What does it mean to be human in the age of machines?

In this final chapter of Becoming Ourselves, I close the trilogy by looking at where we stand — on the edge of the Intelligence Age — and ask one final, urgent question: what will it do to us?

When I first started Decoded by Mo, I thought I’d focus on technology — AI, strategy, innovation. But over time, I realized something deeper was happening. We aren’t just using new tools; we’re becoming new kinds of humans. Our physiology, our psychology, our relationships — everything is shifting.
That realization led to this trilogy: a journey through history, myth, and meaning — to understand not only how technology evolved, but how it changed us.

In Part 1: From Survival to Civilization, we traced humanity’s early chapters — the Extractive Age — when we learned to shape the earth, build societies, and master energy. We saw how our need to survive turned into our desire to control, and how each breakthrough carried its own cost.
In Part 2: Machines, Services, and Experiences, we entered the Industrial and Transformative ages. Factories, electricity, data, and platforms reshaped the world. We gained speed and connection, but lost stillness and presence. We built systems that promised freedom yet quietly trained our habits, our attention, and even our sense of self.

And now, in Part 3: The Intelligence Age, we arrive at the present — and perhaps the threshold of something greater, or more dangerous.
AI is not just another invention. It’s the first system we’ve built that thinks, learns, and decides. It’s not a continuation — it’s a rupture.

This episode starts with the haunting Qatsi Trilogy — films that show life out of balance, in transformation, and in conflict. Through those images, we see our own world reflected back: the rhythm of platforms, the liquid lives of gig workers, the invisible megamachines that organize billions of us every day.
But we also see resilience — the quiet, stubborn creativity that refuses to dissolve, even inside the algorithmic flow.

We explore how education is being rewired in the Intelligence Age — moving from memorization to judgment, from endurance to attention fitness, from test scores to meaningful creation.
We look at how teams and leadership are being redefined — not by hierarchy, but by trust, perspective, and shared values. Because even in a world of infinite automation, human connection remains the only true sanctuary.

And finally, we reflect on what all this means.
Each era — Extractive, Transformative, Intelligent — gave us new powers but asked for something in return.
We mastered the land but lost balance.
We mastered the machine but lost presence.
We mastered information but risk losing meaning.

Becoming Ourselves was never about reaching the next age.
It was about remembering who we are — and deciding who we still want to be.
Because the danger of this age isn’t that machines will stop understanding us; it’s that we’ll stop understanding ourselves.

Our responsibility now is not just to innovate, but to remember. To slow down, to connect, to teach the next generations that being human isn’t about knowing everything — it’s about caring deeply.
To hold on to empathy, curiosity, and meaning — before we outsource them, too.

This episode is both a reflection and a beginning.
While working on it, I realized that this question — how we become ourselves again in a world of artificial minds — deserves a deeper space. That’s why I’ve decided to write a book, Becoming Ourselves, continuing this journey into who we are, and who we are becoming.

Because the next era isn’t about machines becoming more like us.
It’s about us remembering how to be more like ourselves.

History isn’t only about kings, wars, or inventions — it’s about how humans transform. How our relationships, art, psychology, and even our bodies change when economies shift.

In this episode of Decoded by Mo, I’ll navigate from machinesto services to experiences — tracing how value creation evolved across the last two centuries, and how resilience kept reinventing itself along the way.

I begin with the Industrial Age, when coal, steam, and steelmultiplied human muscle. Cities like Manchester became “workshops of the world,” filled with factories, smoke, and new opportunities — but also poverty, disease, and child labor. Time itself was rewired: factory bells replaced seasons, railways created standardized time zones, and education trained children for industrial discipline.

Art became both mirror and critique. Dickens, Turner, Shelley, and Blake captured the hopes and horrors of industry, while futurists celebrated machines. War accelerated everything — from tanks and planes to nuclear bombs.Oil emerged as the fuel of modern power, shifting global wealth to the Middle East. Resilience in this era meant enduring disruption, adapting to machines, and surviving mechanized war.

Next, the Service Economy took shape. Once goods were abundant, value shifted to banks, insurance companies, hospitals, schools, and bureaucracies. White-collar work surpassed factory work. WWII logistics and Cold War systems showed that resilience meant organizational scale and institutional strength. Education expanded into universities and MBAs. Cultureindustrialized too: Hollywood, advertising, and pop music turned services into identities. But fragility appeared in overgrown bureaucracies and crises like the Great Depression or the oil shocks.

Then came the Experience Economy. Disneyland in 1955 sold fantasy, Starbucks turned coffee into identity, Apple built theaters of technology.

People no longer measured wealth only by goods or services, but by meaning and memory. Tourism became the largest industry on earth. Pop art, advertising, and music festivals blurred art and commerce. Even geopolitics became theater: the Cold War staged experiences through Olympics, propaganda, and the moon landing.

But experiences proved fragile. They depend on authenticity, trust, and stability. Over-commercialization, overcrowding, or crisis can collapse them overnight. COVID-19 revealed this fragility dramatically — yet resilience emerged digitally through streaming, virtual events, and online communities.

Across these three shifts, one lesson stands out: value keeps moving, and resilience keeps changing shape. Machines gave us productivity but alienation. Services gave us stability but bureaucracy. Experiences gave us meaning but fragility.

Understanding these transformations isn’t about nostalgia — it’s about preparing for what comes next. Because just as goods gave way to services, and services to experiences, we now stand at the threshold of platforms, data, and artificial intelligence.

This episode isn’t just about economics. It’s about how technology, war, art, and culture reshaped who we are. And it’s about how resilience — in body, mind, and society — remains the thread connecting past and future.

History isn’t just a story of kings and battles — it’s the story of how humans learned to create value, survive collapse, and reinvent themselves again and again.

In this episode of Decoded by Mo, we take a long journey through time — from the hunters painting bison on cave walls, to the farmers who built temples from surplus grain, to the merchants who turned oceans into highways of fortune. Along the way, we’ll see how resilience — a word we use sooften today — was always at the heart of human survival.

We begin in the Extractive Age, when survival depended on memory, ritual, and movement. Hunter-gatherers tracked stars, plants, and animals with astonishing knowledge. Their myths — from Anansi the Spider in Africa to Raven Steals the Sun in the Pacific Northwest — carried lessons of resilience throughwit, courage, and storytelling. Cave art and songlines became the first libraries of value.

Then came the Agricultural Age, where planting seeds transformed everything. Farming created surplus, specialization, and inequality. Religion, law, and education became instruments of power: Pharaohs tied divinity to theNile, Babylon carved laws in stone, scribes trained in cuneiform and hieroglyphs, the Maya created humans from maize. Monumental art — from pyramids to ziggurats — stood as symbols of stored value. Resilience shifted from mobility to stability, but also carried fragility: droughts, floods, andinvasions could destroy entire civilizations.

The Mercantile Age followed, where value left the soil and moved to the seas. Spices, silver, and silk remade the world. Nations embraced mercantilism, colonies became engines of extraction, and joint-stock companies like the Dutch East India Company pioneered capitalism. Myths and storiesreflected this new horizon: Sinbad’s voyages, Odysseus’s cunning, Mansa Musa’s pilgrimage with so much gold it disrupted economies. Coffeehouses became the“internet of the Enlightenment,” where information itself became value. Resilience scaled globally — nations rose by adapting to networks of trade, and fell whenthey failed.

Across these ages, one truth emerges: value is never static. It moves, transforms, collapses, and rebuilds. And resilience is always the deciding factor — whether through mobility, storage, fleets, or finance.

Why does this history matter now? Because the challenges we face today ; climate risk, inequality, technological disruption — are echoes of the past.Ancient myths and systems remind us that resilience isn’t just about surviving shocks, but about creating meaning, building systems of cooperation, and adapting faster than collapse.

This isn’t just economic history. It’s a story of art, education,religion, geography, and human imagination. It’s about how we got here — and what lessons we might need again as we enter the age of AI.

We often ask the wrong question about AI: “Will it take my job?” But behind that fear is something deeper — we’ve allowed our jobs to define us. When a role disappears, we’re left wondering: then who am I?

In this final episode of the trilogy, I bring together the voices of leaders and thinkers — Demis Hassabis, Jensen Huang, and Sam Altman — and reflect on what their insights mean for us.

AI isn’t replacing people. It’s replacing processes — writing, learning, connecting, sharing. But being human has never been just about process. It’s about meaning, intention, growth, love, and connection. That’s the part no machine can touch.

My invitation is simple, not easy:

• Learn AI. Use AI. Experiment with it. But don’t become dependent on it.
• Never outsource your brain, your muscles, or your spirit.
• Improve yourself — not because AI is chasing you, but because growth is what makes life meaningful.
• Stay connected. Build friendships, community, and love. Algorithms can predict emotions, but they can’t give you connection.
• And above all, be spiritual — whatever that means to you. Ask yourself not what do I do? but who do I want to become?

We blame past generations for war, plastics, and even bad fashion and hairstyles. Future generations will judge us too. What will we leave them?

This is the time to reinvent ourselves. And the good news is — we’re still in time.

The jobs of tomorrow won’t belong to the strongest, the smartest, or even the most technical. They’ll belong to those who never stop improving, who never stop connecting, who never stop being human.

That was Decoded by Mo. Thank you for listening. Stay curious — and peace be with you.

Will AI take my job? In Part 2 of this three-part series, we explore two sharply different answers: one of bold optimism, and one of quiet caution.

In Part 1 — Minds vs. Muscle — we heard Mo Gawdat’s uncompromising view:AI will take all jobs, and the belief in “new jobs” is, in his words, “100% crap.” I also shared a Gen Z perspective from my niece, who believes AI should be banned in schools because it’s already making students lazy. Both pointed to the same risk: if we stop using our minds, we risk losing them.

Now, in Part 2 — Abundance or Anxiety? — two new voices take their place at our imaginary dinner table:

Together, Diamandis and Hinton represent two poles of the debate: one whosees AI unlocking abundance, the other who sees it slipping beyond control.

I add my own reflection: while AI will surely reshape jobs, we still have the choice to keep improving ourselves — to learn, to create, to connect. The real danger isn’t AI replacing us. It’s us replacing ourselves by giving up.

This episode is about more than jobs. It’s about balance — the hope of abundance, the fear of collapse, and the question of how we stay human when machines grow smarter every day.

In Part 3 — Builders in the Arena — we’ll turn to Demis Hassabis, Jensen Huang, and Sam Altman: the people shaping AI in real time. But for now, take your seat at Part 2 of the dinner, where optimism and caution collide.

Will AI take your job? It’s one of the most urgent questions of our time — and it touches all of us, no matter our industry, age, or experience. In this special three-part series of Decoded by Mo, I bring the opinions of some of the most influential voices in AI to an imaginery dinner table to debate what happens to work, identity, and humanity in the age of machines.

In Part 1 — Minds vs. Muscle, we explore why this moment feels different from past technological revolutions. From the wheel and the printing press, to the Luddites and the internet, every wave of innovation has disrupted jobs — but it always replaced muscle. AI, for the first time, replaces mind. That changes everything.

I reflect on how our bodies and brains are designed to survive — pruning away what we don’t use. Just like muscles weaken and languages fade when left idle, we risk losing the very muscles of our minds if we outsource too much to AI.

At the dinner table sits Mo Gawdat, the former Chief Business Officer of Google X, who doesn’t hedge his words: AI will take all jobs. Not some, not most — all. He describes AI as a child raised by the internet, learning at hyperspeed from both the best and worst of humanity. He calls the belief in “new jobs” a comforting myth — “100% crap,” in his words. Even CEOs, he argues, may not be safe.

And yet, Mo also points to what will remain: the deeply human. Care, empathy, love, presence. He ties the future of jobs to a deeper question of meaning, consumption, and spirituality: if machines do all the work, what will define us then?

I also bring in a fresh perspective from Gen Z — my niece — who believes AI should be banned in schools because it’s making students lazy. Her refusal to use AI for assignments contrasts with classmates who embrace it to free time for sports, friends, and hobbies. Both views raise the same concern: if we stop using our minds, we risk losing them.

This episode is about fear, warnings, and the possibility that jobs as we know them may end. In the next part, we’ll hear the opposite view — Peter Diamandis’ optimism that AI will create abundance, not collapse — alongside the cautious skepticism of Geoffrey Hinton, the godfather of AI.

So join me at the dinner table. The bread is broken, the music is playing, and the debate begins.

In Episode 1, we explored the big picture of AI and national sovereignty. But before moving forward, I wanted to hit pause and create something different: a pit stop episode, a crash course, a decoder. Welcome to Episode 1.5 — AI 101, explained without the jargon.

In this episode, I take you on a guided journey through the essentials of AI — how it works, why it matters, and what you really need to know to have smart conversations about it. Whether you’re completely new to the field or already working with AI tools, you’ll find this episode both accessible and insightful.

We’ll rewind the tape back to the origins of AI in 1956, through ELIZA the therapist chatbot of the 1960s, to the “AI winter” of the 1980s, the deep learning breakthrough of 2012, and the explosive rise of GPT models. We’ll explore how AI evolved from niche research into the technology wave that’s reshaping business, politics, and society.

From there, we decode the acronyms and buzzwords:

• LLMs (Large Language Models): autocomplete on rocket fuel.

• SLMs (Small Language Models): efficient pocket-sized cousins.

• Platforms vs. Models: the brain versus the body.

• Parameters & Tokens: what’s inside the AI “brain” and how memory works.

• Foundation Models: the raw clay that gets shaped into doctors, lawyers, and poets.

• Training vs. Inference vs. Fine-Tuning: how models learn and specialize.

  We’ll then dive into the hardware story — what GPUs actually are, why they’re essential for AI, and why they’ve become the new oil of the digital age. You’ll hear why Nvidia’s H100 chips cost more than cars, why entire nations are scrambling to secure them, and why supply bottlenecks make GPUs the most strategic commodity on the planet.

  And because AI is moving beyond words, we’ll explore multimodal AI — models that don’t just process text, but also images, audio, and video. Imagine talking to an AI that can see your world, hear your voice, and respond in real time — that’s where we’re headed.

  By the end of this episode, you won’t just recognize the buzzwords — you’ll own them. You’ll understand what they mean, how they connect, and why they matter. You’ll be able to spot hype from reality, and you’ll be able to explain AI concepts to anyone — whether it’s your boss, your team, or your family at dinner.

  This is AI decoded — made simple, engaging, and practical.

  So, if you’ve ever thought: “I wish someone could explain AI to me in plain English, without dumbing it down, but also without drowning me in jargon”… that’s exactly what Episode 1.5 delivers.

AI is transforming industries faster than any technology in history — but who really controls it? In this first episode of Decoded by Mo, we go beyond the headlines to explore why the winners in the AI era will be those who control the “rails”: the data, the compute power, and the platforms.

We look back at history — from the printing press to the internet — to see the patterns of change, dive into real-world AI success stories, and break down why countries are racing to build sovereign AI for security, economic strength, cultural preservation, and control over the narrative.

From political suspensions like Grok’s on X, to the coming wave of AI agents and job shifts, this episode sets the stage for the series: no jargon, no fluff, just practical insights for anyone curious about AI or leading with it.