to live in a world where money waits. They’re going to live in a world where money moves, gets programmed, gets optimized, gets redistributed in real time. A world where stillness isn’t prudence, it’s loss; where staying on the sidelines isn’t neutral, it’s a handicap.

Here comes the idea that’s hardest to accept but that we most need to internalize: saving, as we’ve historically understood it, has stopped being a value-preservation strategy and has become a slow form of losing it. Saving is the easiest way to make yourself poor.

This isn’t a tweak. It isn’t an incremental recommendation. It isn’t that we should save a little less and invest a little more. We’re facing a paradigm substitution.

We’ve gone from teaching kids to resist time to teaching them to amplify it; from teaching them to hoard to teaching them to move within complex systems — and that transition isn’t optional. It’s the difference between taking part in the economy that’s coming… or staying trapped in the one that already ceased to exist.

From Object to Intelligent System

Money has never been static, but what we’re seeing today isn’t just one more incremental co-evolution — it’s a change at the root. Money has stopped being a store of value and is becoming an intelligent, programmable, ontological system. This isn’t a cosmetic change; it’s a change in its very nature.

Before, money was something you stored, transferred, or spent. Today, money is starting to behave, to execute instructions, to interact with other systems without necessarily a person making each decision.

To grasp the scale of this shift, think about something simple. When you deposited money in a bank account, that money sat there, waiting for you to decide what to do with it. It was passive. It depended entirely on your action. In the new environment, by contrast, money can be programmed to act under certain conditions. This is where smart contracts come in. They’re nothing more than coded agreements that execute automatically when something happens. It’s money that stops waiting and starts operating.

Imagine, for example, that you have a rental agreement. In the traditional world, the tenant has to remember to pay, the landlord has to verify the payment, and if something breaks down, a chain of friction begins: calls, delays, conflicts. In a system based on smart contracts, the money is released automatically on the agreed day, provided the conditions are met. If they aren’t, the system acts accordingly. No interpretation, no intermediary. The money executes the agreement all by itself.

Another example even closer to home: many of us had relatives who gave us company shares — certificates we hoarded as if they were Morgan’s treasure. For that to work in the traditional model, someone had to monitor the market, make decisions, buy or sell those securities. In the new model, that portfolio can be managed by automated rules or even by AI, which reallocates capital based on predefined market conditions. If an asset falls below a certain threshold, the system reacts. If another climbs, the system reassigns. The money isn’t just invested — it’s thinking in operational terms.

This is where a gap opens up that we can’t ignore. If we, as parents, are still trying to understand what it means for money to behave this way, how do we prepare our kids to interact with this new kind of system? Because they aren’t going to walk into a world where money is static. They’re going to walk into an environment where money is dynamic, reactive, and — in many cases — autonomous.

Decentralized finance (DeFi) will accelerate this shift even further, because it strips out intermediaries, borders, and business hours. Money no longer needs to be locked inside a banking institution that opens and closes. It can flow continuously, available 24/7, with no geographic or bureaucratic friction. You can move value anywhere in the world in minutes, without asking permission, without waiting for external validations. Money stops being “locked up” and starts behaving like a constant flow.

And in parallel, the tokenization of assets is breaking another historic barrier: access. Before, investing in real estate, art, or certain financial instruments required large amounts of capital. Today, those assets can be fractioned into small digital units, letting more people take part. You can own a fraction of a building, of a work of art, or of a diversified portfolio without needing to be a major investor. Ownership stops being absolute and becomes distributed.

But this democratization comes at a cost: complexity. Because in this new system, having money isn’t enough. You have to understand how it moves, under what rules it operates, what risks it carries. Whoever doesn’t understand these systems simply doesn’t take part in them. And not taking part is no longer neutral. It’s staying outside of where value is being created.

That’s why, more than teaching our kids what money is, we need to teach them how it behaves. Because the money they’re going to inherit won’t be kept in a coin bank. It will be programmed, moved, and will make decisions inside systems that wait for no one.

The Collapse of the Model We Inherited

We grew up under a seemingly stable, almost unquestionable logic: study, work, earn, save, retire. That model didn’t just organize our economic life — it organized our relationship with time, with effort, and with the very idea of the future. It was a cultural contract that offered clarity and an implicit promise: if you did the right thing for long enough, the system would eventually reward you with stability. And yet that contract is being eroded by forces that don’t operate in a linear way but an exponential one — forces that ask no permission and announce no arrival; they simply reconfigure the environment we live in.

AI is perhaps the most obvious manifestation of this transformation. It’s driving down the value of many forms of work, especially those based on repetition or on processing structured information. What once took years of training and experience can now be done in seconds by trained systems. This doesn’t mean human work disappears, but it does mean a deep redefinition of its value. Effort on its own stops being a guarantee of relevance, and knowledge, when it’s replicable, stops being scarce. What really starts to matter is the ability to understand where that effort fits within broader systems that are constantly evolving.

At the same time, and in parallel, capital assets keep expanding at a rate higher than labor income. As Thomas Piketty pointed out, capital grows faster than income — but what truly matters isn’t the phrase itself, it’s the structural implication it carries. The system starts to reward ownership over effort, and that shift introduces a fracture in the model we inherited. If labor loses relative capacity to accumulate and capital gains capacity to expand, then the equation changes at the root. It’s no longer enough to take part in the system through work; you have to understand how to position yourself within it, how to interact with its dynamics, and how to anticipate its moves.

In that new scenario, income starts to become unstable, employment stops being a guarantee and turns into just one more variable inside an uncertain environment. Linear trajectories break, cycles shorten, and predictability — one of the pillars of the old model — begins to fade. It’s at this point that an uncomfortable but unavoidable truth emerges: money grows where it circulates, not where time is invested. This isn’t meant to devalue work, but to place it in its new position within the system. Work is still a means of generation, but the expansion of value happens on another layer, in another logic, at another rhythm.

This is precisely where saving, as we understood it, begins to lose its original meaning. Saving meant stopping money, preserving it, protecting it from the environment. It was a coherent strategy in a system where time didn’t significantly erode accumulated value. But in an environment where inflation acts as constant erosion and where dynamic capital is what earns the biggest rewards, stopping money stops being prudent and starts being costly. Saving, in its traditional form, stops being a preservation strategy and becomes an illusion of security — a feeling of control that doesn’t necessarily translate into real protection within today’s system.

If we widen the lens from an anthropological perspective, the pattern becomes even clearer. Every society has historically trained its new generations in the skills needed to survive within its environment. At one point it was hunting, then farming, later trade. Each transition redefined what it meant to be prepared. Today we’re facing a new transition, one where the critical skill is no longer solely producing value with the body or with time, but understanding how value moves within complex systems. In this context, saying that investing is the new hunting stops being an aesthetic metaphor and becomes a functional description of reality.

Investing determines access to resources, stability, and freedom. It’s the contemporary way of positioning yourself within the economy. Not teaching this skill doesn’t protect our kids from risk; on the contrary, it exposes them to a system they won’t understand, with no tools to navigate it. That said, reducing investing to a technical dimension would be a mistake. Investing isn’t only about knowing financial instruments or platforms — it’s a discipline that demands a deep understanding of yourself. It means managing emotions in volatile environments, holding decisions in the middle of uncertainty, and developing judgment when information is abundant but clarity is scarce. Risk stops being an enemy to avoid and becomes information to be interpreted, read, contextualized.

Within this same fabric another layer appears that’s rarely addressed with the seriousness it deserves: attention. We live in an economy where human time is captured, analyzed, and monetized with millimetric precision. Every digital interaction, every second in front of a screen, every seemingly trivial gesture is being converted into value for someone else. In this context, attention becomes an economic asset, and whoever doesn’t learn to recognize it ends up handing it over without realizing. The question stops being how much time we spend, and becomes who is capitalizing on that time. Your attention is already an asset — the difference is whether you know how to capture it, or whether someone else does it for you.

All of this leads us to a conclusion that goes beyond the financial. The change we’re witnessing isn’t technical, it’s cultural. It’s the move from an employee mindset to an owner mindset. It’s leaving behind thinking in terms of linear income to start thinking in terms of capital allocation, strategic positioning, systemic understanding. We need to raise individuals who don’t just take part in the system but understand it — who are capable of reading weak signals, of anticipating moves, and of making informed decisions in complex environments. Individuals who see money not as an end in itself, but as a dynamic system of relationships that’s constantly evolving.

This means teaching concepts like value, compound growth, and volatility from early on, but it also demands something much harder: teaching kids to hold a long-term vision in an environment engineered for instant gratification. Teaching them to wait when everything invites them to react, to build when everything incentivizes consumption, to think when everything pushes them to execute without reflection. It’s at this point that emotional intelligence becomes as important as financial intelligence, because no strategy, however sophisticated, survives if the person running it can’t sustain it over time. Without emotional control, no strategy survives.

The Map We Choose to Hand Down

The final question isn’t financial, it’s cultural — because it forces us to look beyond money and ask ourselves honestly what kind of humans we’re raising in a world where money stopped being static and became an intelligent, autonomous, constantly evolving system. It’s not a comfortable question, because deep down it exposes us as the hinge generation, that in-between point that received a map that worked but now carries the responsibility of handing it down updated for a territory that no longer responds to the same rules.

The problem isn’t in the past, nor in what we were taught, because that model was right in its time. The problem is that the environment changed faster than the narratives that explained it, and we keep passing on those narratives as if they were still enough. It’s in that lag where everything is decided, because when we hand down an outdated map, we don’t just confuse — we also limit. This isn’t a minor error; it’s a decision that shapes how our kids are going to interpret the world.

If we keep teaching the old paradigm (you have to save), we aren’t being prudent — we’re being blind to change. We’re preparing our kids to navigate a territory that no longer exists, a world where money could sit still, where linear effort built stability, and where time rewarded whoever simply knew how to wait. That world didn’t vanish all at once; it dissolved slowly, and in that process many never noticed.

But if we decide to update the map, then what we hand down stops being merely a financial tool and becomes something far more powerful. We give them the ability to read systems, to understand invisible dynamics, to recognize patterns before they become obvious. We give them the possibility of acting with intention, not from reaction, and of building their own relationship with money — not from fear, but from understanding. Because deep down, this was never just about money — it’s about preparing them to be free.

It’s about gaining degrees of autonomy in a system that tends to capture whoever doesn’t understand it, about being able to make decisions without being fully hostage to a single source of income, about being able to experiment, fail, and learn at stages where the cost is still manageable. It’s about breaking, once and for all, with the invisible inheritance of financial stress that marked entire generations.

That’s why it’s important to understand that investing early isn’t a privilege — it’s a form of literacy. It’s learning to read the language of the system our kids are already living in, even though many adults still don’t fully grasp it. It’s teaching them that money isn’t stored, it’s positioned; that it doesn’t accumulate by inertia, but moves with intention inside systems that amplify it.

But something even deeper happens when we teach this. We aren’t just teaching a skill — we’re activating a way of thinking. We’re changing the question that guides their lives. They’re going from asking themselves what job they’ll land, to asking themselves how they’ll build value, how they’ll allocate it, and how they’ll grow it inside systems that aren’t linear. That shift, subtle as it seems, completely redefines their relationship with the future.

A way of understanding that money is a dynamic system, that attention is an asset, that time isn’t something that passes but something that amplifies depending on how you use it, and that decisions, when they align with the right systems, can scale far beyond what individual effort would ever allow.

We need to explain to our kids that the future doesn’t wait, doesn’t pause for us to be ready, or for us to fully understand its rules. It advances, it reorganizes, it rewards those who take part and leaves behind those who stay static. In the middle of that constant movement, our kids aren’t watching from outside — they’re already inside the system.

They’re already being shaped by it, already interacting with its dynamics, already handing over their time, their attention, and eventually their capital, even if they don’t call it that.

The only question that really matters is whether they’ll arrive in that world with tools… or with obsolete instructions. That decision — despite all the technological complexity, despite the speed of change, despite the apparent inevitability of the system — is still ours.

DISCLAIMER

This document is intended exclusively for informational, educational, and futurist-analysis purposes. It does not constitute financial advice, an investment solicitation, or a recommendation to buy, sell, or take part in any instrument, company, project, or asset.

If anyone uses the content of this material to solicit money, investments, or economic participation, treat it immediately as a possible scam.

Always protect your personal and financial information.

🔮 Keep exploring FUTOPIX

El Factor Fricción

Los Negocios Aburridos

La Economía del No Tiempo

Founders Cry Too…

Not long ago I got to sit on a podcast with a group of fellow founders, swapping war stories about life as entrepreneurs — and digging into why some of us on the panel decided to leave Colombia to look for horizons where our ideas could actually echo.

Before we wade into all that, let me be clear: this take is not a grievance list against Colombia, the country we love and the nation that gave us birth. Quite the opposite — what I’m putting on the table today is a reflection that invites us to find better paths for building new companies.

As a futurist, it matters that you understand the circumstances of each system. Colombia is a country with an immeasurable capacity for producing founders. I’ve always said it and I’ll stand by it here: in Colombia, entrepreneurs sprout up from under the rocks. The problem is the system — which, and this isn’t just because we currently have a socialist/communist government, has always been designed to serve the economy of the past and was never built for the opportunities the future offers.

Now, if the current continuity candidate wins the election, it will be the end of any form of entrepreneurship in Colombia. Or can anyone name me the great brands born out of more than 60 years of socialism in Cuba or North Korea, or the unicorns coming out of economies like Venezuela and Nicaragua?

For a lot of hot-headed folks, it’s easy to talk about crybaby founders from the comfort of speculation, with zero data to back up the argument. So I’ll build this piece on real evidence and drop the sources at the end, in case anyone’s curious and wants to go deeper.

Enough turning the screw — let’s get straight into what brings us here today.

My life as a founder has dragged me through every moment of truth in the brutal job of building companies. I can say without a shred of doubt that I’ve been an entrepreneur for as long as I can remember.

My first memories go way back, when I was barely a kid. My father had bought some gardening tools to keep up the tiny patch of yard at the house where we grew up, in the La América neighborhood of Medellín. Those tools slept in a little storage nook under the stairs that climbed to the second floor of our house… And when I say they slept, I mean it literally: I almost never saw them used.

One day a gardener knocked on the door offering his services. My mother hired him, and soon it became routine. Every two or three weeks the man came to cut the little grass we had out front. Back then I figured it made no sense to keep tools locked away if nobody used them — and that’s how I decided to set up shop against “Don Pedro.”

So I started offering to mow the lawns and yards of the neighbors on the block. I even put together a little crew of buddies, going house to house “buzzing the grass.” The reaction from the stuck-up crowd didn’t take long: the folks who thought they were a class above quickly nicknamed us “the street urchins,” the girls stopped saying hello to us, and plenty of parents forbade their kids from mixing with the riffraff.

That’s where my rebellion against the system began. The truth is I couldn’t care less that they didn’t greet me — all I wanted was to make money.

When I ran out of yards to mow on the edge of the neighborhood, I jumped from gardening into recycling glass and newspaper. I walked the whole neighborhood knocking on the doors of neighbors and acquaintances, asking them to hand over their old bottles and papers — and honestly, business was booming. I piled up mountains of the stuff. My mom nearly threw me out of the house over the stacks of sacks I had piling up in the living room. I was a recycling tycoon.

At the tender age of 15 I made the leap to my first official storefront: a snack bar inside a well-known sports arena in the city, where I sold sodas and snacks to the fans who came to watch their athletes on weekends. I lasted three years there — until they shut me down for being a minor and a “risk” to the sports organization. At least that was the excuse for handing my business over to some other character.

From selling sodas, my entrepreneurial curiosity took another turn. During my university days I opened a carpentry shop in my house’s garage, inspired by a friend (bless his soul) who trained me in the art of wood framing. That’s where one of my great passions was born: furniture design. But the framing days were so long and exhausting that they crashed straight into my studies, so I had to close the workshop and shelve that adventure for a while.

During my days as a framer-carpenter I met a Panamanian businessman who imported all-terrain bikes from China. This kind of bike was just starting to trend (weak signals), which struck me as interesting. So I sank all my savings into bringing those bikes in from Panama, and I sold hundreds of them in just a couple of years. But the dream of becoming a big bike importer got cut short when a well-known businessman from Antioquia, with stores all over the country, went straight to my supplier to buy bikes and demanded market exclusivity. That day, my adventure in that industry was over.

As a university student I joined the underwater activities club at the Universidad Nacional in Medellín to practice another of my great passions: diving. Once again I blew what little savings I had left on the adventure. I got so hooked that, guided by a great friend and mentor, I traveled to the United States to certify as a dive instructor at a famous institute down in South Florida. I went from feeling like Cochise Rodríguez to a homegrown Jacques Cousteau.

I landed in Miami for the first time at the ripe old age of 25, with a suitcase packed full of dreams. And honestly, the United States blew my mind: this country opened the doors of the world to me. I did everything legally to survive and earn my dive-instructor license. In that market I felt that the barriers I’d once hit in Colombia simply didn’t exist here. Back home, building a company was like racing cars in NASCAR; but in this country I had the whole track to myself. I never got a “no” or a “can’t be done” from the businesspeople who gave me the chance to work with them — on the contrary, every time I spotted a business opportunity, the answer was “go for it!” I spent a couple of years there, mostly dedicated to studying.

Once I got my instructor certification, I decided to head back to Colombia with the dream of opening my first dive school. And so it happened — against all odds, with no capital, no godfathers, and a whole lot of guts — Acqualand was born, the first dive school in the El Poblado neighborhood of Medellín. But as usual, the critics showed up right on cue. The old guard of Medellín diving said they’d made me an instructor in six months, that I had no experience; they blocked my access to the pools, wouldn’t fill my tanks — in short, so many hoops to jump through that today they’re just anecdotes. Those were years of hard work and also of prosperity. Diving gave me everything: money, great friends, and even the family I have today. I became a “quasi-surfer” with rich-guy airs; as a dive instructor I met Colombia’s most powerful people, on all sides of the fence. But over time, after so much abundance, I felt something was missing. I didn’t want to spend my golden years teaching diving, so I decided to fill that void with more academia.

I sold everything I owned — car, gear — and even ended a marriage that wasn’t making me happy, and I enrolled at the Universidad de Antioquia to study Anthropology. The Social Sciences knocked me flat on my belly and opened up a world of new possibilities. Along those lines, and together with two great friends (former dive students), I founded my first tech company, way back in the dawn of the so-called dot-com bubble: Ideas Avanzadas. Inspired by the analytical models of anthropology, we built grite.com, the first online survey service, in 1999 — when SurveyMonkey wasn’t even a glimmer in anyone’s eye.

We made hundreds of pitches to investors, but no incubator believed in us, no fund cared. Nobody would bet a peso on the project, because running online surveys in 1999 went against the status quo. We were 10 years ahead of the industry — at the time we were the best example of what weak signals really mean.

In 2001 I requested a meeting with the CEO of AOL/DMS (America Online — Digital Marketing Services) and flew to Dallas to make my first investor pitch abroad. I traveled with my current wife, the bedrock and emotional sponsor of my business adventures. I have to confess that before walking into the room I was scared out of my mind, a bit poisoned by all the rejection the idea had racked up in Colombia. But the surprise couldn’t have been bigger: I found an audience that listened with infinite attention and respect. Sure, they asked the tough questions — but always in a warm tone. That day I walked out of the AOL boardroom with an LOI (letter of intent) to acquire 40% of Ideas Avanzadas — IA and use it as the spearhead to expand DMS across LATAM. The celebration was short-lived, because that same week the Twin Towers attacks happened and the deal went to hell for a while.

After battling for months, we managed to salvage the initiative, but the operation to acquire a piece of IA ended up moving to Bogotá, since AOL had bought a well-known media outlet in the capital. To this day, in the year 2026, we’re still waiting for our dear Colombian partners to pay us our stake in the deal.

IA was my first serious setback. I was left with nothing — as we say in Medellín, we were left riding on the rims. Shortly after such a wipeout, a security company offered me a job in Brazil, and of course I took it. I saw the chance to work a couple of years in that country of wonderful culture and nature — until the entrepreneurship bug bit me again.

I split off from that company and opened up a franchise for a North American industrial degreaser company in São Paulo, alongside three local partners — huge mistake. If building a company in Colombia is hard, in Brazil it’s practically impossible. I spent two years fighting against corruption, bureaucracy, and institutions that simply don’t function. There I understood that Brazil is really two countries: the country of the businesspeople and the country of the politicians. Two polar opposites — and Colombia, sadly, isn’t far off from the same comparison.

Once again I was left riding on the rims, and I had to scrape together frequent-flyer miles just to get back home…

Self-esteem in pieces but with the heart of a founder intact, I pressed on. In 2009 I took the leap again, riding the boom in “minute selling” (yes — the business of renting out a phone to make calls).

I noticed that the practice was getting more and more popular (a weak signal in crescendo), and I remembered that back in São Paulo I’d met the CEO of a South African company that specialized in formalizing that exact business. I made some calls, set up meetings, and flew to Cape Town to meet with representatives of the owners — a company belonging to the De Beers family, one of the biggest players in the world’s diamond business. I came back to Colombia with the first license to operate community mobile telephony (truth is, they gave me a license for five countries). Psitek Andina was born — or Red Minuto, as it would later be known in the market. On paper, it was the perfect business.

But having a beautiful business on paper is one thing, and operating it is another. When it came time to launch operations, things turned ugly fast. The Colombian State’s bureaucratic hurdles for the business were brutal. The red tape was endless: you need a license for a cable, you have to certify a device, you have to pull a permit for an antenna, you have to pay a stamp fee, the seal has to come recommended, the lawyer has to review the commas, and on and on.

Red Minuto was the country’s first MVNO, and to push the idea forward I met with ministers, senators, business leaders, and even the lady who served the coffee in those institutions. But the answer everyone was really fishing for, in unison, was: “and what’s in it for me?” — the famous WIIFM.

One of the challenges we had to solve was presenting an MVP — Minimum Viable Product, so we set up a small Data Center in downtown Medellín, in an office my in-laws lent me across from the Camino Real Shopping Center. I went into debt down to the last available cent on my credit cards, on top of the loans from a few stubborn souls who trusted me. I knocked on every door of the country’s cellphone operators. But nobody seemed interested; many executives at those companies said my proposal would kill calling cards (which, by the way, were already dead by then). But through sheer insisting, resisting, and persisting, a Spanish executive at Movistar they called Chemas understood the signals of the business and believed in it from day one. Thank you, Chemas, wherever you are!

So we started running tests. We installed stations in Medellín and Bogotá. The technology worked perfectly. We had a market opportunity worth 35 million dollars in the first year alone. But for every step forward, we took two steps back. Until we hit the impassable roadblock: interconnection prices and the near-monopolistic market dominance of Claro. The rates we had with that company forced us to work at a virtual loss, and only Claro could fix it. After hundreds of hours of endless lobbying, they gave us the classic line: “don’t call us, we’ll call you.” We waited a year and a half for the call that never came. Red Minuto had to shut down.

Once again, with our tails between our legs, we liquidated what little was left to pay off the debts.

Not a dime in my pocket, and the only things I had left were my education and my experience as a founder.

During a trip to Cape Town back in 2009, I took the chance to go cage diving with great white sharks, a common activity down there. August is winter at those latitudes, and only a handful of crazies would think to dive in that cold — so on the boat there were just two lunatics: me and a Japanese guy who didn’t mutter a single word on the way out. We reached the dive site, geared up, and got in the water, and before long two enormous great whites showed up, each the size of a bus. It was a fascinating experience.

Coming out of the water, the once-mute Japanese man transformed into a chatterbox. He started talking nonstop the whole ride back, like a man who’d just been mugged. Between laughs he told me he was the CEO of Microsoft Research. I thought: “If the sharks had eaten him, it would’ve been on every news channel. Me, on the other hand — nobody would’ve even missed me.”

I did the same and told him I was an anthropologist, that I worked in technology, and that I was in South Africa on a business trip. When we got to port we went for food and a few beers, striking up a friendship that still lasts to this day. Between one beer and the next, he told me: “if you ever feel like working for Microsoft, get in touch — I can throw some projects your way.”

So, after the failure of Red Minuto, I called my friend Kentaro. And that’s how Xcouter was born, the company that has put food on my table ever since.

But why drag all this narration into the presentation? The goal is one and only one: to show you there’s no such thing as the crybaby founder, the victim, the way some people like to paint it. When you play the victim, you tend to pin the blame for your failures on everyone else — and if there’s one thing I’m clear on after decades of building companies, it’s that all my defeats were my own responsibility. And despite everything I suffered, I never felt like a loser; I always turned what I learned into working capital. Now, that it’s easier to build a business in some places than in others is a plain fist-to-the-table truth that can’t be denied. Looking for the best conditions to develop your creativity and enterprise isn’t a matter of being a crybaby or not — it’s a matter of survival.

What the Data Says

Colombia is hard for founders not because it lacks entrepreneurial spirit, but because the country makes the jump from starting a business to scaling it unusually complex.

The GEM reports show that in 2023 nearly one in four adults in Colombia was starting or running a new business, but only one in 30 had a consolidated company. GEM points out that consolidating a company means clearing “serious obstacles.” The score for the entrepreneurial environment fell to 4.1 (ranked 37th of 49), and WIPO placed Colombia 61st in the 2024 Global Innovation Index.

The central point is clear: Colombia generates founders; the United States generates scale. Colombia has energy. The United States has deep capital, innovation infrastructure, and a system that turns startups into durable companies.

Colombia’s problem is systemic. If you want, call me a systemic crybaby.

The main roadblocks for founders in Colombia

Informality: 56% of employment and 77% of microbusinesses operate informally (OECD 2019–2022). The formal ones compete at a disadvantage.

High, complex tax burden: A corporate rate of 35% (vs. the 23% OECD average).

Financing gap: Only 15.3% of microbusinesses can access credit.

Administrative burden: The digital one-stop window still doesn’t cover the whole country.

Infrastructure and logistics: High costs that make scaling difficult.

Weak entrepreneurial education: The worst-rated in GEM. Necessity-driven entrepreneurship dominates.

Why is it harder to build a company in Colombia than in the U.S.?

In the United States the challenge is to compete and scale. In Colombia the challenge is to survive the system + compete + scale.

Why are some of us founders crybabies?

It’s not for lack of drive. It’s because of everything the system lacks to turn that drive into big companies that export and generate quality jobs.

In my humble opinion as a crybaby founder, I believe Colombia doesn’t need more incentives to launch startups, more people making jams and pickles. It needs to redesign the system so companies can emerge on solid foundations that survive and can scale.

The root problem is the systemic friction accumulated over decades: you maximize the entry of new businesses, but you penalize their growth. The Colombian company-building system lets you be born, but the very conditions of that system won’t let you fly.

To change this, we have to keep crying until the establishment focuses on building at least the following strategic levers:

Smart formalization (attacking the root)

A progressive, phased tax regime (a “startup tax ladder“): minimal burden for the first 2 years and a gradual increase afterward.

Impact: more early formalization and less unfair competition.

Credit as infrastructure

Alternative scoring based on real data, massive guarantee funds, and open APIs for fintechs.

Impact: democratize credit and shrink the valley of death.

Radical simplification of the operating system

A national one-stop window, 100% digital, that actually works in every municipality. A single flow for registration, taxes, and social security.

Impact: drastically cut the time and cost of formalization.

Infrastructure to scale

Priority logistics corridors, temporary subsidies for exporting SMEs, and digitization of supply chains.

Impact: reduce costs and enable regional and international expansion.

Re-engineering entrepreneurial talent

Mandatory entrepreneurial education starting in school, scale-up programs (not just incubation), and real alliances between universities and industry.

Impact: shift from necessity-driven entrepreneurship to high-value entrepreneurship, innovation, and exports.

Now, comparing Colombia to the United States is a bit unfair and lopsided. To make the debate more balanced, let’s look at the neighborhood to see how we stack up against our backyard competitors. I invite you to study the following table:

LATAM: Plenty of Energy, Little Conversion

The table above reveals something that, for those of us who’ve lived entrepreneurship in the region, is no surprise — but it does expose structural problems. LATAM doesn’t have an entrepreneurship problem; it has a problem turning entrepreneurship into scalable companies.

Colombia (~25% TEA), Chile (~30%), Brazil (>25%), and Peru all post high indicators, showing a clear pattern: the region produces founders en masse. This is no trivial matter. From my anthropological perspective, the data is telling me about highly adaptive societies, where the individual responds to uncertainty with their own initiative. It’s what Harari would call a cultural capacity to reorganize reality in the face of scarcity.

But here’s the fracture that brings the tears to my eyes: high creation doesn’t mean high consolidation.

Colombia, for example, combines a high TEA with a low innovation index (61st) — lots of jam factories and very few disruptive innovation efforts. The reading is clear: high activity, low consolidation. Brazil replicates the pattern: lots of activity, a weak ecosystem. Peru shows more formalization, but falls even further in innovation (75th). Mexico, on the other hand, can’t even sustain entrepreneurial dynamism.

This leads us to a first conclusion. LATAM is a factory of attempts, not of consolidated results — and that’s where we differ from the United States.

Chile: The Relative Exception

Chile shows up as the most balanced system in the region. It keeps a high TEA while improving its position in innovation (51st) and in the quality of its ecosystem. This doesn’t mean Chile has solved the problem, but it has advanced on something critical: reducing systemic friction.

Programs like Start-Up Chile, clearer regulatory frameworks, and better access to capital have kept entrepreneurship from staying stuck at mere intention. Chile doesn’t have more talent than the rest of LATAM. It has better conditions for that talent to survive.

The United States: The Structural Contrast

The contrast with the United States is brutal and revealing. A lower TEA (19%), but:

A better GEM environment (4.8 / 16)

Top 3 in global innovation

Fewer founders, but more real companies, more scaling… This shatters LATAM’s central myth: the one that says we need more people starting businesses.

The truth is no, we don’t need more people starting businesses. We need better systems to scale the entrepreneurship that already sprouts up from under the rocks — and if that makes us crybabies, then I’ll keep crying until the people in charge of policy finally get the message.

The United States doesn’t beat us because its people try harder, but because the system converts better — capisce!

Capital, infrastructure, a homogeneous market, functional institutions… everything converges on a single point: lowering the cost and friction of scaling.

The Crybaby Paradox

If we connect all the dots, a deep paradox emerges: LATAM is one of the most entrepreneurial regions in the world… and at the same time one of the least efficient at creating durable companies.

This produces an invisible but critical phenomenon:

High human burnout

Low structural productivity

Fragmented innovation

Talent flight (as my own story proves)

Through Toffler’s logic, LATAM is trapped between two waves:

An incomplete industrial economy

A poorly implemented knowledge economy

And in that in-between space, the founder is left exposed.

I Don’t Cry for Sport, I Cry for What Could Be

After telling you part of my story — from the street urchin buzzing lawns in the La América neighborhood to founding Xcouter.com and SmartHOS.io, passing through failures that left me penniless — I’m not here to play the victim. I am not a crybaby founder. I’m a founder who got tired of slamming into the same wall over and over again.

Colombia has a brutal entrepreneurial energy. Founders spring up in every corner of the country. There’s grit, creativity, and drive to spare. The problem isn’t the people. The problem is the system: a system designed to let you be born, but not to let you fly. A system that rewards informality, punishes growth, drowns you in paperwork, and forces you to survive instead of scale.

I’ve lived in my own flesh what the data from GEM, the OECD, and WIPO says. I’ve felt informality in my own pockets — the lack of credit, the bureaucracy, and the “what’s in it for me.” And yes, I’ve also seen what happens when you cross the border: suddenly the same effort pays off ten times more. Not because I’m smarter, but because the ecosystem lets you breathe.

This isn’t about abandoning Colombia. It’s about refusing to romanticize a system that punishes anyone who wants to grow. We don’t need more motivational speeches or more incubators celebrating pretty failure. We need to change the rules of the game: smart formalization, real credit, radical simplification, infrastructure to scale, and education that produces high-value founders, not just survivors.

If Colombia wants to stop exporting talent and start retaining it, the change can’t be cosmetic. It has to be structural. Because as long as we keep maximizing the entry of new businesses and penalizing their growth, we’ll keep producing microbusinesses that never take off and founders who, sooner or later, end up looking abroad.

I’ve already made my decision. I left so I could fly. But I still believe, with all the grit I have left, that Colombia can be a great country to build in. All that’s missing is for the system to stop putting up roadblocks and start giving us wings.

To those who stay and keep fighting from the inside: respect and admiration.

To those who, like me, chose to look for other horizons: may it go well for us — but may we never lose the hope of one day seeing a Colombia where we don’t have to choose between staying and growing.

The future isn’t cried into being. It’s built with less speech and more sweat. May we build it together, without so much systemic weeping.

Meanwhile, I’ll keep singing to the sun like the cicada… so many times they killed me, so many times I died, yet here I am rising again, giving thanks to misfortune and to the dagger-wielding hand that killed me so badly it kept me singing!

**Sources

SUPPORTING ARCHITECTURE OF THE ANALYSIS**

1. Empirical Core — Entrepreneurial Ecosystem (Reality Base)

(This is where the hard evidence lives. What isn’t up for debate gets measured.)

Global Entrepreneurship Monitor (GEM)

Colombia Profile

https://www.gemconsortium.org/country-profile/52

Data used:

Total early-stage entrepreneurial activity rate (TEA), proportion of established businesses, motivation (necessity vs. opportunity), ecosystem score (EFC), global GEM ranking

United States Profile

https://www.gemconsortium.org/country-profile/122

Data used:

Entrepreneurial environment score (EFC), structural TEA comparison vs. Colombia

2. Innovation System — Scaling Capacity (Future Indicator)

(Here we measure who can turn energy into progress.)

World Intellectual Property Organization (WIPO)

Global Innovation Index 2024

https://www.wipo.int/en/web/global-innovation-index/2024/index

Data used:

Global innovation ranking (Colombia, U.S., Chile, Brazil, Mexico, Peru)

3. Systemic Infrastructure — Structural Constraints (What Holds the System Back)

(Here is the invisible friction: where good ideas go to die.)

OECD – Economic Survey of Colombia 2024

https://www.oecd.org/content/dam/oecd/en/publications/reports/2024/09/oecd-economic-surveys-colombia-2024_7b382d76/a1a22cd6-en.pdf

Data used:

Labor informality (~56%), infrastructure quality, institutional environment, structural limitations of the ecosystem

OECD – Financing SMEs and Entrepreneurs 2026 (Colombia)

Data used:

Access to credit (micro vs. medium-sized companies), banking concentration (>70%), business informality (~77% of microbusinesses)

4. Business Dynamics — Conversion to Formality (The Scaling Pipeline)

(Here we see whether the system converts intention into a real company.)

World Bank – Entrepreneurship Database (via Trading Economics)

https://tradingeconomics.com/colombia/new-business-density

Data used:

New business density (Colombia, Chile, Mexico, Peru)

5. Strategic Framework — Interpretation and Action (Cognitive Layer)

(Here lives the “how to think,” not the “what to measure.”)

McGrath, Michael E. – Product Strategy for High Technology Companies

→ Scalability, differentiation, and growth architecture

Li, Charlene & Solis, Brian – The Seven Success Factors of Social Business Strategy

→ Organizational alignment, execution, and resources

McKeown, Max – The Strategy Book

→ Strategic fundamentals and environmental analysis

Hillen, John – The Strategy Dialogues

→ Decision-making in complex contexts

Godin, Seth – This Is Strategy

→ Systemic view of markets and positioning

Whitehead, Jo – What You Need to Know About Strategy

→ Operational definition of strategy and decision logic

DISCLAIMER

This document is intended exclusively for informational, educational, and futurist-analysis purposes. It does not constitute financial advice, nor a solicitation to invest, nor a recommendation to buy, sell, or participate in any instrument, company, project, or asset.

If anyone uses the content of this material to solicit money, investments, or economic participation, consider it immediately a possible scam.

Always protect your personal and financial information.

🔮 Keep exploring FUTOPIX

Space Coast Colombia

What If Colombia Had a Thanksgiving Day

A couple of years ago I heard Hernán Jaramillo, founder of 10 AM, talking about Palantir. At first I barely paid attention; I figured the stock market wasn’t for me, I saw it as a thing for old-timers sipping coffee at the Bar Ganadero downtown in Medellín. Besides, I was deep in the weeds mining Bitcoin (BTC), and anything that wasn’t BTC struck me as a waste of time.

I remember it well: back then Palantir shares were trading somewhere between 16 and 20 dollars, a world away from the price of BTC. To my surprise, weeks later I started watching Palantir grow and grow like foam. While that was happening, I watched the members of the 10 AM chat buy Tesla as if the shares were going to run out tomorrow. It was only when I saw Solana climb after the FTX disaster — rising from $6 to $20, $40, and on up to prices that had seemed unthinkable — that I finally started to wise up.

With that scene playing out in the background, I began to ask myself: how do they do it? What do they see that I don’t? What do the people at 10 AM have that lets them read which stocks or cryptos will grow over time until they hit returns of 10X or more?

At the time it felt paradoxical. I make my living from futurism (foresight), and I found myself questioning it hard, because the “future” is the field of knowledge that puts food on my table. It seems only logical that, if I spend my days spotting emerging signals, I should know how these characters figure out where to put their money and pull off incredible returns.

For this and other reasons I wanted to get a closer look at how the minds of my conversation partners actually work, so I set myself the task of going a little deeper into the craft of learning to read the signals.

Futurism has long been thought of as the disciplined application of resources toward clearly defined goals under conditions of relative stability. Yet in an era defined by accelerating technological change, shifting consumer behavior, and systemic uncertainty, the traditional model is simply not enough. The future no longer arrives as a linear extension of the past; on the contrary, it emerges discontinuously, often out of phenomena that at first look insignificant, fragmented, even irrelevant.

These early, invisible, ambiguous, and usually unnoticed manifestations of change are what some experts call “Weak Signals.” Please don’t get confused: weak signals are not trends, they are not fully formed or consolidated opportunities or threats. On the contrary, they are the precursors of transformation, the earliest detectable indicators of systemic variation, hiding under the structural rug of markets, technology, and society.

In this FUTOPIX we’ll explore the concept of “Weak Signals” in a little more depth, to place it squarely within the field of strategy. That’s why it matters to understand that weak signals are not a peripheral capability but a core function of the strategist; once you learn to spot and develop them, they become a source for decision-making and for building competitive advantage.

The Nature of Weak Signals

Weak signals can be defined as early, low-intensity indicators of developments with significant potential to change the future. They are marked by their ambiguity, their lack of clear interpretation, and their appearance at the margins of the system, well before they surface at its center. Unlike trends — which are visible, measurable, and widely known — weak signals exist in a state of pre-consensus. They have not been validated by large-scale data, nor are they backed by a dominant narrative.

Their position in the life cycle of change is critical. Transformations rarely emerge fully formed. Instead, they evolve in stages. At first, signals show up as isolated anomalies, unexpected consumer behaviors, applications at the bleeding edge of technology, or niche cultural shifts.

For example, years ago the growing use of apps like Musical.ly (which later became TikTok) looked like teenagers goofing around lip-syncing to songs; for the average person it was just irrelevant noise. Take another case: the first experiments with plant-based proteins in food niches around 2015 looked like a marginal fad, but they were announcing the massive transformation of the food industry. In crypto, the resurgence of Solana right after the collapse of FTX — when few people trusted any alternative to Ethereum — was a weak signal that many of us ignored.

Weak signals are something like the foam on a beer: they keep rising as the patterns that form them gain coherence, evolving until they turn into trends that will eventually reconfigure industries and write new rules for the market. The strategic implication is clear: by the time a trend is widely recognized, the window to gain an edge has already closed.

Weak signals, therefore, occupy the most valuable and most challenging stage of this progression.

The Strategic Importance of Weak Signals

The importance of weak signals lies not in their predictive precision but in their power to expand the range of strategic possibilities. Traditionally, strategic planning tries to reduce uncertainty by forecasting outcomes and committing resources accordingly. Weak signals, by contrast, embrace uncertainty as a source of opportunity. They let us identify emerging possibilities before they take definite shape, and in doing so, create options instead of commitments. It’s precisely this shift in logic — from reducing uncertainty to operating within it — that redefines the very nature of strategy.

In high-uncertainty environments, the strategic goal stops being prediction and becomes positioning. When we lean only on established data and validated trends, we’re effectively competing in a known game, where advantages are incremental and often fleeting. But when we decide to operate on weak signals, we enter a different territory, one where the edge doesn’t come from executing better, but from seeing sooner. That’s where we gain access to “privileged insights,” born from understanding emerging dynamics that haven’t yet been shared or widely broadcast.

Such insights aren’t abstract; they translate into concrete advantages within the strategic game. From that anticipatory reading of the system, we unlock three forms of advantage: timing advantages, by acting before others (like the people who bought Palantir or Tesla early); narrative advantages, by shaping emerging markets before any consensus exists; and capital efficiencies, by investing little in the early stages through controlled experimentation rather than late, expensive commitments.

Despite the importance of these signals and the advantages they unlock, we have to admit that, as strategists, we sometimes fail to detect them and, above all, to act on them. Here is the central tension of the problem: understanding the value of weak signals doesn’t necessarily mean knowing how to operate them. These failures aren’t incidental; they’re rooted in our mental architecture, in the way we process information, and in the cultural frames from which we make decisions. The good news is that these aren’t fixed limitations but trainable capabilities, and so they can be reshaped if we approach them with intention (this is what some people at 10 AM do very well).

Cognitively, we tend to operate under a bias toward certainty. Our decision-making processes prioritize data that is measurable, validated, and aligned with existing models, because that reduces perceived risk. This is exactly where the friction with weak signals arises, because they, by definition, meet none of those criteria. They emerge in uncertainty, they live in the territory of the anecdotal, and they’re hard to quantify. Precisely for that reason, our minds discard them easily — not because they lack value, but because they don’t fit the filters we’ve been trained to make decisions with.

Structurally, this cognitive limitation gets amplified. We tend to organize information into compartments, creating mental silos that limit our ability to connect scattered signals. What we fail to integrate, we cannot interpret, and what we don’t interpret, we cannot act on. What shows up as isolated noise rarely becomes a pattern unless there’s a conscious effort to integrate it.

Culturally, on top of that, we tend to reward execution over exploration. We value whatever produces immediate, measurable, defensible results, while the uncertain, the emerging, and the unvalidated gets pushed aside. The result is a structural operational blindness: we optimize the present while making the future invisible.

From Detection to Action

To meet these challenges, as strategists we have to develop systematic approaches that let us detect and act on weak signals. The following framework has five interconnected stages: detection, amplification, interpretation, translation, and activation.

Detection: This is the systematic gathering of signals from a range of sources — what Hernán Jaramillo calls an “information diet.” The goal is to maximize our exposure to the market’s anomalies.

Amplification: We categorize, tag, and cluster the signals in order to spot emerging patterns. In anthropology we call this “recurrence,” and recurrence — even at low levels — is often the first indicator of emerging change in the system.

Interpretation: This is analyzing the trajectory, the potential impact, and the time horizon of the weak signals in order to build informed hypotheses from them.

Translation: This is about turning weak signals into concrete actions for our own personal strategy.

Activation: We turn the insights into small, effective, reversible actions. The goal is to learn to adapt, not to commit prematurely.

Operationalizing Weak Signals

For weak signals to become a personal skill, we have to build them into our way of life instead of treating them as occasional initiatives. That requires dedicated structures, processes, and concrete practices woven into our daily routine.

It’s worth keeping in mind that to enter signal-hunter mode, you have to switch on the mental habits your mind needs to detect and act on weak signals. These mental habits should run with a certain degree of autonomy, free from the immediate pressure of hitting metrics, and they should draw on diverse perspectives — from strategic thinking to technological affinity to reading the context.

Regularly reviewing the information we consume is essential to keep the momentum and make sure the signals surface; the art of detecting weak signals is above all a process of watching your environment. Unlike traditional strategic planning cycles (annual or quarterly), weak signals demand higher-frequency monitoring: weekly or biweekly reviews to catch shifts and respond dynamically according to our interests.

We have to reward curiosity, experimentation, and the generation of insights as the engines of this process, even knowing that signals often come from uncertain terrain. Without that internal backing, it’s impossible to engage in detection at all, because we’ll perceive it as risky and unproductive.

Personal leadership plays a critical role here: we ourselves have to actively support and legitimize the exploration of weak signals. Becoming a signal hunter is a strategic asset.

The Payoff

When we truly develop the ability to read weak signals, we stop reacting to trends and become someone able to anticipate the system’s moves before they become obvious. But what really changes isn’t just our position in the market — it’s the way we inhabit reality. We stop depending on consensus, we stop waiting for validation, we stop moving only once everyone else has already arrived. We start operating in a different territory, a territory where the future doesn’t yet have a name, but already has a direction.

That’s where a difference appears that is anything but minor: not everyone in the system is playing the same game. Most people observe, confirm, and execute; a few detect, interpret, and act. The first group optimizes what already exists, the second takes part in building what’s emerging. Some arrive safe, others arrive early.

In today’s economy — defined by speed, information overload, and constant disruption — the ability to hunt weak signals stops being an intellectual curiosity and becomes a critical competency. It’s no longer enough to be informed, no longer enough to understand trends, no longer enough to execute well. What makes the difference is the ability to see sooner, to hold ambiguity without collapsing, and to act without any guarantee.

Because there’s an uncomfortable truth here that few are willing to accept: the future doesn’t reward whoever understands first, it rewards whoever moves first. Moving first means discomfort, it means doubt, it means exposing yourself without a net. It means operating without the backing of consensus, without clear metrics, without an established narrative. It means thinking and acting like a strategist, not like a follower.

This is where the skill of hunting weak signals stops being optional. Not knowing how to read those signals in this environment isn’t a technical limitation, it’s a systemic vulnerability. It’s being trapped in cycles of reaction, depending on late decisions, competing in spaces where the advantage has already been captured by someone else. In other words, it’s always playing from behind — and from back there, it’s hard to reach 10X returns.

See Before You Act

Finally, I’d like to say that strategizing isn’t simply choosing where to compete, it’s understanding the evolving landscape in which we make decisions before that landscape is obvious to everyone. Weak signals are the earliest and most fragile expression of that system in transformation. They’re incomplete, ambiguous, often uncomfortable — but that’s precisely why they hold the greatest potential for advantage. Learning to detect them and, above all, to act on them is moving from reaction toward anticipation, it’s training a sensitivity that doesn’t rest on certainty, but on attention.

Because seeing weak signals isn’t a technical act, it’s an act of perceptual discipline. It’s consciously deciding to observe what others discard, to connect what looks unconnected, and to hold what doesn’t yet have form. It’s training the eye until the noise begins to reveal the structure.

In the end, our job as strategists isn’t to respond to change, it’s to recognize it before it becomes obvious, and — more importantly still — to act while there isn’t yet enough evidence to justify acting. Because in that space, between what is barely emerging and what has yet to be validated, is where real advantages are built; this is the space where the 10X gets made.

Weak signals don’t arrive as certainties, they arrive as discomfort, as a persistent hunch, as patterns that don’t quite fit. They are the future in its rawest, quietest, and easiest-to-ignore state. For that very reason, they’re the terrain where the ones who follow are separated from the ones who lead, which is why they cost us so much. We often think we’re the ones leading, but reality sometimes shows us that we’re only followers — that someone else is the one carrying the lamp that opens the path.

The invitation isn’t to understand weak signals, it’s to train them. Not to study them, but to practice them. Start observing differently, start questioning the obvious, start paying attention to what doesn’t fit; open your eyes to spot the nerd tinkering in a garage, the one playing with AI, the one who can read a whole industry — because that’s where something is changing.

In an economy like today’s, where time compresses and advantage is captured early, the difference between those who survive, those who compete, and those who build the future isn’t in the quantity of information they consume, but in its quality and their ability to read what hasn’t been explained yet.

Hunting weak signals isn’t an advantage, it’s a way of thinking, it’s a discipline, and increasingly, it’s a skill of economic supremacy.

DISCLAIMER

This document is intended solely for informational, educational, and futures-analysis purposes. It does not constitute financial advice, an investment solicitation, or a recommendation to buy, sell, or participate in any instrument, company, project, or asset.

If anyone uses the content of this material to solicit money, investments, or financial participation, treat it immediately as a possible scam.

Always protect your personal and financial information.

🔮 Keep exploring FUTOPIX

Why We Struggle to Think Big

GAIA 2100, From Homo Sapiens to Homo Humanoides

Designing the Future from the Future

Welcome to a new conversation. Today I want to tackle a topic that’s trending: “Context Engineering” as the centerpiece for building agentic AI architectures. Before I plunge into the deep end, let me give the context some context.

Most people who know me know I’m an anthropologist and a designer; those two disciplines are my great loves. What few people know is that before I ever set foot in anthropology, I studied Civil Engineering and Construction. And honestly, it’s not that I struggled there; I’ve always liked math, physics especially. But back in my engineering student days, the courses I loved most were the ones tied to the social sciences. Part of our training as engineers included a few courses in the history of construction, which were really just seminars in the history of architecture. In those classrooms I discovered that what truly fascinated me was form, design, and how human societies have interacted with it across history. Those experiences quietly laid the path that would connect anthropology and design.

During my early years as an anthropologist in training, I started my working life writing the training and service manuals for a well-known dial-up internet provider (back when you needed a phone line to get online, something the younger crowd will never quite grasp). From the hard chair of a call-center operator, I took my first steps toward understanding how much context matters in the world of technology. I caught so many earfuls of profanity from deeply pissed-off customers that many of those voices still echo in my memory. I owe all of them what I’ve built (thank you, sincerely).

So there, somewhere between training manuals, quality control, and customer service, I managed to fuse engineering, anthropology, and design. That blend let me translate customer needs into engineering requirements, and then turn those into better user experiences. And you know what? Over more than 25 years of professional experience, I’ve come to one conclusion: the key to solving most business problems has always lived in a perfect understanding of context. The angry customer of yesterday and the AI agent that hallucinates today are symptoms of the same disease: nobody took the trouble to map the context in detail.

That’s why understanding it is a critical success factor in the development of any product or experience, whether we’re talking clothing, medical services, banking, transportation, web apps, transactional platforms, you name it. And when it comes to AI, robots, agents, LLMs, once again context is king.

There’s a lot of talk today about “Context Engineering.” It’s an ambitious term, mostly because it sells. Slap the word “engineering” on anything and people instantly associate it with the hard sciences. But the truth is that context is more anthropology, sociology, psychology, and philosophy than it is engineering. I deeply respect the dissenting view, but context is less source code and more meaning. You can sit there and spew out all the code you want, but in the end, if that code doesn’t turn into meaning that drives adoption, it’s just more machine learning. For adoption to go mass-market, it has to leave the world of zeros and ones and enter the world of the social, and that only happens when adoption starts talking in street slang.

Context from First Principles

Thinking from first principles, context can be understood as the structured set of relationships that give meaning to an action, a signal, a behavior, or a decision. Many people wrongly assume that context is strictly tied to the environment in which things happen, but the truth is that beyond being a conceptual space, context is the interpretive field that makes understanding possible.

The word context comes from the Latin contexere, or “to weave together.” That’s why context can’t be thought of as something static; it’s a system of historical, cultural, economic, symbolic, relational, and temporal variables.

To define it rigorously, context is “the dynamic configuration of the conditions, relationships, and meanings that shape a perception, an interpretation, and an action.”

Seen through a systems lens, context operates as a filter (it decides what’s visible and what’s invisible), a constraint (what’s possible and what isn’t), a frame (what carries meaning and what doesn’t), and a force field (which behaviors get rewarded and which get punished).

Max McKeown captures this when he says that context determines how a strategic issue fits into “a broader pattern of leadership in the situations we’ve been working through in pursuit of success.” In other words, strategy can’t exist independently of context; it’s embedded in it. McKeown nails the executive dimension of context, but as we’ll see, that’s just one of its many layers.

The Anthropological Lens

We anthropologists don’t see context as backdrop. For us, context is the primary object of analysis in our profession. We know that human behavior can’t be understood in isolation, as if we were studying it inside a lab. Context has to be interpreted through symbolic systems, cultural codes, and social structures. This is exactly where the “context engineers” fall short: their mistake isn’t technical, it’s a failure of depth. They model the surface and call it architecture.

For anthropologists, context is a system of multi-vector layers, where each layer is itself another system. All of these layers are cultural in nature: economic, political, religious, technological systems, and so on. It’s more like a giant onion whose layers you peel back until you reach the core. Don’t confuse these cultural layers with the operational mapping layers we’ll get to later; both describe the same phenomenon but from different levels of analysis: one to understand it, the other to design it.

Clifford Geertz, one of the most influential anthropologists of the 20th century, defines culture as a vast web of meaning spun by human beings; context, in this sense, is the web. A wink and a blink are physically identical, yet we all know they’re not the same thing.

The Strategic Lens

If anthropology explains how meaning is built out of context, strategy explains how competitive advantages are built. From this angle, all strategy is contextual by definition. Jo Whitehead defines strategy as a quantum entity: it’s both objective and process, all happening at once. But the process can only exist in relation to context, that is... market conditions, the competitive frame, the organization’s internal capabilities, and so on.

Without context, strategy is just a form of abstraction. This is the second weakness of the “context engineers”: it’s not only that their contexts are abstract, it’s that they routinely ignore the power structures and market dynamics that condition every decision.

I’m fond of the concepts laid out by Michael Porter. Plenty of people call him a dinosaur, but I think much of his work still holds up, especially his model for mapping contexts, known as the five forces: competitive rivalry, the bargaining power of suppliers, the bargaining power of buyers, substitute products, and barriers to entry. These define the economic structure of a context.

Now, other experts like Mintzberg, Godin, and McGrath show up a little late to the party when they define context as an invisible, non-static system that co-evolves and persists, and therefore it’s strategy that must do the same (I say late because the anthropology classics had already worked this out).

In the world of tech systems, context includes rapid technological shifts, very short product life cycles, and accelerated competitive innovation dynamics. In short, in tech context is far too volatile, and it compresses in time on top of that, AI is certainly giving us proof of that.

Mapping the Context

How are we doing so far? Tired of all the theory? Take a deep breath, we’re almost at the good part. We’re about to step into the topic that brought us here: how do you map a context in order to build experiences? Because despite AI’s apparent novelty, agents are, in essence, experiences, and every experience can be mapped.

Before I walk you through how to map a context, let me remind you that we anthropologists think in “frameworks.” Apologies in advance for the anglicism, but honestly I’ve never found the word in Spanish that captures everything a framework-based mode of thinking represents. If you read my last Substack, you’ll easily get that thinking in frameworks is basically a conceptual mode of thought where all possibilities are possible.

In essence, context mapping is basically systems mapping. Methodologically, it helps to think of systems as if they were made of layers and levels of interaction (philosophically it gets a bit more complicated, but we’re not here to tie ourselves in knots). It’s worth stressing that no two systems are alike and that every system has its own specifics.

One important skill we anthropologists are trained for is pattern recognition. We’re always on the hunt for those recurring elements that let us make the system visible. What is it that repeats? Which patterns trace out a behavior? This is Layer #1 of context: observable reality, or surface structure.

I’ve watched plenty of “context engineers” jump straight to design, skipping contextual discovery entirely. The result is agents that produce hallucinations and serve up barely relevant information. Conclusion: agents that manufacture frustration. I know the path I’m proposing is longer than what most sources push on social media, but I guarantee you, with absolute certainty, that this model is “free of hallucinations and frustrations.”

So take my advice and, as the Supertramp song goes... “take the long way home.”

Let’s keep going. Write down everything you know about observable reality, leaning on market data, consumer behavior, trends, signals, and so on. Then move to identifying the structural context, Layer #2: which market forces hit it, what the economic constraints are, the power dynamics, the cost and pricing structures, and so forth. Then move toward Layer #3, the cultural context. This one is made of beliefs, social norms, narratives, and identity mechanisms. The next layer is symbolic context, Layer #4, composed of meaning systems, brand perceptions, and status signals. Layer #5 is temporal context; ask yourself what your project’s historical trajectory is and what its future expectations are. Finally, Layer #6 is hidden context; ask yourself which factors are actually driving the market, what its fear structures are, and what asymmetries are present, what’s invisible but decisive.

I know this sounds a bit overwhelming, and I’m certain you’re wondering, do I have to do all of that just to program a personal AI agent? My answer is NO. But the quality of the experience you want to create will depend on how much rigor you put into your context discovery.

Once you’ve done solid context research, the next step is to visualize it. I suggest you map the context as if it were a radar: at the center you must always place the person who consumes the experience, it could be you, it could be a customer. That center isn’t decorative, it’s the system’s center of gravity; everything you draw around it must relate to it, directly or indirectly.

Then start tracing circles around the center. These will be the interaction levels of your experience. Think of these circles as layers of proximity: the closest circle represents the most direct, immediate, and frequent interactions, while as you move away from the center you start to find more indirect, more sporadic, or even invisible interactions, invisible to the user but no less important for it. They can be clicks, decisions, moments of use, moments of friction, moments of abandonment, moments of discovery. Every experience is different, so draw as many levels as you think you need. There’s no correct number, there’s an appropriate depth.

Now, each of those circles has to be read in three dimensions, and this is where the map stops being a drawing and starts being a system. Divide your radar into three sectors and name each one: Touchpoints, Channels, and People.

The Touchpoint is the moment of truth. It’s the specific event where the interaction happens. It’s where the system materializes in front of the user. It can be a click, a call, a conversation, a notification, a delivery, a recommendation. It’s precise, it’s concrete, it’s measurable. If you can’t point to it, it’s not a touchpoint. At this level you have to ask yourself: what exactly is happening here? What does the user expect? What do they actually get? Where does the friction show up? Where is value created?

The Channel is the infrastructure that makes that touchpoint possible. It’s the medium through which the interaction occurs. It can be an app, a call center, a physical store, a chatbot, an email, an API. It’s the pipe the experience flows through. Very often design errors aren’t in the touchpoint but in the channel: latencies, friction, badly resolved interfaces, disconnections between systems. Here the question isn’t what happens, but where and under what conditions it happens.

People are everyone who interacts with the system, but not just as functional users, rather as contextual beings. This is where everything you already mapped comes into play: beliefs, expectations, fears, motivations, cultural context, socioeconomic level, life stage. The same experience changes radically depending on who lives it. That’s why, at this level, you have to ask: who is this person at this specific moment? Not in the abstract, but in situation.

When you map these three dimensions, Touchpoint, Channel, and People, you’ll start to see the patterns, you’ll start to identify where the system is aligned and where it breaks. Where there’s coherence and where there’s noise. Where context is being respected and where it’s being ignored. This is exactly where the real value of the exercise shows up: not in the map as an artifact, but in the ability to intervene. Because once you can see the system, you can redesign it, you can strip out friction, reinforce moments of value, adjust channels, reconfigure interactions. In essence, you can align the experience with the context.

Mapping context this way isn’t an aesthetic exercise, it’s a precision instrument. It’s what separates an agent that responds from one that truly understands.

From Context Maps to AI Systems

This is the point where the synthesis reaches its real transformative potential. Most artificial intelligence systems fail not from a lack of data, but from a deeper deficiency: the absence of contextual understanding. They process huge volumes of information, but without a framework that grants meaning, which inevitably leads to superficial results and decisions misaligned with operational or strategic reality.

That’s why AI shouldn’t be conceived as a system trained solely on data, but as an architecture trained on contextual structures. This means incorporating relationships, hierarchies, and meaning systems that let it interpret information beyond its raw form. In this sense, context maps become genuine design blueprints: they function as an operating system for intelligent agents, defining not only the inputs, signals, metadata, and relationships, but also the interpretation layers grounded in cultural rules and strategic logic, and finally the decision layer, where context-aligned responses are generated.

A clear example of this difference shows up in an AI-based sales agent. Without a well-structured context, the system would recommend products randomly or generically. But operating on top of a context map, the agent understands the customer’s life cycle, the industry’s dynamics, the buying signals, and the cultural codes that shape the decision. The result is a highly relevant action, with a higher probability of conversion and strategic coherence.

To reach this level of sophistication, AI training has to be structured across multiple layers of context. First, the data context, made up of structured datasets. Second, the relational context, which defines the interaction networks between actors. Third, the behavioral context, which captures patterns of action. On top of that comes the strategic context, which incorporates objectives and constraints, and finally the cultural context, which integrates language, tone, and social norms. Only by integrating these layers can you build truly intelligent systems.

Designing a personal AI agent is one thing; orchestrating an agentic architecture that operates across an organization’s processes is another. Although they seem essentially the same, scale matters when it comes to mapping contexts.

Context shouldn’t be understood as a passive background, nor as a mere environment, and least of all as a set of isolated data points. Context is, in truth, the invisible architecture of reality. It’s the system that organizes what’s possible, what’s visible, and what’s meaningful.

Anthropology teaches us that context creates meaning. Strategy shows us that context creates advantage. And artificial intelligence forces us to recognize that context must be modeled explicitly to generate true intelligence.

DISCLAIMER

This document is for strictly informational, educational, and futurist-analysis purposes. It does not constitute financial advice, an investment solicitation, or a recommendation to buy, sell, or participate in any instrument, company, project, or asset.

If anyone uses the content of this material to solicit money, investments, or financial participation, treat it immediately as a possible scam.

Always protect your personal and financial information.

🔮 Keep exploring FUTOPIX

The Sword and the Companion

The Dynamo Paradox

From Dr. Google to Dr. GPT

Genesis

Six months have passed since we first met here at FUTOPIX. It’s been an extraordinary ride for me. This little corner of the internet has helped me exorcise my demons and, at the same time, cracked open a door for me to express myself. I have to confess that at the start I was writing purely for myself, expecting nothing in return, like someone tossing a bottle into the sea with the secret hunch that somebody, on some far shore, will eventually find it and read the message tucked inside. But as the weeks went by, your comments have kept egging me on to keep this thing alive, episode after episode, week after week. Before I begin, I want to thank you for these six months and for making it this far, because if you read me every week, it’s because my essays give you something. I hope to keep doing exactly that for a long time to come. I also want to tell you that new projects are on the way, and I’ll be sharing them through this channel, like someone opening new rooms inside the same house.

The piece I’m bringing you today was hard to write. It may be the hardest of the 26 I’ve tackled since I started this adventure, and I don’t say that because the subject itself is so difficult, but because of the sheer number of debates that creativity has sparked across the ages. There are words that look transparent, but when you try to touch them, you discover they’re made of layers, of sediment, of centuries of accumulated argument. Creativity is one of them. We all think we understand it until we try to define it rigorously, and at that moment the concept turns slippery, shifting, almost untamable, gaseous.

I decided to write this piece about the creativity/AI relationship as the result of several conversations with friends, students and coworkers. I sense there’s a certain whiff of fear and ignorance around that relationship, a strange cocktail of fascination and suspicion, of enthusiasm and threat, so I wanted to give myself the room to write a bit about it. After four essays’ worth of deleting, editing, copying and starting over, I think I finally feel at peace enough to lay out my views on the matter.

I naively began by writing down the vox populi ideas of what most of us consider creativity, or the art of making the new, and every time an idea landed in my mind, I couldn’t help wondering, in parallel, whether that same conceptual gesture could be pulled off by a computational intelligence. As I moved through the questions, I kept running out of easy answers, until only a handful of paths were left on the table for understanding, with any seriousness, what it actually means to be creative.

Don’t think the text you’re reading today came easy. The truth is I had to push myself harder than usual to get here. I had to go back to my notes as an anthropology student to recall concepts and dig up authors who really know this terrain, so I could escape the shallowness with which so many treat this subject and hand you something a touch more rigorous, but that would still give us a decent map for moving forward along the road of ideas. Because there are subjects that today get discussed far too fast, almost as if having an opinion were enough to crown ourselves experts. That, precisely, is one of the risks of our era: confusing speed with depth, repetition with thought, and familiarity with understanding.

Along these lines, to explain what creativity is, we first have to understand what an idea is. This is a question that psychology and anthropology have asked themselves ever since they existed as disciplines. Without hesitation, my favorite author for understanding what ideas are is Gregory Bateson, an anthropology classic that all of us who carry the title simply have to read. Bateson has a wonderful body of work called Steps to an Ecology of Mind; I recommend it to anyone who wants to read a scientific text bursting with ideas and concepts, because it changed my life, at the very least.

Today I can say that, as an anthropologist, I’m profoundly Batesonian, though my anthropological DNA also carries a little bit of Chomsky, Lévi-Strauss, De Saussure, Morin and Capra, among others. You don’t think from a single author, but there are authors who become a kind of backbone of your thinking, and Bateson, at least in my case, occupies that spot.

To explain what an idea is, Bateson centered his studies on children, from newborns to adolescents. The basic question in his research model was how ideas take shape. To that end he observed how we humans learn, and he ended up classifying learning into three levels that, roughly speaking, he called the sensory, the categorial and the conceptual. Along these lines, he discovered that during our first years of life we discover the world through the five senses.

That’s why, when a baby eats dirt, in what pediatricians call the oral stage, it’s basically stress-testing its sensory system. Bateson argued that our brain thinks by generating comparisons in order to extract differences. That’s how babies learn to classify things according to the stimuli produced in the brain, and then extract the difference. When the baby eats dirt, its brain is comparing the taste of dirt with other, previously learned tastes: milk, purée, soup, and so on. The sensory system identifies the taste, processes it, and extracts the difference. In the Batesonian world, then, an idea is the result of comparing things and extracting the differences: idea = difference.

Here it’s worth pausing a moment, because this seemingly simple formulation holds enormous depth. If an idea is a difference, then thinking doesn’t consist merely of accumulating information, but of learning to distinguish, to discriminate, to perceive what separates one thing from another. Thinking isn’t piling up data like someone filling a warehouse; thinking is perceiving relevant differences. This matters when we start comparing human intelligence with artificial intelligence, because a machine also detects differences, but it detects them in a radically different way. The machine identifies statistical distances, correlations, probabilities, patterns within mathematical spaces; the human, by contrast, extracts differences that then get embedded in a symbolic universe. For an AI, a difference can be a variation inside a vector; for a human, that difference can turn into meaning, memory, judgment, emotion, metaphor or destiny. That’s where a decisive fault line begins to open up between the two kinds of intelligence.

Later, as we near seven years of age, we begin climbing to the next level of cognition, the level of categories. According to the Batesonian postulates, our brain takes those differences = ideas and orders them into categories.

It’s something like a set of Matryoshka dolls, where information gets organized according to what was learned at the level of the senses. Our categorial system organizes information according to our life experience and the cultural (symbolic) system in which we’re immersed. Let me open a parenthesis here to invite you to think of culture as a movie screen; it’s a gaseous, liquid thing onto which we project our life experience. It’s the screen where each society projects its own film, or to put it another way, it’s the operating system of society, where we install the set of beliefs within which we live, feel, interpret and even dream.

Back to the categorial system: Bateson says we organize ideas according to our experience, and we subject that experience to a classificatory system that sorts emotions into good or bad, fast or slow, sweet or salty, beautiful or vulgar, sacred or profane, and it keeps feeding itself until it manages to build what we understand as cosmogonies. Now, we spend practically our whole lives living in the categorial, so when do we acquire the conceptual? For Bateson, we all have the capacity to think conceptually, but it requires a capacity that’s scarce in many of us, and that’s the capacity to build contexts. Here, in my view, a central part not only of what we understand as creativity, but of intelligence itself, begins to play out.

Let me give you an example: take a sheet of paper; I’m going to ask you to think of a tree and draw it… with absolute certainty, most of you will draw an image resembling the following:

In our minds we hold a default idea, set by the context of experience we grew up in, of how trees are represented. This first abstraction belongs to the world of the sensory; it’s the way we learned to visualize trees when we were kids. Next, I’d like you to think of and draw an apple tree, for which most of us would proceed to draw an image like the one I’m showing you below:

Notice that we’ve basically added elements and colors that set our tree apart from others, which tells us we’re now thinking from the world of categories. Finally, I want to invite you to imagine and draw a forest.

Once again, each of us will bring to mind an image of the forest built from our own experience and context. From the conceptual standpoint, our forest could contain every possible image of trees, according to our experience with the concept of a forest. Something fascinating happens here: the forest is no longer just a sum of trees, but a broader mental structure, a totality organized by context. When we reach that point, we’re brushing up against the conceptual as the sum of all possibilities; that’s why we struggle so much to understand the proof-of-concept work of costume designers, among others.

The idea of context is so powerful that it invites even deeper reflection. The broader my knowledge of forests, the broader the contexts I can recreate, so concepts like cloud forest, rainforest and tropical jungle could be explained without difficulty, and the differences between them (ideas) can be extracted with great ease, to the point where we could argue why a wet jungle isn’t a tropical forest, or why two landscapes that to an uninformed observer might look identical actually answer to completely different ecological, symbolic and material logics. To think conceptually is, in large measure, to widen the field of possible contexts.

Here the comparison with AI shows up again. An artificial intelligence can process an immense quantity of examples of trees, forests, jungles and mountains. It can classify millions of images, learn regularities, segment attributes, label shapes and produce surprisingly accurate descriptions. But its relationship with context is not lived; it’s calculated. It doesn’t inhabit the forest, doesn’t fear the jungle, doesn’t remember a walk in the rain, doesn’t associate the smell of wet earth with childhood or the rustle of leaves with an intimate experience. It can reconstruct the pattern, but it can’t inhabit the symbolic world that gives the pattern its dimension. That’s crucial for understanding the limits of computational intelligence: we humans don’t just process information, we live it inside a fabric of culture and meaning.

Steve Jobs became even more famous for a speech in which he told his audience that there was nothing brilliant about his work and that all he did was “connect the dots.” But what exactly did Jobs mean when he talked about connecting the dots? The answer is simple, but its depth is immense: the more you learn about a subject (the dots), the greater your capacity to widen your knowledge and question it, so as to simultaneously extract the differences (ideas) and, in this way, connect them around a need (vulnerability). This process is what we call creativity. Creativity, then, is not a magic spark fallen from the sky nor a privilege reserved for a caste of the enlightened; it’s the capacity to relate contexts, to build bridges between domains, to discover fertile differences where others see only scattered information.

It’s worth saying something important here: today we tend to overvalue the speed with which artificial intelligence connects dots, but we forget that not every connected dot produces meaning. A machine can link concepts with astonishing efficiency, but human creativity doesn’t consist solely of connecting; it consists of knowing why to connect, from where to connect and what for. In other words, the human doesn’t just assemble relationships; the human introduces direction, relevance, desire, anguish, intuition and meaning. The machine can suggest combinations; human consciousness decides which ones deserve to become meaning.

And what about intelligence?

Let’s go back to the Batesonian examples. This man argued that the human brain thinks in images; that is, we process images more easily than text. Advertisers and designers know this all too well: a brain that thinks in images processes more information thanks to a biological premise; the optic nerve transmits data to our brain at a speed and processing density that make the image a privileged pathway for organizing experience. Let’s do a new exercise. If I tell you to think of your friend John… take a few seconds before continuing. What will happen in your mind is that it will start scanning its internal database of images of every friend you have named John. We’ll watch the images go by, identifying the distinctive features of each one. What probably comes next is that many of you will say: John, which John?

So I’ll answer: John, the one who looks like James. Again the scan of your image database will kick in, in which you’ll surely contrast the images of the Johns in your memory with those of the Jameses, to extract the differences (ideas) and finally conclude whether they look alike or not. Conclusion: an idea is the image of a difference.

What seems so simple actually describes a deeply sophisticated mechanism. Our brain doesn’t think like a linear list of data, but like a web of associations, contrasts and activations. We think by comparing, filtering, associating, discarding, trying again.

Fine, but this still doesn’t fully explain what intelligence is. To find answers we have to bring in another genius of anthropology and linguistics: Ferdinand de Saussure. This linguist coined what we know today as the theory of the value of the sign, which explains the relationship between the signified and the signifier, or between what a thing denotes and what it connotes. We have an expression that sums it up simply and without so much fuss: what the donkey says is one thing, what the driver behind it says is another. I love that saying because, in its apparent crudeness, it holds an extraordinary semiotic truth: things don’t mean anything by themselves; they mean within a relational system.

Culture, as the operating system for humans, dictates the rules of what actions, objects, life experiences, rituals and bonds mean to us. A plate of beans has one symbolic dimension across the Americas and another in the islands of the Japanese archipelago. It’s the same bean, yes, but while in the Americas beans accompany a primarily savory meal, in Japan they can be filling for sweet pastries. The object is the same; the symbolic dimension is different. That symbolic difference isn’t decorative or anecdotal; it’s precisely what turns a simple material object into a cultural unit charged with meaning.

I don’t buy this business of IQs (Intelligence Quotients) as absolute indicators of human intelligence; to me the matter is quite relative. What I do believe is that there are people with far broader contexts of information, which let them generate more neural connections, more fertile associations, more capacity for reading across domains. Let me explain myself. Imagine a person with a very high IQ who, through one of life’s twists, is abandoned deep in the Amazon jungle. What do you think that person’s odds of survival will be in a place they have no context for? They’ll probably use every tool of their knowledge to try to survive. Now, on the other side there’s a member of a nomadic Indigenous community of the Amazon, with a probably lower IQ against the reference subjects by which such things are measured. Who will have a better shot at surviving in that place? Before moving on, I want to clarify that I’m not underestimating our Indigenous person; it’s merely a comparison to illustrate the concept, since the same thing could happen in reverse, and I’m almost certain our Indigenous person would have an easier time of it in a metropolis than a genius in the middle of the bush.

Intelligence, then, can be understood as the capacity we humans have to connect dots between different domains and contexts, both within a single symbolic domain and across others. In other words, the speed, depth and plasticity we have for connecting biology with astrophysics, robotics with anthropology, design with survival, memory with invention, so as to extract the differences and build networks of meaning.

The difference between human intelligence and computational intelligence is that we build the former out of networks of meaning, while the latter is built through mathematical algorithms devoid of any symbolic content of their own and of lived experiences. To an artificial intelligence, it makes no difference whether you’re writing a romantic poem or a formula to reinvent Coca-Cola, because in both cases it’s processing correlations, regularities and probabilities within a mathematical field. The machine processes; the human interprets. The machine correlates; the human seeks meaning.

This difference is no small thing. An AI can recognize a poem’s style, imitate its meter, produce an emotive verbal structure and even simulate delicacy or melancholy, but it doesn’t know what it is to love, to lose, to wait, to grow old, to fail or to remember. It has no biography, because it has never been wounded, has never felt the urgency of time, nor shame, nor grief, nor desire. Its intelligence, however powerful, lacks the existential pressure that gives density to human experience. Without that density, what you have is a hollow intelligence, formidable at operating, but not necessarily intelligent at understanding in the anthropological sense of the word.

What role does vulnerability play in all this mess?

Since time immemorial, when we painted the cave walls, forged arrowheads as tools for hunting big game or gathered seeds on the floodplains, vulnerability has been the mother of creativity. No one can dispute that creativity switches on when we’ve got the predator breathing down our neck. Postwar Germany wouldn’t be what it is today were it not for the tremendous vulnerability its population was left in after WWII, and the same goes for Japan. The thing is, nowadays we prefer to call vulnerability unmet consumer needs. Maslow wrote a whole treatise on the subject; I’m not going to get into that debate, I just want to say that vulnerabilities are the active spectrum of creativity, and that depending on the level of those vulnerabilities, so too will be our response.

Here I want to insist on something I find central: human creativity is not born of comfort, it’s born of friction. It’s born of the fragile body, of scarcity, of need, of fear, of curiosity, of the discomfort of not understanding, of the pain of falling short, of the pressure to solve. The wheel, the needle, the city, medicine, myth, agriculture, philosophy and even art can be read, to some degree, as creative responses to human vulnerability. We don’t invent for the fun of it; we invent because something is missing, because something threatens us, because something calls to us, because something hurts us. Here again an enormous border with AI appears. The machine doesn’t know vulnerability, doesn’t feel hunger, doesn’t fear death, doesn’t need shelter, doesn’t fall in love, doesn’t grow old, doesn’t seek transcendence. It can optimize solutions, but it doesn’t suffer the problem. Between suffering a problem and processing it there’s a gigantic anthropological distance.

Necessarily, when we talk about creativity and human intelligence, we have to talk about our attitudes toward needs, and on that front I’ll say there are at least two perspectives. There are people who face needs with a jigsaw-puzzle mindset, connecting little pieces (ideas) one by one until the puzzle takes shape (meaning). But what happens when pieces are missing? This is where the challenges to creativity arise. Now, on the other side we have those who see problems like engine builders; that is, they connect dots (ideas) with which they seek to move forward, refusing to let the absence of a single part stop them. Along these lines, which kind of intelligence do you identify with more, the jigsaw-puzzle assembler or the engine builder?

I find this comparison powerful because it describes two very different cognitive dispositions toward the world. The puzzle assembler needs the final image, needs the exact piece, needs to close off the meaning. The engine builder, by contrast, is willing to improvise, to substitute, to reconfigure, to advance even when the map isn’t complete. One seeks completeness; the other seeks movement. One orients by the finished image; the other by the possibility of generating traction. If we think about it carefully, the age of AI is demanding of us more and more engine-builder intelligence and less jigsaw-puzzle intelligence, because the world coming won’t reward only those who know how to repeat a recipe, but those who know how to reconfigure domains, cross contexts and build meaning amid the abundance of automated information.

What we know as intelligence also has to do with the way our neural network operates (connecting the dots). There are those who live in attention mode, that is, constantly monitoring the information they consume, filtering out what adds value to their system of connections and eliminating the noise. Their information diet is policed rigorously. On another level are those who live in something neuroscience calls the DMN (Default Mode Network). No matter what they’re doing, their mind lives in constant connection training; rather than hunting for the missing piece, they seek to hack the default order to find new formulas or disruptions. Finally there’s scanner intelligence, the kind of intelligence permanently searching for signals, in order to detect them, amplify them, extract the differences (ideas) and add them to its symbolic domain. They live in permanent reinvention.

These three forms aren’t sealed compartments, but they do help us understand distinct styles of being in the world. The one who lives in attention mode filters. The one who lives in the DMN recombines. The one who lives in scanner mode detects weak signals before everyone else. If there’s anything I want to underline here, it’s that creativity doesn’t depend solely on having information, but on the way the mind works with that information. AI, for example, may look like an extraordinary scanner-mode machine, because it tracks patterns at brutal scales; it may also look like an inexhaustible recombination machine, because it articulates scattered elements with great speed. But it still lacks the thing that makes creativity truly human: the tension between the inner world and the outer world.

Computational intelligence and creativity

Whereas human intelligence and creativity require broad networks of context and symbolic content to develop, mediated by culture as a catalyst and accelerator of knowledge, computational intelligence and creativity lack the capacity to analyze content outside the symbolic field of whatever they’re processing. Their operating system is algorithmic, while for humans the operating system is governed by culture, which allows unlimited combinations of dots (ideas). Think of AI as a system whose OS is limited to a linear program within an exclusive field, or if you prefer, an LLM, whereas human intelligence can run transversally across all operating systems; it would be a multivectorial intelligence set against the specialized, statistical and bounded character of LLMs. It’s agnostic in operating-system terms.

I want to make an important clarification so I’m not misread. I’m not saying AI is small potatoes. Quite the opposite: I think it’s an extraordinary tool, probably one of the most powerful humanity has ever produced. What I’m saying is that its power isn’t equivalent to our form of consciousness. AI can help us explore possibilities, accelerate iterations, simulate scenarios, find improbable connections, push cognitive work toward speeds previously unthinkable. But that’s no reason to make the mistake of granting it a cultural sovereignty it doesn’t possess. The machine doesn’t generate culture on its own; it participates in culture through the human materials it was trained on and through the human uses we give it.

In other words, AI can produce content with astonishing power, but producing content is not the same as producing meaning. Meaning emerges when a human community interprets, argues over, adopts, rejects, ritualizes, transmits or transforms something within a cultural network. A poem doesn’t become important because it’s well written, but because it strikes a human chord and manages to insert itself into a symbolic economy. A theory doesn’t change the world just for being coherent, but because it reorganizes the way a society thinks about reality. A technology doesn’t matter just because it works, but because it alters habits, expectations, relationships and structures of power. That’s where the difference lies.

Now then, will we be replaced by AIs? As long as AIs don’t have the capacity to generate culture, we’re safe. When I say generate culture, I don’t mean mixing signs, styles or references, but the deepest part of the cultural process: building meaning out of experience, out of a vulnerability, out of a shared history, out of the experience of being alive within a human community. The machine can be a great mirror, a great amplifier, a great cognitive telescope; but the astronomer is still the human. It can help us traverse the territory, but it still can’t produce, on its own, the symbolic ground on which we walk.

The AI debate tends to close with a question that inevitably shows up in any contemporary conversation: will we be replaced? Deep down, this question reveals more about our cultural insecurities than about the true nature of intelligence. When we fear being replaced by a machine, we’re assuming that human intelligence and computational intelligence belong to the same order of phenomena. But across these pages we’ve seen that this isn’t so.

The truly interesting question isn’t how fast a machine can produce something, but what that something it produces means. Here a fundamental difference appears: whereas AI operates over statistical patterns within an algorithmic domain, human intelligence operates within a symbolic universe built by culture.

In other words, an AI can combine millions of texts to produce a poem that’s perfect from a formal standpoint, but it doesn’t possess the cultural fabric that lets it understand what love, loss or nostalgia mean within human experience. It can recognize the structure of a metaphor, but it doesn’t inhabit the world where that metaphor acquires meaning. AI calculates; human intelligence interprets.

If we recall the Batesonian definition of an idea as a difference, we can see this point more clearly. An artificial intelligence can detect statistical differences between patterns of data, but those differences have no cultural meaning for it. They’re simply mathematical variations within a vector space. For humans, by contrast, differences don’t just exist; they mean something. They get inserted into symbolic networks, into stories, into emotions, into cultural contexts that transform a mere difference into an idea charged with meaning.

That’s why, when we talk about human creativity, we’re not simply talking about producing something new. We’re talking about reconfiguring networks of meaning within a cultural system. A technological invention, a scientific theory or a work of art doesn’t just introduce novelty; it reorganizes the way a society understands the world. That’s the true creative act.

AI, by contrast, operates within a system we could describe as a space of probability without experience. It can generate millions of possible combinations within a semantic field, but it lacks the thing that feeds human creativity from its deepest roots: lived experience, vulnerability, history, cultural memory.

Let’s recall that above we affirmed that vulnerability has always been the mother of creativity. Tools were born because humans were physically weak against predators. Cities were born because cooperation increased our chances of survival. Science was born because we wanted to understand natural forces that overwhelmed us. Even art, in many senses, was born as an attempt to tame the fear and mystery of existence.

Human creativity, therefore, doesn’t arise from an abundance of information but from the friction between our limitations and our aspirations. It arises when something hurts us, when something is missing, when the world doesn’t quite fit our expectations. The machine, by contrast, knows no vulnerability. It feels no hunger, no fear, no existential curiosity. Without experience, there’s no culture.

For this reason, when many announce the end of human creativity in the face of AI’s advance, they’re actually confusing two radically different things: content generation and the creation of meaning.

Culture is a living system that emerges from the interaction of millions of human minds interpreting the world from different perspectives. It’s a symbolic system that evolves across generations. AI can participate in that system as a tool, as an amplifier, as a catalyst even, but it can’t substitute for the cultural process that produces it.

In a sense, we could say AI functions as a cognitive telescope. It expands our capacity to explore possible combinations within existing knowledge. But just as a telescope doesn’t replace the astronomer, artificial intelligence doesn’t replace the thinker. What it does is expand the horizon from which we think.

If anything characterizes our species, it’s precisely that capacity to extend our faculties through tools. Fire amplified our capacity to transform nature. Writing amplified our collective memory. The printing press amplified the circulation of knowledge. The computer amplified the processing of information. AI amplifies our capacity to explore the space of ideas.

The right question isn’t whether AI will replace human creativity. The right question is what kind of creativity will emerge when we live alongside intelligences capable of processing information at inhuman speeds. What new forms of thought will appear when massive computation stops being a scarce privilege and becomes everyday infrastructure? What kind of authors, scientists, designers, storytellers or strategists will emerge when thinking no longer means doing it all alone, but knowing how to converse with systems capable of brutally expanding the field of possibilities?

Because every time humanity has created a powerful tool, human creativity hasn’t vanished. It has changed form. Let me ask you: did painters disappear with the rise of photography? What we’re witnessing now isn’t the end of human creativity, but an expansion of its territory.

AI will be able to help us explore combinations of ideas that would once have taken decades of work. It will be able to suggest unexpected connections between distinct domains of knowledge. It will be able to accelerate processes of research, design or discovery. But the decision about which problems are worth solving, which questions are worth asking or which meanings we want to build as a society will remain a deeply human decision.

Because at the end of the day, machines can process information, but only we humans live the stories.

That’s why, if there’s any conclusion to draw from this reflection, it’s that human creativity is not threatened by AI. What is threatened, in any case, is our intellectual laziness. The ease with which we can outsource thinking to automatic systems. The temptation to consume ideas instead of building them. The risk of turning a tool for cognitive expansion into a crutch for the imagination.

Artificial intelligence will be as powerful as the culture that uses it. If we use it to repeat what we already know, it will become a gigantic echo machine. But if we use it to explore the unknown, to connect distant domains, to widen our collective imagination, then it will become one of the most extraordinary tools humanity has ever created.

Ultimately, the future of creativity doesn’t depend on artificial intelligence. It depends on our will to stay curious. On our capacity to keep widening our contexts, to keep connecting dots between seemingly unrelated domains, to keep asking ourselves why the world is the way it is and how it could be different.

DISCLAIMER

This document is for informational, educational and futurist-analysis purposes only. It does not constitute financial advice, nor a solicitation to invest, nor a recommendation to buy, sell or participate in any instrument, company, project or asset.

If anyone uses the content of this material to solicit money, investments or economic participation, treat it immediately as a possible scam.

Always protect your personal and financial information.

🔮 Keep exploring FUTOPIX

Context as OS

The Sword and the Companion

The Dynamo Paradox

When Intelligence Became Abundant

For centuries, we humans organized our entire civilization around a quiet premise: intelligence was a scarce asset. Societies rewarded whoever could think better, calculate faster, remember more, or make more complex decisions. Universities became temples of intellectual selection — holding a college diploma became more valuable than holding a title from a royal court. The high-prestige professions grew up exactly where human thinking was hardest to replace.

And yet, thanks to the advances in AI, we are now entering a historic moment where that premise is starting to wobble. For the first time since we became a civilization, intelligence — at least in its computational form — is beginning to turn into an abundant resource. Artificial intelligence models, agentic systems, and humanoid robotics are extending the reach of automated thinking at a pace that just a decade ago seemed improbable. What once took entire teams of specialists can now be done by digital systems capable of analyzing data, writing code, producing text, interpreting images, or coordinating complex processes in a matter of minutes. What used to take us weeks can now be done in minutes.

This phenomenon should not be read as an existential threat or a technological dystopia; we should understand it as a transition toward a new paradigm (remember Thomas Kuhn’s concept of normal science from “The Structure of Scientific Revolutions,” which we’ve already discussed in earlier editions of FUTOPIX).

Along these lines, every great technological leap — agriculture, the printing press, the steam engine, electricity, the Internet — profoundly reorganized the role of human beings in the economy and in culture. The artificial intelligence revolution will be no different. What will change isn’t only the technology, but the very structure of economic value built around it.

Remember that when physical strength was abundant, industrial machines redefined human labor; when information stopped being scarce, the Internet transformed the knowledge economy. Now that computational intelligence is starting to multiply, we’re entering a new phase in the reorganization of the productive system.

In that context, an unavoidable question surfaces — especially for those of us who are parents, educators, or future-builders:

What should our kids study in order to thrive in a world where computational intelligence is no longer a scarce resource?

Answering that question demands that we look past the tech headlines. It requires understanding the weak or early signals that hint at where the global economic system is heading. That, in turn, calls for a foresight mindset — one capable of reading emerging patterns before they harden into dominant trends.

Recently, Citrini Research published a piece titled “The 2028 Global Intelligence Crisis,” in which they map out exactly these signals — not as a doomsday prediction, nor as an exercise in economic science fiction, but as an intellectual experiment that explores the possible consequences of a world where AI works so well that it transforms the very foundations of employment, productivity, and economic distribution.

From the FUTOPIX perspective, documents like this aren’t warnings we read in fear — they’re navigation charts, incomplete maps of territory still taking shape.

Some of the weak / early signals that show up in that analysis are cognitive automation, autonomous agents, partially robotized economies, and new financial infrastructures. They don’t describe an inevitable collapse; they describe a deep reorganization of the system — and, like every reorganization, it also opens new frontiers of opportunity.

Detecting and understanding these signals is the first step toward building a solid educational strategy for the coming generations, because the goal is not to protect our kids from technology, but to prepare them to use it as an extension of their creativity, their intelligence, and their capacity to build better slices of the future for the generations that follow.

Reading the Weak Signals…

Citrini Research describes a set of emerging mechanisms that, though still in their infancy, could profoundly transform the global economy over the coming decades. This symptomatic evidence doesn’t show up as abrupt ruptures, but as what we futurists call weak signals — early hints that reveal how the foundations of the productive system are slowly being reconfigured. It’s like learning to read the ventures cooking in a garage that carry the potential to change the world.

The first of these signals appears as a technological feedback loop driven by the compounding adoption of AI. As companies start integrating AI systems to boost operational efficiency, certain professional roles will experience a gradual shrinkage. What was initially rolled out as a support tool ends up becoming an optimization mechanism. When operating costs drop thanks to automation, organizations free up capital that they then reinvest in new technological optimizations. That capital feeds new productivity gains, which in turn incentivize more automation. The result is a virtuous cycle of technological acceleration that constantly feeds itself.

This kind of dynamic isn’t new. Similar phenomena played out during the industrial mechanization of the nineteenth century and later with the computer automation of the twentieth. But the fundamental difference in today’s revolution is that AI acts directly on the tasks demanding cognitive activity — the ones we used to think of as exclusively human. This isn’t just about replacing physical strength or repetitive chores; now the technology steps into the processes of analysis, interpretation, planning, and decision-making.

The first signs of this shift can already be seen in tools like GitHub Copilot, which dramatically amplify programmers’ productivity, or in advanced automated data-analysis systems that let companies run studies that once required entire teams of specialists. I’m not saying these advances wipe out human work entirely, but they do profoundly redefine its nature. Professionals in these fields will stop being the direct executors of these tasks and become designers, supervisors, or integrators of intelligent systems.

A second weak signal appears with a phenomenon some analysts have begun to call Phantom GDP. This concept describes an economy where a growing share of output is generated by machines that don’t take part in the traditional cycle of human consumption. Robots don’t buy homes, don’t plan vacations, don’t consume cultural goods. When a significant portion of productivity comes from automated systems, economic growth can begin to partly decouple from human employment.

We can already see early hints of this in fully robotized warehouses and restaurants, in highly automated logistics chains, and in factories where production keeps running through the night with no direct human presence. These systems generate enormous efficiency and economic value, but they also redistribute the gains toward whoever owns the technological infrastructure, the computational capital, or the automation platforms.

Another meaningful signal emerges with the arrival of agentic AI (AAI) — that is, systems capable of planning, executing, and correcting complex tasks autonomously. What sets agentic AI apart from traditional software models is that these systems don’t just respond to specific instructions. They can analyze goals, design strategies, and execute multiple intermediate steps to reach them.

The first versions of these technologies can already be seen in experimental tools that can write complete software, manage business processes, or coordinate complex logistics operations. Systems like these suggest that many layers of economic intermediation could gradually vanish. When an intelligent agent can weigh options, compare prices, negotiate terms, and execute decisions in a matter of seconds, the traditional frictions of the market start to drop sharply — in some cases all the way to zero (take a look at openclaw).

This phenomenon seems to be on a collision course with another quiet transformation moving in parallel. I’m talking about the evolution of global financial infrastructure. In the digital ecosystem, there already exist blockchain-based payment networks capable of near-instant transactions at extremely low cost. Platforms built on networks like Solana are proving that value can move without the traditional layers of financial intermediation.

If (AAI) agents begin to interact economically with one another — negotiating purchases, contracting services, managing digital resources — we could enter a stage where economic transactions become partly autonomous. In that scenario, some components of the traditional financial system will have to adapt to a far more automated and programmable environment. Something that traditional banking finds both hard and painful.

When we look at these signals together — cognitive automation, Phantom GDP, agentic AI, and the new decentralized financial infrastructures — a clear pattern emerges. This isn’t a simple technological evolution; it’s a profound reorganization of the global economic system.

Now, these signals should not be read as announcements of inevitable crises. Economic history shows us that every great technological transformation generates both disruption and new opportunity. What these signals are really showing us is that the foundations of productivity, of human work, and of value creation are entering a liminal phase of transition.

Understanding these transitions before they become trends is precisely the point of strategic foresight. Because in the moments when the system begins to reorganize itself, those who manage to read the early signals don’t just adapt to the future — they dare to build it.

The Professions in Transformation

When you look at these signals together, a structural shift in the labor market comes into focus. I should note that not every profession will face the same degree of transformation. The occupations whose essence is mainly about processing information in routine ways are among the most exposed to the reconfiguration AI brings.

For decades, much of the knowledge economy was organized around tasks like reviewing documents, comparing information, processing data, or verifying procedures. Professions such as operational accounting, routine financial analysis, legal contract review, or certain kinds of commercial intermediation were born precisely in a context where the ability to analyze information was limited, expensive, and slow. That context is changing forever.

Today, intelligent agents can analyze contracts, catch regulatory inconsistencies, compare financial terms, or process massive volumes of data in seconds. This capability doesn’t automatically mean those professions disappear, but it does mean a transformation of their operational core. A professional’s value will no longer sit so much in performing the basic analysis of the information, but in interpreting its implications, designing strategies, and making complex decisions inside uncertain contexts. These roles will have to shift from passive agents to active players — we’ll move from the accountant who only presents the books to the one who suggests strategy.

A similar phenomenon is starting to show up in software development. For a long time, writing code was a skill held by a few, considered highly specialized and demanding years of technical training. But the arrival of AI-assisted programming tools is beginning to automate part of the most repetitive tasks in the development process. Generating simple functions, fixing syntax errors, or completing code snippets are activities today’s systems can already handle very efficiently. When it comes to laying out architectures and wireframes, AI does it faster than humans and without mistakes. What used to take a developer weeks, AI does in minutes — with the added advantage that the user can iterate with the system in real time until they land on the result they want.

This doesn’t mean developers are going to disappear. What will probably happen is a migration of the purely technical roles toward higher levels of abstraction. The engineer of the future will have less to do with hand-writing every line of code and more to do with designing ontological systems and technological architectures, integrating them into complex systems, and supervising fully automated environments. In other words, engineering will evolve from technical execution toward the orchestration of ontological systems.

In parallel, the development of humanoid robots will introduce a new component into this transformation. Unlike traditional industrial robots designed for extremely specific tasks, humanoids aim to replicate human physical capabilities — walking, manipulating objects, interacting with variable environments. For anyone who imagines the humanoid sitting beside you typing information into a keyboard, the answer is: why would it? Typing data is a wildly inefficient process. Just think about how R2-D2 (Star Wars) jacks into a spaceship’s systems, plugging directly into the network to transmit packets of information. Humanoids will simply send data via proxies — even over Bluetooth signals.

Although these devices are still in early phases of development, their potential points toward taking part in activities like advanced logistics, technical maintenance, or certain flexible-manufacturing operations (FUTOPIX has plenty of articles on this).

The challenge isn’t about defending specific occupations against technology. The real question is understanding which human skills become more relevant as technology advances. In this transition, those skills tend to cluster in areas dominated by interpretation, creativity, strategic judgment, and the ability to grasp complex systems.

In the end, the professional map of the future won’t be defined solely by the arrival of new technological tools. It will be defined by the way human beings learn to integrate them within broader economic, cultural, and social structures. That’s exactly where human intelligence remains decisive.

What Our Kids Should Study

If we accept that the near future will be a hybrid ecosystem where human intelligence and artificial intelligence coexist, then it’s obvious that education will have to transform as deeply as the economy and technology have. For decades, educational systems have been designed to prepare individuals capable of performing specific tasks inside relatively stable labor structures. But in a world where machines are taking on more and more operational functions, education has to reorient itself toward capabilities that let human beings understand, direct, and expand the technological systems now emerging.

One of the pillars of this transformation will be solid AI literacy. It’s not that every student has to become a specialized machine learning engineer or a data scientist. The broader challenge is understanding how intelligent systems work, what kinds of decisions they can make, and where their limits lie. Just as digital literacy became indispensable in the Internet era, in the coming decades grasping the logic of algorithms and machine-learning models will be part of the basic knowledge of any educated citizen.

This understanding will unlock something more important than mere technical skill. It will make it possible to interact critically with the machines, to design more useful tools, and to keep automated systems from becoming black boxes that most of society can’t comprehend.

A second inevitable educational pillar will be philosophy and applied ethics. As automated systems take part in decisions tied to the economy, medicine, justice, or public administration, moral questions will stop being academic debates and become practical problems of social design. How should intelligent systems behave in situations of conflict? How are the gains of automation distributed? How do we protect human autonomy in an environment increasingly mediated by algorithms?

Faced with these questions, philosophy stops being an abstract exercise and turns into a strategic discipline for governing technological societies. Understanding ethics, political theory, and the philosophy of knowledge will let us shape individuals capable of designing institutions that weave together technology and social responsibility.

A third fundamental component will be the deliberate development of creativity and emotional intelligence. Machines are demonstrating an enormous capacity to process information, but they still struggle to fully grasp human contexts, cultural narratives, and the emotional complexity that defines societies. For that reason, creative and emotional capabilities shouldn’t be treated as educational extras, but as strategic assets in the economy of the future.

The arts, music, theater, literature, and visual storytelling work as genuine laboratories of human imagination. That’s where you train skills like symbolic interpretation, the construction of meaning, and the ability to connect seemingly unrelated ideas. In an economy where information is abundant, the ability to imagine new combinations of knowledge takes on extraordinary value.

Alongside these humanistic dimensions, it will also be crucial to strengthen applied technical training. The next generation will live side by side with robotic systems, automated infrastructure, and complex technological environments. That’s why educational programs that integrate mechanics, electronics, mathematics, physics, bioelectronics, bioengineering, biomechanics, and AI will produce professionals able to work directly with the physical technologies that will shape the economy of the future.

This kind of training won’t be confined to traditional academic labs. Technology workshops, digital-fabrication environments, and hands-on learning programs will become essential spaces for developing skills that blend technical thinking with real-world problem-solving.

Finally, the education of the future will inevitably lean toward interdisciplinarity, and toward the skills for cross-cutting thinking (T-shaped knowledge). The great innovations rarely come from a single field of knowledge. They arise when different disciplines meet and mix. Biology meets robotics to create new forms of bioengineering. Art crosses with technology to produce immersive experiences. Psychology integrates with design to build more intuitive human interfaces.

In this new educational paradigm, students won’t be trained solely as isolated specialists, but as integrators capable of connecting diverse knowledge to solve complex problems.

Preparing our kids for this future doesn’t mean pinning down a rigid list of professions. It means equipping them with a flexible intellectual architecture, one that can evolve as the world changes. Because in an era where intelligence becomes abundant, the true human advantage will lie in the capacity to keep learning, reinterpreting, and continually creating new forms of knowledge.

A Balanced Vision of the Future

When we analyze large-scale technological transformations, it’s common to find narratives dominated by risk. Every great innovation has stirred up similar fears — job loss, economic collapse, the dehumanization of society. And yet, culture is the great filter that always points us toward social reinvention.

Every industrial revolution began with uncertainty. Mechanization transformed farm work, electrification reorganized cities, and digitization radically altered the information economy. But in every case, the transition periods ended up generating new forms of prosperity, new professions, and new social capabilities.

The arrival of AI should be understood within that same historical frame. This isn’t a technological anomaly; it’s a new stage in the evolution of the tools that amplify human capability. Managed with institutional intelligence, this technology could unlock levels of productivity that until recently belonged to the realm of economic speculation.

Intelligent automation has the potential to meaningfully cut the time people spend on repetitive or highly structured tasks. In that scenario, a considerable share of human energy could shift toward activities dominated by creativity, scientific research, cultural exploration, and social innovation. What we perceive today as disruption could, over time, become an expansion of the space for human action.

But for that transition to unfold in a balanced way, the central challenge won’t be technological — it’ll be organizational. Societies will have to develop new ways of distributing the gains from automation while, at the same time, ensuring their educational systems evolve at the pace of technological change. In other words, technical progress on its own doesn’t guarantee collective prosperity. The way we organize our institutions will determine the final outcome of this transformation.

The Challenge Isn’t AI — It’s Our Imagination

Every technological revolution produces two great cultural narratives. On one side, a narrative of fear appears, imagining a future where machines end up displacing human beings. On the other, a narrative of construction emerges, one that reads technology as an extension of our cognitive, creative, and productive capabilities.

Historical experience suggests that the second narrative tends to win out over time. The printing press didn’t eliminate human thought; it multiplied the circulation of ideas. Electricity didn’t replace human labor; it radically expanded industrial and urban productivity. The Internet didn’t shrink cultural creativity; it opened up an entirely new space for expression, collaboration, and global knowledge. AI will most likely follow a similar path.

It’s true that some professions will be transformed, it’s true that certain economic models will have to adapt, and it’s true that the value of human work will evolve into new forms. But these transformations don’t represent the end of human relevance. Rather, they represent a historic opportunity to redefine what kind of intelligence we want to cultivate as a civilization.

For far too long, educational systems focused on training individuals to repeat processes, memorize information, or execute highly structured tasks. That model answered the needs of an industrial economy where operational efficiency was the main organizing value.

The world now beginning to emerge is different!

In the era of abundant intelligence, the human advantage won’t lie in processing information faster than a machine. That contest no longer makes any sense. The real human differentiator will be the capacity to imagine new possibilities, integrate diverse fields of knowledge, interpret complex contexts, and build meaning inside dynamic social systems.

It will be the ability to connect seemingly distant disciplines, it will be the capacity to understand complex systems, it will be the talent for creating cultural narratives that steer technological development, and it will be, above all, the capacity to design institutions that harness technology without losing the deeply human dimension of society.

Our kids won’t grow up in a world where they have to compete against AI. They’ll grow up in a world where they live alongside it — and that simple fact completely changes the rules of the game.

The leaders of the next generation won’t necessarily be the ones who accumulate the most information. They’ll be the ones who learn to ask the right questions in an environment where the technical answers can be generated in seconds. They won’t be the ones who memorize formulas, but the ones who understand how to combine technology, creativity, and ethics to design systems that work at human scale.

Because the real revolution doesn’t happen inside the algorithms. The real revolution happens in the way we choose to educate, organize, and project our society into the future.

That’s why the most important question is no longer whether AI will transform the world. That transformation is already underway — it’s imminent.

The truly decisive question is another one:

Will we be capable of preparing the next generation to lead this transformation with intelligence, imagination, and purpose?

I’ll leave the question open…

“Innovation is rarely about inventing something completely new; it almost always comes from learning to combine what already exists. Machines may multiply intelligence, but only humans can give it direction. That’s why the future won’t belong to those who compete with artificial intelligence, but to those who know how to imagine new combinations to build what doesn’t yet exist.”

DISCLAIMER

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