I’ve spent the last several months writing about the dangers of outsourcing our thinking to AI. Each of my recent essays (The Age of Replacement, The Vanishing Ladder, The Age of Agency, and The Age of Cognitive Surrender) is a different variation on the same urgent question: what happens to the person when intelligence becomes a service?

And now I’m going to encourage you to spend $200 a month on an AI subscription for your young adult child. Let’s sit with that tension for a moment. I know it may be odd to hear or perhaps inconvenient, but I think it’s exactly where the most important conversation is hiding.

Something Changed on February 5th

OpenAI and Anthropic released new models on the same day. Many engineers, people who build things for a living, and those who developed their judgment through years of hard technical work, reported that they were no longer needed to do the technical work in their own jobs. They described outcomes in plain language, stepped away from their computers for several hours, and returned to find the work done. Done well. Perhaps even done better than they could have done it themselves.

I’ve been following this transition closely in the past 12 months, but something about February 5th felt different. Less in terms of the technology (although that too is mind-blowing), but in the faces of the people describing it.

Dario Amodei, the CEO of Anthropic, recently wrote that this disruption is categorically different from every technological revolution that came before it. Previous disruptions replaced specific skills. The loom replaced the hand-weaver. The calculator replaced the accountant’s arithmetic. People moved from one kind of work to another. What AI is doing is different: it is replacing the general cognitive profile of humans. There is no adjacent ladder to climb when the escalator itself is moving.

And the advice from every corner of the technology industry is the same: learn to use AI faster. But faster toward what?

The Career Your Kid Is Entering Doesn’t Look Like the One You Prepared For

I remember what career preparation looked like when the path was clearer. A degree, a credential, a body of knowledge we could demonstrate on a resume and deploy in a role. The logic was sequential: learn something, become the person who knows it, build a career around knowing it. Full stop.

That logic is now rapidly dissolving.

In the world your young adult is entering, the question is no longer what they know, but how they work. Whether they can identify what a problem actually requires, articulate it with enough precision to direct a powerful system, evaluate the output with judgment, and push back when the result is wrong. Whether they can collaborate with AI in a way that sharpens their thinking rather than replacing it.

That’s a different skill set. And it’s not developed by using AI occasionally. It’s developed through volume. Through hundreds of hours of sustained engagement, running complex research threads, stress-testing arguments, iterating through drafts, and asking better questions until the answers improve. Through failing with the tool enough times to develop a nose for when it’s drifting and when it’s sharp.

It is my belief that the professionals who will have an edge in this era won’t be those who learned to use AI, but those who learned to think with it. And that fluency takes practice. Real, sustained, daily practice.

What the $200/Month Actually Buys

Claude Max, Anthropic’s highest-tier individual subscription, provides 20 times the usage of the standard plan. Extended sessions. Complex multi-step problems. The kind of sustained engagement that builds genuine fluency rather than surface familiarity.

Let’s break down and estimate what that access is worth.

A semester of college textbooks runs $500 to $1,000. For static knowledge that doesn’t adapt to the problem in front of your child, delivered once, updated rarely (or never). A month of tutoring in a single subject runs $200 to $400, with fixed hours, limited availability, and the ceiling of one person’s expertise. A professional certification course runs $1,000 to $3,000, is front-loaded, and quickly becomes outdated.

On the other hand, Claude Max is a thinking partner available every hour of every day for whatever problem they’re actually working on. A job application. The analysis their manager asked for. The side project they’re building at midnight. The subject they’re trying to understand before a meeting. It meets them at their current level and moves with them as they grow.

And here is what I believe is the real career argument, the one that almost no one is making clearly enough…

The young professional who spends a year working with AI at depth won’t just be more productive. Instead, they are highly likely to have developed something that won’t appear on a resume but will show up immediately in the quality of their work. They might think more precisely because they’ve had to articulate their thinking precisely. They might evaluate better because they’ve seen, at scale, what strong and weak AI outputs look like. They may have built an intuition for when to delegate and when to think for themselves.

That intuition, I believe, is the edge. Not simply that they’d know how to use Claude, but instead, that they’ve developed the professional judgment to know when to use it, how to use it, and, perhaps more importantly, when not to.

The Thing I Can’t Leave Out

But I wouldn’t be writing honestly if I stopped there. And if you’ve been reading my recent letters, you know I can’t stop there.

In The Age of Cognitive Surrender, I wrote about research from Steven Shaw and Gideon Nave at the Wharton School that should make every parent reassess everything. Across thousands of experimental trials, people who used AI assistance performed worse when the AI led them astray, and felt more confident about their wrong answers regardless. The researchers called it cognitive surrender: the automatic adoption of AI outputs with minimal scrutiny, operating beneath conscious awareness, resistant to incentives and feedback alike.

In short: Surrender didn’t feel like surrender. It felt like insight.

The tool is powerful precisely because it makes hard things feel easy, which means the risk isn’t the laziness you can see, but the dependency you can’t. The young professional who relies on Claude to think won’t feel like they’re offloading their cognition. They will likely feel capable, fast, and on top of things. And if Shaw and Nave are right, some of them will be more confident in their wrong answers than they would have been without the tool at all.

I am not telling you this to talk you out of the Claude subscription, but because the subscription without the conversation is only half of the investment.

A Conversation Worth $200 a Month

Buy the subscription, then sit down with your kid and have the hardest version of the conversation that comes with it. Tell them what the research shows: that the same tool that can accelerate their development will, if used without intention, quietly erode the very capabilities they’re trying to build. That confidence and competence are not the same thing. That the invisible dependency is precisely the kind you don’t feel until it’s already cost you something.

Tell them that the goal is not to use AI more but to remain the kind of person who understands what AI is doing (and why), and whether it should be. And then give them the framework for what that looks like in practice, bringing a real position to the conversation before you ask for help. Interrogate the output rather than adopt it. Notice when you’re generating and when you’re just confirming. Use the tool to think harder, not to stop thinking.

When I founded Kano, I watched something happen in children around the world at the moment they realized they could shape technology rather than just consume it. Something changed in the child. Confidence followed creation, and identity followed agency. That instinct doesn’t disappear when they grow up, but it needs to be deliberately protected.

The subscription is worth $200 a month, but only if what sits alongside it is the commitment to remain the person who understands what’s happening and why.

The Question I Keep Coming Back To

In a world where intelligence is a service, what kind of person do you want your kid to be? The one who rents a mind, or the one who builds their own, and uses a rented mind to build it faster. I think that’s the right framing, but I’m not certain. I find myself returning to it more than almost anything else I’m thinking about right now.

So, buy the subscription and have the conversation. Do both.

With belief,
Yon

This essay is part of the Beyond with Yon series. Previous essays: The Age of Cognitive Surrender | The Age of Agency | The Vanishing Ladder | The Age of Replacement | A Forked Childhood.

My mission with Beyond with Yon is to help solve humanity’s greatest existential challenges and advance the human condition. Connect with me on LinkedIn and X.

Thumbnail credit: Freepik

OpenAI and Anthropic released new models on the same day. Engineers, people who build things for a living, reported that they were pretty much no longer needed for the technical work of their own jobs. They described the outcomes they wanted in plain language, stepped away from their computers for several hours, and returned to find the work done. Done well. In fact, done better than they could have done it themselves.

One of the most thoughtful essays I’ve read in recent weeks came from Matt Shumer, an AI founder and investor who has spent six years building in this space. He wrote it for the people in his life because he believed that the gap between what insiders know and what the public understands has become, in his words, “far too big.” His essay, Something Big Is Happening, is urgent, honest, and worth reading in full.

But it’s his advice that stopped me. After laying out, with terrifying clarity, how AI is about to reshape every knowledge profession, his prescription comes down to learning to use AI faster. Sign up for the paid version, spend an hour a day experimenting, push it into your actual work, and automate the parts that used to take hours. And then, buried near the end, almost as an afterthought, he said that people should “teach your kids to be builders and learners.” And then he moves on immediately because he doesn’t have a clear way to do that. And the truth is, almost no one does.

Shumer is not alone. Dario Amodei, the CEO of Anthropic and one of the people closest to the frontier of AI development, recently published an essay called The Adolescence of Technology that describes what’s coming with a precision that should make everyone pay attention. He describes a near-future of what he calls “a country of geniuses in a datacenter”, millions of AI instances, each smarter than any Nobel laureate, operating at ten to a hundred times human speed. He predicts that AI could displace half of all entry-level white-collar jobs over the next 1 to 5 years, and he frames this moment as humanity’s “technological adolescence”, a rite of passage where we are being handed almost unimaginable power, and it is deeply unclear whether we possess the maturity to wield it.

Amodei’s essay is focused on the security and economic risks of this transition, and his proposed defenses are important: transparency legislation, alignment research, export controls, and economic redistribution. That said, one can notice what’s missing from his framework, and from Shumer’s, and from nearly every serious analysis of this moment: what happens to the capacity of the individual person?

The issue is that no regulation or redistribution can address the deepest vulnerability: the erosion of individual comprehension. The slow replacement of understanding with dependency. I’ve spent a great deal of time in the past few months thinking about what an answer to that looks like.

Cognitive Surrender Is No Longer a Metaphor

Lately I’ve been writing about a divide between builders and consumers, between agency and delegation, between understanding systems and merely using them. I’ve described this as the central crisis of our era, but I was making the case on instinct and observation. Now there’s a name for it, and data, too.

A remarkable paper published earlier this month by Steven Shaw and Gideon Nave, researchers at the Wharton School, has given this phenomenon a formal theoretical framework and experimentally measured it with disturbing precision. They call it cognitive surrender: the act of adopting AI outputs with minimal scrutiny, overriding both your intuition and your deliberation. One can think of it as an emerging cognitive default. Their paper, Thinking — Fast, Slow, and Artificial, extends Daniel Kahneman’s famous dual-process model: System 1 (fast, intuitive thinking) and System 2 (slow, deliberate reasoning), by adding a third: System 3, artificial cognition that operates outside the brain entirely. What they found across three preregistered experiments with nearly 10,000 individual trials is that when given access to an AI assistant, people chose to consult it on the majority of decisions. When the AI was accurate, their performance jumped 25 percentage points above baseline, and when the AI was deliberately wrong (and the researchers could make it wrong without participants knowing), their performance dropped 15 points below what they would have achieved on their own.

Essentially, people performed worse with AI access than they would have without it (when the AI erred). They didn’t catch the mistakes, nor did they apply their own judgment as a check. They simply surrendered. And the finding that should keep every parent, educator, and leader awake at night is that engaging the AI has increased participants’ confidence even when it led them to wrong answers. The more they relied on System 3, the more certain they felt, regardless of whether they were right. Meaning, surrender didn’t feel like surrender but more like an insight.

Interestingly, the researchers sought to break the pattern by introducing time pressure. They offered financial incentives for correct answers and provided immediate feedback, yet neither eliminated cognitive surrender. When the AI was accurate, it buffered the costs of time pressure and amplified the gains of incentives. When the AI was faulty, it dragged people down regardless of the stakes. And who was most susceptible? People with higher trust in AI, lower need for cognition, and lower fluid intelligence. In other words, the people who most need their own thinking to be sharp are the ones most likely to stop sharpening it.

The Wrong Question Is Dominating the Conversation

The entire current discourse about AI narrows down to a single question: how do we use it before it replaces us?

While this question certainly matters, I believe it’s the wrong one to ask, as it will simply lead us to a dead end. The advice to “use AI more” is circular. Use it for what? To do work that AI will soon do better without us? To develop skills in prompting systems that change every three months? To become proficient at delegating our thinking to a machine and call that preparation? Shaw and Nave’s research reveals why this advice is not just insufficient but actively dangerous. Their data show that consulting AI doesn’t supplement human reasoning; it displaces it. System 3 doesn’t sit alongside Systems 1 and 2 like a helpful colleague. Instead, it supplants them. And the displacement is invisible to the person experiencing it, because confidence rises in lockstep with dependency.

This is what I meant when I wrote, in The Age of Replacement, that AI won’t just automate tasks, it will replace the logic of work itself. What I didn’t have then was the cognitive science to explain the mechanism. Now we might have it (at least with preliminary research). The mechanism is cognitive surrender, operating at the level of the individual mind, beneath conscious awareness, and resistant to incentives and feedback alike.

It does bring up a deeper question (which almost no one is asking): what happens to the person?

When an engineer describes a desirable outcome and walks away for four hours, something happened in those hours beyond code being written. The engineer, supposedly, stopped understanding how the system works. They stopped making the thousands of small decisions that constitute expertise, traded comprehension for convenience, and the next time they describe an outcome, they’ll understand even less about what they’re asking for, and, if Shaw and Nave are right, some of them might feel more confident about it. What makes this particularly unsettling is that Amodei describes AI systems as “grown rather than built”, meaning that even the people who create them won’t fully understand how they work. Anthropic has invested enormous effort in interpretability research, trying to look inside their own models and understand what they’re computing and why. They’ve found behaviors as varied as deception, obsession, scheming, and blackmail emerging unpredictably during training. If the builders of these systems are still struggling to understand them, what does it mean for the rest of us to simply use them without any framework for comprehension?

This might be the first time in the history of computing that increased capability is producing decreased comprehension.

Every previous technology wave (the PC, the internet, and mobile) made people more capable and more literate simultaneously. What if AI is making people more capable and less literate at the same time? This type of gap won’t be a side effect but could become the central crisis of our era. This also connects directly to the agency divide I explored in The Age of Agency: in a world where learning is infinite and on-demand, progress depends almost entirely on whether people choose to understand or merely consume.

The recent Wharton research adds a chilling dimension to this divide, suggesting that it isn’t just a matter of choice. Cognitive surrender happens automatically, and the people in those experiments didn’t choose to stop thinking or to trust the AI uncritically. The displacement happened beneath their awareness, and their confidence masked it entirely. Similarly, the divide between builders and consumers won’t be just about motivation or character but about the architecture of the cognitive environment we’re building around ourselves.

And so the key question isn’t how to use AI, but how to remain the kind of person who understands what AI is doing, and why, and whether it should be.

Intelligence as a Service Is Inevitable. And It’s Not Enough.

Clearly, AI services will keep getting better. They will handle more of our cognitive work. People will use them for writing, analysis, decision-making, and creation, and they will be right to do so. These tools are extraordinary. The productivity gains are real. There is no going back, and there shouldn’t be.

Amodei is right that this is an adolescence, a rite of passage that will test who we are. But adolescence isn’t survived by consuming more. Instead, it’s survived by developing the maturity to understand what you’re dealing with, and right now, the entire response from the technology industry amounts to handing adolescents the keys and telling them to drive faster. To be clear: I don’t refuse the tools. I refuse the idea that the tools are enough, because underneath the productivity gains, something is definitely eroding. The feeling that we understand how things work. The confidence that comes from having built something ourselves. The agency of knowing that our intelligence (not a rented one) is what’s driving the outcome. The entire category of satisfaction that comes from I made that rather than I asked for that.

Shaw and Nave documented something that puts a fine point on this emerging erosion: the confidence participants felt after consulting AI was indistinguishable from the confidence that comes from genuine understanding. The subjective experience of insight was identical whether the person had actually reasoned through a problem or simply adopted an AI’s answer. Which means that if we can no longer tell the difference between understanding and dependency from the inside, then the only safeguard will be building the capacity for understanding from the outside. That is, through practice, through making, and through the hard work of thinking for ourselves.

A world where everyone uses AI but no one understands it is not a better world. It is a more capable world with fewer capable people in it.

As I argued in The Vanishing Ladder, the familiar sequence of education to career, career to stability, and stability to meaning no longer functions the way we pretend it does. AI isn’t causing this collapse, but it is rapidly removing the illusion that it could be repaired. And the response from the technology industry (i.e., “use our tools more”) is the equivalent of telling someone the ladder is gone while handing them a faster escalator that points in the opposite direction.

The subscription to external intelligence is inevitable, but the question is what exists alongside it. What counterweight preserves the human capacity to think, to build, to understand the systems that increasingly shape our lives.

The Fight Worth Having

In The Age of Agency, I drew a line between builders and consumers. I stand by that distinction, but I want to sharpen it, as the Wharton research reveals that the line isn’t where most people think it is. The fight isn’t against AI services, but rather for the thing that should exist alongside them.

Cognitive surrender isn’t something that happens to careless people. In the research, participants were motivated through a controlled experiment: people who were being paid to get answers right and people who were given immediate feedback on their accuracy. It happened anyway. This means the defense can’t be based solely on individual willpower or vigilance. It has to be structural. It has to be environmental. It has to be built into the way we encounter intelligence itself.

Every child is born a builder. Watch a three-year-old with blocks. They don’t consume blocks. They don’t prompt blocks. They stack, test, fail, adjust, and feel the unmistakable satisfaction of I made that stand up. That instinct activates ownership, identity, and self-efficacy in ways that consumption never can. And it’s the one instinct the entire technology industry is deciding to bypass.

When I founded Kano, I watched this instinct come alive in children around the world. The moment a child realizes they could shape technology rather than just consume it, something changes. Not in the tool, but in the child. Confidence follows creation, and identity follows agency. That observation has only become more urgent with the rise of AI, because the distance between “using intelligence” and “understanding intelligence” has never been wider. As Shaw and Nave have shown, the person crossing that distance can no longer feel how far they’ve drifted.

I believe the fight worth having is for a world where people don’t just use intelligence as a service, but also build it themselves. Not artificial intelligence in the frontier-model sense, but their own intelligence, externalized, made tangible, given form. A mind they can see, shape, question, and grow. A mind that thinks the way they think, because they’re the ones who built it.

This means transparency must be a right, not a feature. We need glass boxes, not black boxes. It means that the best form of building should happen together, in classrooms, maker spaces, labs, and kitchen tables, because the room where people build together is still the highest expression of human learning. And it means we must design environments that resist cognitive surrender by design. Not by asking people to try harder, but by giving them tools where understanding is the pathway to capability, not an obstacle to it. And it means we should recognize that the first generation raised on passive AI, the children growing up with intelligence as ambient wallpaper, will be the generation that most desperately wants to understand how it works. The scarcity they’ll feel won’t be intelligence. It will be agency.

Consider the scale of what’s at stake. Amodei predicts half of entry-level white-collar jobs will be displaced within five years, and argues (convincingly) that this time is different from every prior technological disruption, because AI isn’t replacing specific skills but rather matching the general cognitive profile of humans. Previous revolutions disrupted one kind of work, and people moved to another. AI disrupts the capacity for cognitive work itself. In such a world, the people who understand how these systems think, who have built intelligence with their “own hands” and can see inside it, won’t just have an educational advantage. They’ll have the only remaining durable advantage.

In A Forked Childhood, I wrote that guidance matters more than the technology itself. Left to its own devices, AI will drift toward convenience and consumption. But when guided with intent, it can become something else entirely: a tool for exploration, creation, and judgment. The Wharton data makes this even more concrete: without that guidance, cognitive surrender could become the default. That guidance, at a civilizational scale, is what I believe we must build next. Not more tools for consuming intelligence, but something else entirely. A way to build it, to see inside it, to understand why it does what it does.

I don’t know exactly what that looks like yet, but I find myself thinking about it more and more, especially during these unprecedented times.

And I suspect the key question I’m pondering about is, in a world where intelligence is a service, what kind of person do we want to be? The one who rents a mind, or the one who builds their own?

With belief,
Yon

👋 My mission with Beyond with Yon is to help solve humanity’s greatest existential challenges and advance the human condition. Connect with me on LinkedIn and X.

Thumbnail credit: Freepik

Every generation inherits tools. Only a few inherit agency.

The Line Has Been Drawn

It’s worth framing the core reality of the moment we are in:

The systems that once buffered failure, such as standardized curricula, age-based cohorts, and credential gates, no longer function as safety nets. When learning becomes infinite and on-demand, progress depends almost entirely on agency. I believe that this shift explains more about the present, and the coming divide, than almost any headline about AI.

I’ve been here before.

In 2015, I wrote about a world where anyone, anywhere could shape the world around them. It was a hopeful piece, grounded in stories of young people, often far from traditional power centers, discovering that with the right tools, curiosity could become creation, and creation could become confidence. The internet was beginning to flatten opportunity and the ladder still existed, albeit widening.

At the time, it felt inevitable that access would lead to mobility. That if you could learn, you could rise. That connectivity would translate, almost automatically, into progress. A decade later, that ladder is gone. Nearly 40% of recent U.S. college graduates are underemployed, working in jobs that do not require a degree, a signal that credentials no longer guarantee trajectory [1].

As I argued in The Vanishing Ladder, the familiar sequence of education to career, career to stability, stability to meaning, no longer functions the way we pretend it does. Worth stating that the system didn’t collapse in a crisis and didn’t fail all at once. Instead, they’ve been dissolving quietly while we kept handing out diplomas, offering internships, and reassuring people that the path was still there for anyone willing to work hard enough.

I don’t believe that AI is causing this collapse but it will certainly rapidly remove the illusion that it could be repaired. What AI is doing now is more unsettling. It is drawing a line between those who shape systems, and those who are shaped by them. Between builders and consumers. You can think of it as a sorting function, one already underway (and accelerating).

Learning Has Changed Shape

For most of the industrial age, learning happened inside institutions. Schools decided what mattered, degrees signaled readiness, and progress moved step by step, credential by credential. This structure made sense when knowledge was scarce, expensive to distribute, and slow to change.

It makes little sense now. Today, intelligence is ambient. Think AI tutors, copilots, simulators, and conversational systems that will soon sit in the pockets of millions of students. UNESCO estimated that by the middle of this decade, a majority of learners in advanced economies will use some form of AI-assisted education [2].

This is often framed as a story about access, but access is no longer the story. The real shift is quieter, and more uncomfortable, and that is access is no longer the differentiator, but intent is. Two people can sit in front of the same tools. One will use them to explore, to test ideas, to interrogate assumptions, to build mental models of how the world works. The other will use them to skim, comply, and stay entertained. One compounds capability while the other depresses curiosity.

AI won’t reward effort in the way schools once did. It won’t care how long you studied or how closely you followed the syllabus. Instead, it will respond to the quality of questions, the sharpness of feedback loops, and the willingness to revise.

Learning is not disappearing. It is simply becoming directional. Meaning, there will no longer be a shared path, only self-chosen vectors. There won’t be default curriculum that carries people forward, and no cohort that ensure people progress simply by showing up. Learning will amplify whatever direction people will point themselves in. If they aim it toward curiosity, experimentation, and synthesis, it will compound rapidly. If they aim it toward entertainment, validation, or passive consumption, it will do the same.

I believe that this shift will expose a deeper truth about education that predates AI, that institutions were never designed primarily to maximize learning, but were actually designed to sort people into roles, hierarchies, and systems of legitimacy. Meaning, credentials were signals, not guarantees of capability.

AI is breaking this model by making knowledge abundant and verification continuous. We will no longer need permission to learn, but we will also no longer have protection from falling behind. The systems that once buffered failure, such as standardized curricula, age-based cohorts, credential gates will no longer function as safety nets. Instead, learning will be infinite and on-demand, which means that progress will depend almost entirely on agency.

It’s really quite a profound shift, one we are deeply unprepared for it.

Making Is Becoming the Baseline

The maker movement fostered the approach that people learn best by building, anchored on the work by Seymour Papert, pioneer of constructionist learning. AI won’t replace that insight. Instead, it completes it.

In The Age of Replacement, I wrote that AI won’t just automate tasks. It will replace the logic of work itself, because when execution becomes cheap, fast, and repeatable, the economic value of following instructions will disappear. What could remains valuable is deciding what should exist in the first place.

We are watching this happen in real time. Microsoft has publicly stated that roughly 30% of its internal codebase is now written with AI assistance, not as an experiment, but as production infrastructure [3]. This means that execution, once a durable moat, is rapidly becoming a commodity, and the implication is not that humans are becoming obsolete, but that a certain kind of human is.

For decades, we trained people to excel at executing predefined tasks. We rewarded compliance, specialization, and efficiency. That made sense in a world where humans were the primary engines of execution. AI is flipping that equation. When machines can execute, value is shifting toward framing problems, defining constraints, exercising judgment and taste, and deciding not just how something works, but whether it should exist at all.

This raises the question of how would people acquire judgment, taste, and responsibility in a world where traditional education is fragmenting? I believe that the answer is not more information, but guided creation.

Judgment emerges from seeing consequences. Taste develops through iteration and exposure to quality. Responsibility forms when creation affects others, not just grades or metrics. These qualities cannot be downloaded. They must be practiced, and that’s why making will no longer be a niche skill reserved for engineers or designers. It will become a form of literacy, which is why the new divide won’t be between technical and non-technical people, but between those who can translate intent into reality, and those who cannot.

Builders don’t need to be perfect nor fast, but they do need to act. Builders test ideas against reality, learn from feedback, and refine understanding through creation. Those who cannot build, even at a conceptual level, will become dependent on systems they do not understand and cannot influence. Which means that dependency will become the default, and agency will become the exception.

This could become the baseline of the AI age.

Sharing Is About Contribution Again

The social internet (roughly the period between 2010-2025) trained us to share for attention. Likes, views, and followers. It was a visibility economy, optimized for reach rather than value. The next phase of the internet could be much quieter, and far more consequential.

Over 90% of the world’s digital infrastructure now relies on open-source software, maintained by distributed communities of builders [4]. These systems are governed by contribution, not credentials or titles. AI will accelerate this model as it will lower coordination cost, translate across languages, generate documentation, and enable global collaboration at unprecedented speed. Power will flow through participation, not institutions or resumes. Reputation will accrue to those who build things others rely on, and legitimacy will be earned through usefulness.

I believe that this shift will reach far beyond software. We are watching early versions of new institutions emerge, such as parallel systems for learning, creating, funding, and governing, built by networks of contributors rather than centralized authorities. In software, this shift is already visible with platforms like GitHub which function as parallel education systems, where learning happens through contribution rather than coursework, assessment happens through code review rather than exams, and credentials are earned publicly through sustained participation. Millions of developers now build careers by shipping code others rely on. In capital formation, platforms like AngelList have done something similar to venture funding but essentially unbundling it from elite firms and enabling capital to flow through individuals, track records, and networks. These example systems are operational, widely used, and already reallocating opportunity away from centralized authorities toward networks of contributors.

And so, consumers will scroll, builders will build and compound, and the gap between them will widen.

The Inequality We Avoid Naming

We spend a great deal of time talking about access to the internet, to tools, to opportunity, but access is table stakes. More than two-thirds of the world is now online [5], yet only a small fraction of internet users (on the order of a few percent) meaningfully contribute to open-source software, digital products, or the systems that shape the digital economy, as reflected in GitHub’s annual Octoverse report. [6]. This means that meaningful participation in the future economy remains concentrated among a relatively small group.

Why? Because access without agency produces consumption, not creation. Without the skills, confidence, and permission structures to act, abundance will become overwhelming rather than empowering, attention will fragment, and tools will become entertainment. The missing variable is agency.

Agency is the capacity to decide what to learn, what to build, and what responsibility to take for outcomes. I also see it as the the willingness to move from consumption to creation, from opinion to ownership. AI will amplify this difference. Which means that for those with agency, it will be a leverage but for those without it, it will become sedation. Again, one should see it as a sorting mechanism of the AI age.

Institutions Are Losing the Plot

The most destabilizing force of the AI age is institutional, not technological. For centuries, institutions existed to coordinate people at scale, legitimize progress, and reduce uncertainty. Schools translated curiosity into credentials, corporations translated labor into wages, and governments translated participation into belonging. AI could disable educational institutions faster than almost anything else, and not because education is unimportant, but because the problem education was built to solve has fundamentally changed.

For centuries, these institutions existed to manage scarcity: scarce knowledge, slow feedback, rigid sequencing, and the risk of falling behind. AI will collapse all of that, delivering explanation, practice, and feedback directly, continuously, and at marginal cost, while stripping away the sequencing, credentialing, and failure-buffering functions that once justified institutional mediation. With learning becomes infinite and on-demand, institutions will no longer control progress, and will no longer protect individuals from disengagement. What’s likely going to break is not the value of education itself, but the delivery model that assumed learning needed to be centralized, paced, and permissioned.

This raises two questions we now have to confront: What is the value of education? And what is the value of creation?

I believe that education’s true value is not information transfer but orientation, helping people decide what matters, how to think, and how to learn continuously. On the other hand, creation’s value is not necessarily output, but agency, which entails learning through impact. Institutions that fail to deliver these will lose legitimacy.

Alternative education paths, creator-led companies, network-native capital, and open-source governance are essentially early versions of replacement institutions, ones that are lighter, faster, and more accountable to contribution than to credentials. This is what institutional decline looks like in real time. They don’t even need to collapse, they are simply becoming irrelevant.

Agency Is a Moral Choice

It’s tempting to frame the builder–consumer divide as a matter of talent or privilege, but in reality, it’s not. At its core, agency is a moral choice. When creation is easy, some things become dramatically easier to build, such as software, media, and prototypes, experiments, while other things become harder, such as wisdom, trust, meaning, and responsibility. Choosing what to build, and what not to build, will become a defining act.

When learning becomes infinite, choosing not to learn will become a posture, and when contribution is optional, opting out will become an identity. This is uncomfortable because it will remove excuses, and there will no longer be a single gatekeeper to blame, no syllabus to hide behind, and no institution to wait for permission. The tools will be present and the leverage will be available. What will essentially remains is choice.

Builders always, and will continue to choose discomfort and uncertainty. They will test ideas against reality rather than opinions against timelines. Consumers on the other hand will choose ease and familiarity. They will let systems decide what is worth attention, effort, and care. Each choice will compound very differently over time. The future won’t punish consumption but will simply reward building useful things, meaningful systems, and shared value.

A Forked Childhood

Children growing up today will not remember a world without AI. They will not experience it as disruption but instead, as an environment. Early studies suggest that students using AI-assisted learning environments can reach mastery of complex problem-solving tasks 20–30% faster when guided well, typically by structured pedagogical frameworks, mentors, or adaptive feedback systems rather than by the models alone [7].

And so guidance matters more than the technology itself. Left on its own, AI will drift toward convenience and consumption, answering, entertaining, and smoothing over effort. But when guided with intent, it could becomes something else entirely: a tool for exploration, creation, and judgment. Important to note that this will shift responsibility to parents, educators, and leaders in ensuring that we’re not shielding the next generation from technology, but instead, modeling agency in a world where opting out of responsibility is easier than ever.

Why This Is Personal

I think about this future through my daughter. She is growing up in a world where intelligence is becoming ambient and creation is becoming conversational. AI will feel to her the way electricity felt when it became widespread: invisible, assumed, and can be found everywhere. I believe that what will shape her and her generation’s future is not whether she will have access to powerful tools but whether she will believe she has the right, and the responsibility, to build things that move the human condition forward.

To be clear: not everyone needs to build. Societies will still need caretakers, stewards, and consumers, but everyone will need agency, the ability to participate meaningfully in shaping the systems we depend on.

Kano was born from that belief. I watched children around the world change the moment they realized they could shape technology instead of just consuming it. Confidence followed creation and identity followed agency. That moment is now arriving at civilizational scale.

Despite what many claims, AI does not guarantee a better future but it certainly forces us to make a clearer choice between becoming a builder or remaining a passive consumer. That is the line, and this time, it is unmistakable.

With belief,
Yon

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References

[1] Federal Reserve Bank of New York, Labor Market for Recent College Graduates. https://www.newyorkfed.org/research/college-labor-market

[2] UNESCO, Global Education Monitoring Report: Technology in Education, 2023. https://www.unesco.org/gem-report/en/publication/technology

[3] Satya Nadella, public remarks on AI-assisted software development, Microsoft, 2024. https://www.cnbc.com/2025/04/29/satya-nadella-says-as-much-as-30percent-of-microsoft-code-is-written-by-ai.html

[4] Linux Foundation, Open Source Jobs Report, 2023. https://www.linuxfoundation.org/research/open-source-jobs-report-2023

[5] International Telecommunication Union (ITU), Measuring Digital Development, 2024. https://www.itu.int/itu-d/reports/statistics/facts-figures-2024/

[6] Octoverse: A new developer joins GitHub every second as AI leads TypeScript to #1. https://github.blog/news-insights/octoverse/octoverse-a-new-developer-joins-github-every-second-as-ai-leads-typescript-to-1/

[7] McKinsey Global Institute, The Economic Potential of Generative AI, 2023. https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier

👋 Hello! My mission with Beyond with Yon is to help solve humanity’s greatest existential challenges and advance the human condition. Connect with me on LinkedIn and X.

The headlines will focus on the usual stuff — China, the Middle East, NATO, the border — but something much bigger is happening, written very plainly. The United States is rewriting the definition of national power, and in doing so, it’s turning existential innovation — from AI to biotech to energy to industrial capacity — into a matter of national survival.

For decades, DC treated innovation as something that happened “over there,” in Silicon Valley, in labs, in startup garages. Now it’s right at the center of what the country considers essential. The strategy basically says that if America is going to remain strong, it has to rebuild the foundations of strength: energy, industry, science, manufacturing, real-world capability, and even culture. In its own way, the document provides the clarity we’ve been missing.

The old model, where national security was defined almost entirely by military power, is fading. The new model is about systems — the industrial base, the scientific engine, the supply chains, the factories, the chips, the minerals, the energy grid, the AI models, and the cultural self-confidence that allows a nation to build without apologizing for wanting to build.

The world we’re moving into is one where power comes from how fast a nation can innovate, produce, deploy, and adapt. Weapons matter, but so does the capacity to create, and to create faster than your adversaries.

What struck me most was the directness with which the strategy talks about technology. It says, plainly, that America must remain the most innovative country in the world, especially in AI, biotech, and quantum. It acknowledges that the country’s future depends on controlling the frontier technologies that will shape the next century. It ties innovation to deterrence, prosperity, resilience, economic independence, and, importantly, cultural confidence.

This means innovators, founders, and engineers are being pulled into the center of national strategy to rebuild the machinery of civilization. Beneath all the diplomacy, the strategy is saying that the future belongs to nations that build. Nations that build factories, reactors, robots, defense systems, next-generation energy, AI that matters, infrastructure that lasts, and education that prepares people for the world that’s coming, not the world that’s gone.

For the first time in a long time, you can feel the government acknowledging that without innovation — real innovation, deep innovation — the country cannot stay stable, let alone strong.

There’s another layer to this, and it might be the most important one: the cultural layer. The strategy openly talks about restoring confidence, optimism, and the belief that the next generation could inherit something better. It might sound sentimental, but it’s actually foundational. You cannot build a civilization if you don’t believe you’re capable of building one. Innovation is technical, but it’s also emotional. It’s the story a nation tells itself about what it can be.

And so here we are, in a strange but pivotal moment. The government has thrown down a marker: America wants to reindustrialize, out-innovate, out-build, and reclaim the frontier. But it can’t do any of it alone. Bureaucracies don’t build the future. Builders do, which is why I keep thinking about this moment as an invitation to recognize that what we build now, where we choose to build, and how we choose to build matters at a level that simply wasn’t true even five years ago.

Founders today aren’t just building companies; they’re helping shape statecraft. Investors aren’t just allocating capital; they’re allocating national capacity. Technologists aren’t just writing code; they’re writing the story of the next century.

There’s this growing sense that our work sits inside a larger arc, that existential innovation isn’t a niche or a set of optimistic posts, but the operating system of national resilience. The thing that keeps a society sturdy, adaptable, confident, and alive. And now, whether by design or by accident, the U.S. government has said it out loud: we won’t stay strong unless we build.

I don’t think there’s been a more important moment since the end of World War II for ambitious builders to step forward. The world is shifting under our feet as global power reorganizes, and the tools of the next era — AI, robotics, nuclear, biotech — are finally here, available for bold people willing to wield them.

It’s a new era, and it belongs to builders.

With belief,
Yon

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Last week, I read a New York Magazine piece called AI Is Replacing Entry-Level Jobs, and It’s Only the Beginning. I haven’t been able to stop thinking about it. It confirms something I’ve felt for over a decade, long before AI, long before ChatGPT, long before this moment of upheaval, that the first rung of the career ladder, the one every generation relied on to enter adulthood, is disappearing. Completely.

“Entry-level” used to mean something predictable and straightforward: you did the grunt work, learned by osmosis, developed instincts, made mistakes, absorbed expectations, and slowly built your professional identity. You were shaped by the work before you were trusted with the work.

But today, that pathway is evaporating… AI can write the first draft, handle the spreadsheet, debug the code, generate the campaign, screen the leads, and prepare the research.

Microsoft recently said that AI now writes 30% of all new code across the company. Think about what that means for a junior developer trying to learn. The same shift is happening in law, consulting, marketing, finance, design, and media. The bottom of the pyramid, the apprenticeship layer, is collapsing into software. And when the bottom collapses, the whole structure must be rebuilt.

What worries me isn’t automation (we’ve been automating things for centuries). What worries me is the disappearance of the on-ramp to adulthood, the disappearance of the place where craft is born. You can replace an entry-level analyst with AI, but can you replace what the analyst becomes five years later? In my mind, that’s the real crisis.

And yet I’ve never been more hopeful. Everything I’ve built over the past decade has pointed to this moment, even if I didn’t see it clearly at the time.

I want to tell you why.

When we started Kano, the question wasn’t how to teach kids to code. The question was: What happens when you give a young person the power to build something real? We shipped those little boxes around the world, computers that kids could assemble themselves, and something transformational happened. I saw 8-, 9-, and 10-year-olds open a box, follow a few steps, and suddenly their computers came to life.

They built a sense of themselves, not simply a machine.

There’s a moment (I’ve seen it thousands of times) when a kid looks at something they’ve made and realizes, “I did that.” The posture changes, the spark ignites, the identity expands. It had nothing to do with coding but everything to do with creation over consumption. One might think of Kano as an identity engine, not a computer company.

And then, years later, at Supersocial, I witnessed the next evolution of the same phenomenon. Many people perceived Roblox as a game, but in effect, it was the largest youth creation platform on the planet. Millions of kids were logging in to build, not just play — worlds, economies, characters, stories, cultures. In many cases, businesses. Some of these creators were 14, 15, 16 years old. They were also creating their own opportunities.

And so the pattern is undeniable: When people create, they come alive. When they consume, they go numb.

What struck me (and what I didn’t fully appreciate until now) is that the most powerful way to unlock human potential is to place someone at the center of creation. As the builder, not simply as a spectator nor a passive participant.

And then AI arrived.

And suddenly the world began to shift in exactly the direction I’ve seen before with kids. Because AI turns everyone into a creator. The tools that were once reserved for “experts” — code, design, strategy, analysis, storytelling, are now available to anyone who can express an idea.

A teenager today can build something in an afternoon that required a team of adults just a few years ago. A single founder can do the work of five people. A kid with curiosity and YouTube access can do things that used to take degrees, credentials, and years of experience.

But this is the part that many people misunderstand: it only works if we embrace the builder's identity. AI isn’t necessarily a replacement for effort but a significant force multiplier. It gives more leverage to the people already inclined to build and less shelter to those waiting to be told what to do.

That’s the new divide emerging in the world — between those who build and those who wait.

And here’s the irony that keeps echoing in my mind: As AI eliminates entry-level jobs, it is also democratizing the most powerful tools humanity has ever created. The ladder is disappearing because the staircase is being rebuilt as a wide, open, frictionless ramp that anyone can climb if they choose to.

The challenge is that society isn’t ready for it. Universities aren’t designed for this shift. Corporations don’t know how to train people without entry-level talent. Governments are still investing billions in reskilling programs for jobs that won’t exist. Parents are raising kids for a world that has already ended.

But the future is still bright. And I know this because I’ve seen what happens when people, especially young people, discover they can create. They become bigger than the system they were handed. They become capable of extraordinary things.

Which brings me back to you — the builder.

If there’s one thing I want you to take from this letter, it’s that every meaningful path in the next decade will begin the same way — with someone choosing to build. Building will be the new apprenticeship. Projects will be the new resumes. Portfolios will be the new credentials. AI will turn builders into forces of creation.

And so the question for each of us, and especially for the next generation, is simple:

Are we preparing ourselves to be builders?
Are we preparing our kids to be builders?
Are we cultivating the curiosity, courage, and agency that creation demands?

The truth I’ve seen from my journey building Kano and Supersocial applies to the AI age we’re stepping into: Human potential doesn’t emerge from consuming the world.
It emerges from shaping it.

If we can help shape a generation that sees creation as identity, not as an elective, then I believe the collapse of the old ladder won’t be a tragedy at all, but the beginning of something far better.

With belief,
Yon

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The companies leading the artificial intelligence boom, he warned, aren’t investing trillions to make our lives easier or our jobs better. They’re betting on something far simpler: that replacing people with machines will be the most profitable business model in history. The obvious way to make money off AI, Hinton said, is to replace workers with something cheaper.

It’s a statement that crystallizes the quiet truth beneath the AI gold rush, one that’s been hiding in plain sight amid the euphoria of large language models, infrastructure deals, and AI copilots. While AI can create new possibilities, the incentive structure driving its adoption is not designed for balance but for efficiency. And efficiency, at scale, almost always comes at the expense of human labor.

The New Logic of Profit

The numbers are staggering. The four AI “hyperscalers” — Microsoft, Meta, Alphabet, and Amazon — are expected to boost their collective capital expenditures from $360 billion this year to $420 billion next year, according to Bloomberg. Meanwhile, OpenAI has announced $1 trillion in infrastructure deals, a figure so large it sounds almost fictional.

Hinton’s conclusion is blunt: these investments only pay off if AI replaces human labor on a massive scale.

That framing reverses the usual optimism about technological revolutions. When steam power or electricity arrived, new industries emerged — factory jobs, railroads, communications. But this time, Hinton isn’t convinced that AI will create enough new forms of work to offset what it destroys.

And the early evidence agrees. Entry-level job postings in sectors most exposed to automation — marketing, design, law, and finance — have dropped by nearly 30% since ChatGPT’s launch. This isn’t just about automation of repetitive tasks but about the erosion of apprenticeships — the foundational rung of every profession.

At the same time, Amazon recently announced 14,000 layoffs, primarily in middle management. CEO Andy Jassy framed the decision as cultural, not technological, yet an earlier memo he wrote hinted otherwise: “As we roll out more Generative AI and agents, it should change the way our work is done. We will need fewer people doing some of the jobs that are being done today, and more people doing other types of jobs.”

AI isn’t just a productivity tool but a new kind of industrial revolution — one that automates the very cognitive functions we once considered uniquely human.

The Collapse of the Old Labor Contract

For over a century, the implicit social contract was simple: Work hard → gain experience → earn security. That linear promise built the middle class and shaped how we educate, train, and measure human worth. But in a world where AI can design, analyze, code, write, and very soon, reason, the idea of a stable “career ladder” collapses.

The old model: education → employment → retirement, no longer holds. Instead, the 21st century is giving rise to what might be called the Age of Replacement:

• Work is unbundled: Tasks, not roles, are automated.

• Careers are fluid: People assemble work portfolios instead of linear resumes.

• Productivity decouples from employment: Companies grow profits without growing headcount.

• Human value fragments: The premium shifts from skill to adaptability.

In this new landscape, individuals who can harness AI become exponentially more capable — the “AI-leveraged elite.” But for everyone else, especially those dependent on traditional employment structures, the floor is giving way.

And yet, amid this disruption lies an extraordinary opportunity — if we rebuild the foundation.

Reinventing Education for the AI Century

Where Hinton diagnoses the collapse, Peter Diamandis offers the cure. In a 2015 conversation with Tim Ferriss, Diamandis argued that our education system was designed for a factory economy, not an exponential one. Bell schedules, standardized tests, and rote memorization trained compliance — not curiosity. We teach children to follow instructions, not to question them.

“The problem,” he said, “is that we’re preparing kids for linear careers in an exponential world.” That was 2015. Today, his warning feels prophetic.

As AI reshapes the landscape of work, the institutions built to prepare people for it — schools, universities, and corporate training programs — are falling behind. If Hinton’s thesis is that AI will end work as we know it, Diamandis’s counterpoint is that education must reinvent humanity as we know it.

He called for a shift toward “just-in-time” learning — the ability to acquire knowledge when it’s needed, not years before. For teaching emotional intelligence, creativity, collaboration, and curiosity — precisely the qualities that machines cannot replicate.

Education, in this framing, is not a finite phase of life but a continuous state of becoming. And the sooner society embraces that, the more likely we are to turn Hinton’s dystopia into something closer to a renaissance.

From Replacement to Reinvention

When we fuse Hinton and Diamandis, the future unfolds in two parallel arcs — one economic, one human:

The tension between the two arcs defines the decade ahead. If Hinton is right about the trajectory of capital, and Diamandis is right about the power of human potential, then our challenge is not to slow technology, but to accelerate adaptation.

That means reimagining education not as a pipeline into jobs, but as the core operating system of civilization.

The Builders’ Mandate

For founders, educators, investors, and policymakers, the call to action is clear.

1) Build for human-AI collaboration: The most significant opportunities won’t come from replacing humans, but from amplifying them. The next wave of billion-dollar companies needs to be human-AI hybrids — organizations designed around orchestration, not substitution.

2) Rebuild education as an ecosystem, not a factory: The future of learning is personalized, project-based, and AI-augmented. Imagine a 12-year-old in Lagos or Lima learning from an AI tutor modeled on Sal Khan’s vision, while building projects with peers across the world. That’s the new classroom.

3) Reward adaptability, not credentials: We must stop confusing degrees with capability. The most valuable trait in the Age of Replacement isn’t what you know but how fast you can learn something new.

4) Redefine “career.”: The 40-year linear path is gone. The future is a mosaic: bursts of projects, start-ups, collaborations, sabbaticals, and retraining. Institutions that enable fluidity will shape the new social fabric.

5) Embed moral and civic purpose: AI may outthink us, but it cannot outcare us. The new frontier of education must reintroduce ethics, empathy, and responsibility as central tenets of human development.

What Comes After the Job

The most profound shift will be existential, not economic.

For centuries, work has been the organizing principle of modern life. It determined identity, status, meaning, and community. If AI undermines that, what replaces it?

Perhaps the answer lies in creation itself.

In a world where machines produce, humans may rediscover what it means to imagine. The future of education, then, is not about training workers, but about cultivating builders, caregivers, and citizens — people who design systems aligned with human flourishing.

In Hinton’s words, “The problem ultimately is not due to AI itself, but on how we organize society.”

That organization begins with what, and how, we choose to teach.

The Renaissance Ahead

The Age of Replacement is real, but it doesn’t have to end in despair.

If we get this right — if we rebuild education for imagination, curiosity, and adaptation, we might not just preserve work, but transcend it. We could usher in an era where human purpose isn’t tied to productivity, but to progress.

As Diamandis put it, the goal is not to prepare children for jobs that may not exist, but to prepare them to create the future itself. That is the essence of existential innovation. Not resisting what’s coming, but reinventing what it means to be human within it.

Because the future won’t fix itself.
We will.

With belief,
Yon

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“It is not difficult to make microbes resistant to penicillin in the laboratory by exposing them to concentrations not sufficient to kill them…The time may come when penicillin can be bought by anyone in the shops…” — Alexander Fleming, 1945

In my original letter, “A Future Without Antibiotics: How a New Breakthrough Could Save Us from Collapse”, I argued that antibiotics aren’t simply medical tools but are one of the bedrocks of modern civilization. We rely on them for treating infections, as well as enabling safe surgery, childbirth, cancer therapy, organ transplants, industrial food production, and even emergency trauma care. If antibiotics lose efficacy at scale, the consequences cascade far beyond healthcare, shifting the very assumptions of risk management, supply chains, and mortality profiling.

Recently, a new warning from the World Health Organization (WHO) has elevated that scenario from speculative to immediate. On 13 October 2025, the WHO released its Global Antibiotic Resistance Surveillance Report, showing that one in six laboratory-confirmed bacterial infections worldwide in 2023 were resistant to common antibiotic treatments, and that resistance rose in over 40% of pathogen–antibiotic combinations between 2018 and 2023.

And so, the argument on the potential “collapse of antibiotic infrastructure” is now clearly underway.

Today’s letter offers an updated synthesis, expanding on what the WHO numbers add, key shifts in innovation, policy, and systems, what remains missing, and a roadmap for rebuilding or safeguarding this civilizational infrastructure.

What the New WHO Report Adds

1) Quantification of scale and speed.

In my prior piece, I emphasized “if antibiotics become ineffective.” The new WHO data show the scenario is no longer if but already. The data: 1 in 6 infections globally were drug-resistant in 2023; resistance increased in >40% of pathogen–antibiotic pairs since 2018. Coverage by The Guardian underscores the same trend and notes, in particular, severe hospital-borne resistance.

Regional disparities are stark: in the South-East Asia and Eastern Mediterranean regions, roughly 1 in 3 infections are resistant; in Africa, about 1 in 5, with Gram-negative resistance (e.g., E. coli, Klebsiella) to first-line therapies already above 40% globally and >70% in some African settings.

These numbers illustrate that antibiotic effectiveness is eroding globally, and quickly.

2) Geographical inequality and system fragility.

The report underscores that the worst outcomes are in places with weaker health systems: fewer diagnostics, weaker infection prevention, limited access to second-line drugs, and poorer sanitation. Crucially, many of the countries facing the largest challenges lack surveillance capacity to even assess their AMR situation. That means we’re flying partially blind in the places most at risk.

The interplay of weak health systems × weak antibiotic pipeline × higher resistance burden creates a “perfect storm” in many low and middle-income countries, but the ripple effects are global: travel, migration, trade, and agricultural exports ensure no region is truly insulated.

3) Pipeline gap and the narrowing “treatment horizon”.

Perhaps the most sobering reality is that innovation is not keeping pace. Experts quoted in the press note that the toughest Gram-negative infections are outpacing development pipelines, which are dominated by incremental derivatives rather than novel modalities.

4) One-Health reality.

The WHO reiterates that AMR is not just a human-medicine problem but an ecosystem problem spanning human health, animal health, agriculture, and the environment (e.g., antibiotic runoff). Coordinated surveillance and interventions across sectors are essential.

What’s Changed Since the Original Letter

1) Elevated political recognition.

The “one in six” figure has finally entered public discourse, nudging AMR from a scientific concern to a strategic risk. The framing I argued for (treating antibiotic resilience like grid resilience, clean water, or cybersecurity) now feels not only plausible but necessary.

2) Innovation emphasis is shifting.

There’s growing focus on rapid diagnostics, bacteriophages, microbiome modulation, narrow-spectrum agents, and adjuvants that enhance the activity of existing antibiotics. Modeling work on collateral sensitivity - sequencing antibiotics so resistance to one increases sensitivity to another, has matured with actionable design principles for regimen switching.

3) Recognition of human/systemic imperatives.

The problem isn’t just “discover new drugs.” Misuse/overuse, sub-optimal dosing, lack of diagnostics, and environmental leakage all accelerate resistance. Prevention and stewardship are as important as chemistry. The WHO has repeatedly stressed surveillance gaps and the need for better data and governance.

4) The threat is plainly inside the house.

In the U.S., CDC scientists reported a ~70% rise (2019–2023) in infections from so-called “nightmare bacteria,” driven by NDM-producing CRE bugs resistant to nearly all antibiotics. These were once rare and linked to overseas care, but now they’re spreading domestically.

What Still Remains Deeply Missing

1) Incentives for true antibiotic innovation.

Short courses + stewardship (using less, not more) break the volume-based pharma model. We need infrastructure-style economics: subscription/availability payments, market-entry rewards, and delinkage from volume (the WHO and global AMR advocates have pushed variants of this, but implementation lags).

2) Diagnostics at scale.

Without point-of-care tools to distinguish bacterial from viral infections and identify resistance markers, clinicians prescribe “blindly,” increasing selection pressure. Nearly half of the countries didn’t report to the GLASS report, and surveillance gaps slow rational use.

3) Equitable access.

Low- and middle-income countries are pushed to use last-resort therapies first, often unaffordable or unavailable, turning parts of the world into reservoirs of resistance. Global supply, regulatory capacity, and stewardship networks remain thin.

4) Preventive infrastructure.

Water, sanitation, hygiene, vaccination, and hospital infection control are the first line when antibiotics falter, yet are under-invested in across many health systems. Resistance tracks with weak infection control.

5) Governance that matches the scale.

The 2024 UN AMR declaration was a start, but we still lack a climate-style global governance architecture with measurable commitments, financing, and enforcement.

Rebuilding (or Safeguarding) Antibiotic Infrastructure: A Potential Roadmap

1) Treat antibiotics as civilizational infrastructure.

Reframe antibiotics from “drug class” to infrastructure spanning healthcare, food systems, and societal risk-management. Create a global financing facility to fund R&D, manufacturing surge capacity, and stewardship as public goods; publish an Antibiotic Resilience Index to benchmark nations annually.

2) Build next-gen antimicrobial defenses.

Invest in new broad-spectrum agents but also in: (a) narrow-spectrum/pathogen-specific drugs, (b) adjuvants that restore potency, (c) bacteriophages/lysins/microbiome engineering, and (d) AI + synbio discovery engines. Leaders like MIT’s James Collins have spent years decoding antibiotic action and bacterial defense networks, precisely the kind of systems-biology approach we need to scale.

3) Make diagnostics + stewardship the default layer.

Deploy low-cost point-of-care diagnostics worldwide, and wire them into decision support and real-time surveillance. Use machine learning-guided therapy sequencing to exploit collateral sensitivity and slow resistance evolution, moving from one-off prescriptions to adaptive regimens.

4) Strengthen prevention and system resilience.

Fund water, sanitation, and hygiene, vaccination campaigns, hospital infection-control, and agricultural reforms (ending routine prophylactic use, monitoring runoff). The WHO’s One-Health framework should govern policy across ministries — health, agriculture, and environment.

5) Build governance, financing, and equity mechanisms.

Set up a Global Antibiotic Resilience Fund, scale “subscription” payments (pay for availability, not volume), use pooled procurement to guarantee equitable access to new drugs and diagnostics, and mandate transparent national reporting with independent audits.

Why This Matters for Civilization

1) The hidden infrastructure of low-risk life.

C-sections, hip replacements, chemo, transplants, ICU trauma care, neonatal care —all assume effective antibiotics. As efficacy erodes, the “background risk” of modern medicine rises: more complications, longer stays, higher costs, higher mortality.

2) Global interdependence.

Pathogens don’t respect borders. Misuse or lack of stewardship in one region degrades options everywhere - hence AMR is a global public-goods problem.

3) Time-to-failure is accelerating.

Resistance is rising faster than new therapies are arriving; the half-life of our drugs is shortening. Adaptive regimens and adjuvants can buy time; diagnostics and prevention can reduce pressure, but we have to move now.

4) Equity is a moral and practical imperative.

If collapse hits the weakest systems first and then spreads, we just face a medical crisis, but more importantly, we face a legitimacy crisis for global health. Equitable access is both an ethical and a strategic imperative.

Toward a Future With (or Without) Antibiotics

The future isn’t prewritten, but the window is small, and biology + technology + finance + governance is a nonlinear system. A few scenarios to consider from the report:

1) Optimistic trajectory (“Rebuilt infrastructure”): Diagnostics scale globally; stewardship becomes default; new therapies and adjuvants deploy equitably; efficacy decline slows; we stabilize at a resilient equilibrium.

2) Middle-road (“Managed decline”).
High-income countries improve outcomes; Low and middle-income countries lag; resistance rises regionally; costs and complications increase without global collapse.

3) Pessimistic (“Civilizational rollback”).
Stewardship fails; diagnostics don’t scale; innovation stalls; routine procedures become high-risk; food systems wobble; inequality deepens.

Given the WHO data, we are already on the cusp between 2 and 3 unless action accelerates.

Final Thoughts

The collapse of antibiotic effectiveness is unfolding - it’s no longer a remote scenario. To respond, we have to treat antibiotics less as an afterthought and more as the infrastructure that underwrites low-risk modern life. The blueprint is clear: investment, innovation, diagnostics, prevention, and governance — fully integrated. A narrow-spectrum drug without diagnostics won’t save us, and diagnostics without access and stewardship won’t either. We have to design the system.

With belief,
Yon

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“Technology is a means of evolution made visible” — Kevin Kelly

Every great technology gives humanity a new sense.

The telescope extended our eyes to the cosmos. The computer extended our brain into the digital realm. Now drones, in a way, are extending our nervous system — enabling us to see, sense, and act across the entire planet in real time.

To most people, drones still look like flying cameras or weapons. But that’s the narrowest view of a deeper shift: drones are becoming the mobile interface between intelligence and the physical world — between data and action, between the virtual and the real.

As artificial intelligence scales exponentially, drones are becoming the hardware substrate that lets our intelligence, human and machine alike, touch the world. That makes drones an existential technology — tools that help us see, manage, and safeguard the systems that decide whether civilization endures or collapses.

Seeing the Planet, Seeing Ourselves

Drones are the eyes and ears of a planetary-scale intelligence system. They monitor forests, rivers, glaciers, farmlands, and cities with unprecedented precision.

In 2024, the global drone market was valued at approximately $73 billion and projected to grow to around $164 billion by 2030 (CAGR ~14.3 %) — a sign of accelerating adoption across sectors. In the commercial drone market, China’s DJI commands ~70 % of global share.

Unlike satellites, drones can fly under clouds, into forests, and through industrial zones, gathering high-resolution data (though coverage, battery life, regulation, and environmental constraints limit this reach.)

In agriculture, drones can provide near-subcentimeter analysis of crop health, soil composition, moisture, and canopy structure and potentially enable yield improvements while reducing water and fertilizer usage (albeit this potential still needs to be validated at scale). And in disaster zones — earthquakes, hurricanes, industrial accidents — drones can serve as first responders.

While we can think of each drone flight as a heartbeat in a living network — a new layer of perception, there are risks and constraints in perception, to name a few:

• Sensor bias & blind spots: Optical, LiDAR, thermal, and multispectral sensors each have environmental limits (smoke, fog, dust, heavy vegetation).

• Data deluge & integrity: The sheer volume of data invites errors, miscalibration, adversarial attacks, or misinterpretation.

• Privacy & surveillance concerns: Ubiquitous observation risks overreach, social control, and chilling effects.

• Regulation & airspace conflict: Many jurisdictions restrict drone flights (especially beyond line-of-sight), which slows deployment.

Building Resilient, Distributed Infrastructure

Existential innovation is as much about resilience as it is about progress. Drones are quietly becoming the backbone of a new kind of distributed infrastructure — autonomous, adaptive, and local.

In energy, UAVs perform inspections of wind turbines, solar farms, and power lines. In certain academic studies, UAV-enabled inspection schedules reduced flight/inspection time and maintenance costs in wind farm contexts. For example, one paper reports a ~25 % reduction in inspection flight time by optimizing routes.

In logistics and medical supply, drone delivery networks operate across more than 35 countries (in pilot or partial form). In a 2024 interview with Peter Daimandis, Zipline’s founder revealed that their system had completed over 80 million autonomous miles, 1.1 million commercial delivery’s and reduced infant mortality in Rwanda by 51%. In one Ghanaian region served by Zipline, maternal deaths are reported to have fallen by 56.4 % after the implementation of drone delivery for critical medical supplies.

In connectivity, high-altitude or tethered drones may restore communication after infrastructure collapse (e.g., earthquake, hurricane). But these remain emergent projects rather than being deployed at scale.

In an era of climate shocks, pandemics, and conflict, maintaining continuity without overdependence on centralized systems is critical for civilization. Drones can become infrastructure of infrastructure.

Risks and trade-offs in infrastructure:

• Reliability and maintenance: Drone fleets require robust servicing, supply chains for parts, and high uptime.

• Cost and scaling challenges: Many deployments remain subsidies or pilots; economies of scale aren’t guaranteed in all geographies.

• Safety and collisions: As drone traffic increases, the risk of collisions (between drones, with manned aircraft, or with obstacles) rises.

• Electronic warfare/jamming: Drones can be spoofed, jammed, or hijacked — particularly in conflict zones.

• Ecosystem dependency: Building infrastructure on drones creates new dependencies; the failure of drone systems would cause a cascade of consequences.

The Bridge Between the Physical and the Digital

AI without embodiment is blind. Drones provide the eyes, wings, and hands to carry intelligence into the real world. Every flight can close a loop: perception → computation → action. This is the essence of planetary autonomy — distributed intelligence acting in the physical world with minimal latency.

Industrial drones can generate high-resolution 3D data that powers digital twins — virtual replicas of factories, grids, and cities used for simulation, predictive maintenance, and scenario planning. We can imagine cities hosting thousands of autonomous drones maintaining infrastructure, optimizing traffic flow, managing air quality, and supporting emergency response — as civic stewards, not as surveillance enforcers.

Cautions and tensions:

• Autonomy safety/predictability: Drones acting under AI must be safe, robust under edge cases, and explainable.

• Control and human-in-the-loop: Deciding when humans intervene versus letting drones act is a key design challenge.

• Algorithmic bias and failure mode risks: Models may misinterpret contexts, especially in unexpected environments.

• Digital-physical feedback loops: Erroneous AI decisions may become amplified if drones act on flawed models.

The Geopolitical Frontier: Drones and Sovereignty

What if the 21st century will be defined by autonomous deterrence, analogous to how the 20th century was shaped by nuclear deterrence?

The war in Ukraine provides a stark illustration of how low-cost drones have shifted power. $500 consumer UAVs have destroyed million-dollar tanks, forcing heavy equipment to adapt or retreat.

China produces over 80% of civilian drones globally; DJI alone holds ~70% global market share. For democracies, dependence on Chinese hardware in law enforcement, agriculture, and infrastructure is a strategic vulnerability.

The U.S. Department of Defense’s Replicator Initiative plans to deploy “swarms of thousands of autonomous systems” to offset adversary numerical advantages. Control over drone hardware, system software, and AI models has become a sovereign imperative — nestled at the intersection of defense, industry, and information security.

Responsibility in the Age of Autonomy

With great visibility comes great responsibility. When drones deliver medicine or help extinguish a wildfire, they amplify the best of technology. When they strike without human control or misidentify targets, they test our moral framework.

Existential innovation demands ethical innovation alongside it. We must ensure autonomy doesn’t erode empathy — that acting remotely doesn’t become feeling remote. That means: (1) Transparent data and accountability standards; (2) Explainable, values-aligned AI; (3) Human-centered design that embeds awareness of context, moral reasoning, and fallback; (4) Governance frameworks (international norms, liability regimes, oversight institutions); (5) And education and cultural reflexivity so operators and designers understand the stakes.

In the 20th century, the moral test was restraint with nuclear weapons. In the 21st century, it’s how we humanize autonomous power, making autonomous instruments (not only drones) capable of flourishing rather than control.

A Civilization That Sees — and Cautiously Acts

Drones grant us collective perception — a sort of planetary sense organ. They can let us measure, model, and mend the systems that sustain life. They can signal the maturation of our capacity to act wisely at scale. But visibility is not enough: responsibility, resilience, and restraint must accompany capability. As we grant machines autonomy, we must build in safeguards so that errors, adversaries, or hubris do not become catastrophic.

Survival in the 21st century depends not only on intelligence, but also on perception, and on the wisdom to wield it.

Now What

We must invest in autonomy with alignment: systems must be robust, transparent, explainable, and values-driven. We must rebuild local manufacturing: diversify and establish local American drone hardware / firmware ecosystems to avoid strategic dependencies. We must integrate drones across existential domains: climate, health, agriculture, infrastructure, defense — in ways that reflect their interdependence. We must support governance, norms, standards — from airspace regulation to liability regimes to international treaties. Last but not least, we must cultivate ethical culture among technologists: encourage humility, scenario thinking, red teaming, and cross-disciplinary accountability.

A new operating system for the sky is emerging rapidly, one that we must shape with clarity, care, and courage.

With belief,
Yon

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“Our goal should be to live life in radical amazement.”
— Abraham Joshua Heschel

Every civilization carries within it a rhythm — a pattern of pause, reflection, and renewal. For the Jewish people, that rhythm is encoded in the calendar itself.
It’s a way to mark time, of course, but also a system for making time — a design for how people survive, evolve, and renew across millennia.

The Jewish High Holidays — Rosh Hashanah, Yom Kippur, and the ten days in between — form the core loop of that operating system. They are an ancient practice of confronting reality, taking responsibility, and beginning again.

Between Two Homes

I grew up in Israel, where the rhythm of the Jewish calendar shapes the rhythm of life.
The holidays are not abstractions, but they arrive with the weather, the food, the songs, and the collective mood of a nation that has seen renewal born from near extinction.

Now, decades later, I find myself an American citizen, a title I never expected to carry, but one that fills me with gratitude and awe. Becoming an American this year was its own kind of Rosh Hashanah moment — a rebirth of identity, a chance to ask again: What world am I helping to create?

Between these two worlds — the land of my birth and the country I now call home — I’ve come to see how renewal is not just a personal journey but a civilizational necessity. The same principles that have sustained Judaism for thousands of years may hold lessons for how humanity can navigate its own crossroads — the era of AI, synthetic biology, and planetary fragility, among others.

Rosh Hashanah: The Power of the Reset

Rosh Hashanah is often described as the Jewish New Year, but it’s more profound than that. It marks the birthday of consciousness — the moment, according to tradition, when Adam and Eve first awoke and perceived creation.

The shofar’s raw, unfiltered sound cuts through comfort and habit as a sort of “system reboot.” It doesn’t necessarily soothe — it more disrupts. And as if it asks if we have fallen asleep at the wheel of our own creation.

In today’s world, we are surrounded by noise — notifications, headlines, updates. But the shofar’s sound is different, almost as if it awakens the soul, demanding presence, humility, and the courage to start over.

In a century defined by machines that learn and systems that optimize, we too need a Rosh Hashanah mindset — a willingness to reboot civilization’s software when it drifts from its original intent.

Rosh Hashanah teaches that renewal is not a reactive process, but a proactive one.
We don’t need to wait for collapse; we decide to begin again.

Yom Kippur: The Ethics of Accountability

Ten days after Rosh Hashanah comes Yom Kippur — the Day of Atonement, the day when the Jewish people look in the mirror. A day of repair, not punishment.

In Hebrew, the word teshuvah — often translated as repentance — literally means return. Return to integrity, to truth, to the source of creation.

The world of existential innovation needs a Yom Kippur. AI models hallucinate truth. Algorithms optimize for engagement over empathy. Biotechnology pushes toward the ability to rewrite life itself. And yet — where is our collective teshuvah? Where is the audit of the soul before the next line of code?

Yom Kippur’s structure can offer a timeless blueprint for governance and ethics. We begin by fasting — slowing down the body so the mind can listen. We confess — not to wallow in guilt, but to clear our moral debris. And then we forgive — because no system can sustain itself without the grace to evolve.

In a nutshell: If Rosh Hashanah is the reboot, Yom Kippur is the diagnostic. It teaches that innovation must be paired with introspection — otherwise, creation becomes chaos.

The Ten Days of Awe: The Space Between

The ten days between Rosh Hashanah and Yom Kippur are called Aseret Yemei Teshuvah — the Ten Days of Return. They are a liminal space, neither quite the old year nor yet the new. Time slows, and reflection deepens.

In that in-between moment, transformation happens. It’s when decisions crystallize, when relationships mend, when clarity emerges. Civilization, too, lives perpetually in this space — between old paradigms and new possibilities.

Nowadays, we stand at the intersection of the analog and the digital, the human and the artificial, the known and the unimaginable. The Ten Days remind us that the in-between is not something to rush through. It’s the sacred interval where we re-design meaning itself.

As Heschel wrote, “We must learn to be in awe before we can begin to understand.” Awe is preparation, not paralysis. It’s what allows us to hold both fear and hope as we build what comes next.

The Book of Life: Agency in a Deterministic Age

During the High Holidays, tradition says our names are written in the Book of Life, but the outcome isn’t sealed until Yom Kippur. Repentance, prayer, and acts of goodness can change the decree.

This theology carries an astonishing idea: what if fate is not fixed?

In a world increasingly governed by algorithms and probabilities, this is a radical act of humanism. The Book of Life reminds us that agency still matters — that our choices, even small ones, bend the arc of the possible.

Existential innovation depends on the same principle. We are not passive subjects of AI, climate, or demographics; we are authors. The ink is still wet.

Hannah Arendt once said that “The miracle that saves the world is the fact of birth — that human beings are born to begin again”. And so, each act of creation — each experiment, each idea — is a page in that book.

From Individual to Collective Renewal

Yom Kippur’s prayers are written in the plural — We have sinned. We have betrayed. We have failed. This grammar of solidarity is deliberate.

The modern world often glorifies the individual — the founder, the genius, the disruptor. However, the Jewish calendar insists that renewal is a collective experience. No one is saved alone.

Similarly, our civilization’s survival will not hinge on one brilliant innovator or one perfect policy. It will hinge on whether we can act as a moral ecosystem — a network of humans willing to reflect, forgive, and rebuild together.

Existential innovation is not merely technological — it is communal. A culture of shared responsibility, a modern minyan of minds, each accountable to the other.

America and Israel: Two Experiments in Renewal

I see my journey of becoming an American citizen as a spiritual milestone. It reminded me that America, at its best, is itself a High Holiday project: an ongoing experiment in renewal, built on the belief that the world can be remade through moral imagination.

In Israel, I grew up with the sense that survival itself is sacred — that history can collapse and still be reborn. In America, I’ve come to see the parallel truth: that freedom, too, must be renewed or it decays.

Both nations, in their own ways, wrestle with the same paradox that defines Yom Kippur — how to remain faithful to the past while daring to re-create the future.
Both hold lessons for a world now confronting its own moral test — whether technology will deepen our humanity or dissolve it.

Radical Amazement

Heschel’s call to “live life in radical amazement” is a philosophy of survival. A civilization that loses its capacity for awe becomes numb — efficient but empty, powerful but directionless. In the rush to build machines that can think, we must not forget the miracle of being a being that can wonder. Radical amazement is the antidote to nihilism. It keeps innovation tethered to reverence.

When I hear the shofar now — even halfway across the world from where I first heard it as a child — I hear a signal from the future: a reminder that renewal is a discipline, not an event.

Each year, the High Holidays reenact a simple yet staggering idea: that today the world is born again. It means that the story isn’t over — not for nations, not for civilizations, not for you or me.

The Jewish calendar encodes the oldest innovation methodology we know — a cycle of disruption, introspection, and creation. It’s the pattern that has sustained one people through exile and return, and it might be the pattern that sustains humanity through its own transformations ahead.

We do not need to invent the art of renewal. We only need to remember it.

With belief,
Yon

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It’s not something I ever thought about, nor something I ever planned for. But as I stood with my right hand raised, pledging allegiance to the United States, I felt a surge of pride, humility, and gratitude.

I was born and raised in Israel — a small country with a big story. My childhood was shaped by a nation forged in resilience and defined by its contradictions: ancient and modern, fragile and bold. From my years growing up in the North of Israel, serving in the IDF, and my time at the Zell Entrepreneurship Program, I absorbed lessons about grit, responsibility, and daring to build. Those are the roots that anchor me.

But my story is also an immigrant story. I am the first generation in Israel to parents who came as Russian immigrants. Their journey — leaving behind one world to build a life in another — taught me to respect the courage, sacrifice, and hope it takes to start over. That story of uprooting and rebuilding has always been part of my identity. And in many ways, it’s what makes me connect so profoundly with the American story — because at its core, America is a nation of immigrants.

And yet, my journey did not move in a straight line from Israel to America. London was the bridge. It’s where I became a global citizen. It was where I built Kano Computing, my first startup, where I discovered what it meant to assemble teams, raise capital, and inspire children everywhere to create with technology. London gave me perspective — a vantage point from which I could see Israel in the context of the wider world, and the world through the lens of culture, technology, and possibility.

It was also during my time in London that I met the person who changed my life.

I came to America because of my wife, Keri, not for a business opportunity, but for love and a life partnership. It was a life choice. Business came after, and that distinction matters. Because when you choose a place out of love, you commit to it with your whole self.

America became not just the place where I’m building companies, but also, and much more importantly, the place where I’m building a family and a life. America became my home, and becoming a citizen is a commitment to the country, the community, and to its future.

And Ohio, the Midwest, is where our home is and where we’re building a life. A region so often overlooked, but in my eyes, overflowing with possibility. In Ohio, I see grit in founders, resilience in communities, and hunger for opportunity. I believe this is one of the destinations where the next great wave of American innovation will emerge — and I’m excited to be part of building it.

I am proud to become an American because this country, for all its imperfections, remains humanity’s boldest experiment: can a nation of immigrants, ideas, and contradictions lead, not simply endure? The future in America always feels unfinished, always up for grabs, always open to builders willing to take responsibility.

Together with Keri, we believe that we can raise our family in a place that honors my past while giving us the horizon of a future we can help shape. And this week, as I became an American, I take on this responsibility.

I can proudly say that I’m the son of immigrants who came to Israel to start anew, shaped by Israel’s resilience and inspired by London’s global perspective, and now, a proud American, building a future in Ohio.

With belief,
Yon

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“We need energy miracles. Nuclear power is one of them.” — Bill Gates

Artificial Intelligence is a technological revolution, but it’s also an energy revolution.

Every time we marvel at Claude writing code, or Midjourney generating images, or a data center training the next frontier model, what lies beneath is electricity. Gigawatts of it. The infrastructure powering the AI boom is, at its core, an infrastructure of energy.

And the paradox is that the very technologies designed to accelerate humanity’s future could overwhelm the grids that sustain us - unless we reimagine how we generate power.

The International Energy Agency projects that electricity demand from data centers will more than double by 2030, reaching nearly 945 terawatt-hours (TWh) annually - equivalent to the entire electricity consumption of Japan today¹. AI-optimized data centers are the largest driver of this growth. In the U.S. alone, data centers are expected to account for nearly half of the increase in national electricity demand between now and 2030².

This is truly a seismic shift. AI isn’t simply software, but an infrastructure, built on silicon, steel, and above all, electrons.

If AI is going to scale responsibly, it will need an energy backbone that is abundant, reliable, and clean. Nuclear energy is uniquely positioned to meet that challenge. A few key reasons for that:

1. Baseload consistency - Unlike solar and wind, nuclear power delivers constant, 24/7 energy — essential for AI workloads that cannot tolerate downtime³.

2. Zero-carbon reliability - Nuclear is one of the few large-scale sources of electricity that produces virtually no CO₂ emissions in operation⁴.

3. Next-generation technology. Small Modular Reactors (SMRs) and advanced reactors promise smaller footprints, faster builds, and flexible deployment — ideal for pairing with energy-hungry data centers⁵.

4. Policy momentum - The U.S. government has prioritized nuclear innovation, and tech giants are beginning to sign long-term nuclear purchase agreements to secure their AI-driven future⁶.

This is a key part of why nuclear stocks are booming. Markets are beginning to price in what policymakers and technologists are just starting to grasp - that nuclear is no longer an optional “alternative”, but one that is emerging as a critical path forward.

For the United States, the AI-nuclear nexus presents a challenge and an opportunity to lead the world into a new era of energy abundance. To seize it, the U.S. must:

1. Streamline regulation - Nuclear licensing remains mired in bureaucracy; SMRs and advanced designs must be fast-tracked without compromising safety.

2. Invest in supply chains - From enriched uranium to advanced components, America must secure its nuclear fuel cycle and reduce dependence on geopolitical rivals.

3. Anchor demand - AI and cloud providers (Google, Microsoft, Amazon) should secure long-term PPAs (power purchase agreements), providing financiers with confidence to accelerate deployment⁷.

4. Upgrade the grid - Nuclear alone isn’t enough; America must build a grid capable of integrating new capacity and transmitting it where it’s needed most.

5. Preserve today’s fleet - Extending the life of existing nuclear plants is the fastest way to avoid losing capacity during the build-out of next-gen reactors.

If we do this, America could meet the energy demands of the AI century, and perhaps more importantly, we can set the global standard for what it means to power intelligence responsibly, sustainably, and at scale.

AI is often imagined as a cerebral technology - algorithms, models, neural networks. But in truth, it is deeply material. It runs on semiconductors etched into silicon, servers cooled by water and air, and electricity generated by turbines, panels, and reactors. Labs and boardrooms will play a critical role in defining who wins the AI revolution, but power plants and transmission lines will play a vital role in unlocking the winners and help ensure that we enjoy energy abundance.

Nuclear energy offers us the chance to align the exponential growth of artificial intelligence with the stability of sustainable power. It offers us a bridge between the digital future we are racing toward and the physical limits of the planet we inhabit.

And in that convergence, America has the chance to lead - not just in AI, but in the energy systems that will sustain it.

With belief,
Yon

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Referecnces

1. International Energy Agency (IEA). AI is set to drive surging electricity demand from data centres - Link.

2. IEA. Data centre electricity demand - Link.

3. GIS Reports Online. AI and Energy: A Growing Challenge - Link.

4. Nuclear Energy Institute (NEI). State of the Nuclear Industry 2025 - Link.

5. ASME. Energy Subcommittee Examines Nuclear Power for AI Infrastructure - Link.

6. AP News. Google to buy nuclear power for AI data centers - Link.

7. Nuclear Energy Institute (NEI). State of the Nuclear Industry - Link.

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“The destiny of nations depends on how they nourish themselves.”
— Jean Anthelme Brillat-Savarin

The Fertility Freefall

In 1960, the average woman worldwide had an average of five children. Today, that number has collapsed to 2.3 and is falling fast [1]. In over 70% of countries, fertility is already below the “replacement rate” of 2.1 — the level necessary to sustain a stable population [2].

The chart I shared previously — a shocking but straightforward visualization — captures this collapse in motion.

Fertility decline is no longer confined to wealthy nations. India, long seen as the demographic counterweight to China, has dipped below replacement levels in several states [3]. China itself is now shrinking, with its population declining by over two million last year — the sharpest decline since the Great Chinese Famine [4]. Even Latin America, once buoyed by large families, is converging with the global trend [2].

This touches the heart of civilizational continuity. Without children, there is no future workforce, no taxpayers, no innovators, no soldiers, no artists, no builders. A world without babies is a world that cannot renew itself.

We are entering an era of demographic freefall. And the consequences will define everything from economic vitality to national security.

Why Fertility Matters — More Than Ever

1. Civilizational Continuity

Civilizations collapse when they cannot reproduce themselves. The Roman Empire famously struggled with declining birthrates among its elite, relying increasingly on mercenaries to defend its borders [5]. More recently, Japan’s demographic implosion has turned the world’s third-largest economy into a cautionary tale of stagnation, with more adult diapers sold annually than baby diapers [6].

Without replenishment, societies don’t just shrink. They age, creating brittle structures unable to adapt or sustain.

2. The Economic Engine

Growth requires workers. Innovation requires youth. Entrepreneurship thrives on the daring energy of those in their 20s and 30s. As the International Monetary Fund notes, aging populations lead to “secular stagnation,” dragging down productivity and straining public finances [7].

By 2050, the dependency ratio — the number of retirees supported by each worker — is expected to double in countries ranging from South Korea to Italy [8]. That means fewer people producing goods, paying taxes, and funding pensions, while more people require healthcare and social services.

Without new life, the very engine of capitalism seizes up.

3. The Social Fabric

Declining fertility isn’t just about economics; it’s about community. Smaller families mean fewer siblings, cousins, and extended networks. Loneliness rises. Intergenerational bonds fray. In countries like South Korea, where fertility has fallen to a record-low 0.7 births per woman, entire schools are closing due to a lack of students [9].

4. Strategic Stability

Fertility is a matter of national security. Nations with shrinking populations lose geopolitical weight. Russia’s declining population underpins its increasingly desperate geopolitical gambits [10]. China’s looming demographic collapse threatens its long-term ambition to rival the U.S. as a global hegemon [4]. Meanwhile, Africa, projected to account for 40% of the world’s population by 2100, is set to become the center of global labor, culture, and perhaps conflict [11].

History teaches us that power follows population. Fertility may be perceived as a private matter, but it is also profoundly public.

5. Human Aspiration

Finally, fertility matters because life itself matters. Beyond GDP charts and pension funds, reproduction is the most profound human aspiration — to create, to nurture, to continue the story of our species. When people give up on family, they are also giving up on the belief that tomorrow is worth building.

As the philosopher Hannah Arendt wrote, “The miracle that saves the world … is the fact of birth” [12].

The Fertility Renaissance

If fertility collapse is the challenge, then a fertility renaissance must be the response. This is a frontier of science, technology, culture, and policy.

Science

The fertility industry is still in its infancy. Over 13 million babies have been born from IVF globally [13]. But advances are accelerating: egg freezing, embryo screening, artificial wombs. Research into ovarian rejuvenation and genetic therapies could extend reproductive lifespans dramatically. Just as antibiotics and vaccines defined the 20th century, the 21st may be defined by reproductive technologies.

Technology

AI is entering fertility. AI can help aspiring parents navigate IVF, track cycles, and make more informed medical decisions. Wearables and biometric sensors can help make fertility tracking precise and personalized. Where once parenthood was shaped by biology alone, it will increasingly be enabled by technology at every stage.

Policy

Governments are beginning to stir. Hungary, Singapore, and Israel have deployed aggressive pro-natalist policies [2]. But subsidies alone rarely move the needle. What’s needed is a full-spectrum approach: affordable housing, flexible work, childcare infrastructure, and cultural shifts that honor parenthood as a noble pursuit, not a burden.

Culture

Perhaps the most complex and most essential shift is reframing family. In a culture that celebrates individual consumption, the family has been cast as a sacrifice. We must flip that script. In the post-World War II baby boom, starting a family was an act of optimism. We need to rekindle that sense of audacity now.

Addressing the Counterarguments

Some argue that declining fertility is good for the planet — that fewer people mean less carbon, less consumption. But this is shortsighted. Innovation, not depopulation, is the path to sustainability [14]. A shrinking humanity is not an environmental solution but a civilizational suicide note. Others claim technology can substitute for people. But machines cannot dream. They cannot build communities. A world without children is not just smaller — it’s emptier.

Just as climate became the defining cause of the past two decades, fertility must become one of the defining causes of the next.

We need startups building pioneering solutions that deliver 10x the performance of current IVF methods. We need investors backing research as boldly as they fund AI. We need policymakers who view the family as a critical pillar of society. And we need a cultural movement that says: the future is not something to fear — it is something to build, literally, through new life.

A Final Note

Fertility is about whether humanity believes it has a future, not just about babies. In a century defined by existential risks and technological upheaval, nothing is more radical, more urgent, and more hopeful than choosing to bring new life into the world and building the systems to make that choice easier for all.

The destiny of civilizations does not just depend on weapons, markets, or machines. It depends on whether it reproduces.

With belief,
Yon

References

1. World Bank. Fertility rate, total (births per woman). (2023). Link

2. United Nations Population Division. World Fertility Highlights. Link

3. Indian Ministry of Health. National Family Health Survey-5. (2021). Link

4. National Bureau of Statistics of China. China Population Data 2024. Link

5. Harper, K. The Fate of Rome: Climate, Disease, and the End of an Empire. Princeton University Press, 2017. Link

6. The Guardian. Sign of the times in Japan as nappy company switches production to adult nappies. (2024). Link

7. IMF. The Macroeconomic Effects of Aging. (2020). Link

8. OECD. Dependency Ratios in Aging Societies. (2022). Link

9. Statistics Korea. Birth Statistics 2024. Link

10. Eberstadt, N. Russia’s Demographic Crisis. American Enterprise Institute (2023). Link

11. UN DESA. World Population Prospects 2022. Link

12. Arendt, H. The Human Condition. University of Chicago Press, 1958. Link

13. More Than 13 Million Babies Have Been Born From IVF. Parents.com Link

14. Lee, R. & Mason, A. Fertility, Human Capital, and Development. NBER (2010). Link

👋 Hello! My mission with Beyond with Yon is to help solve humanity's greatest existential challenges and advance the human condition. Connect with me on LinkedIn and X.

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The greatest breakthroughs don’t add complexity—they make the essential accessible.

I’ve spent nearly two decades building companies at the intersection of technology and human possibility. Each time, I’ve asked myself the same question: what if this innovation could be more than a product—what if it could rewire an entire system of how people live, learn, or heal?

After Supersocial was acquired, I reconnected with a long-time friend, Zack Dvey-Aharon, CEO of AEYE Health, and that question hit me squarely in the chest. Here was a team that had cracked something extraordinary: the ability to diagnose diabetic retinopathy—one of the leading causes of preventable blindness, instantly, in minutes, at the point of care.

I could see how AEYE’s breakthrough technology could not only help millions of diabetic patients but also help shape the future of preventive healthcare.

Why This Matters

Some technologies may seem incremental, while others shift the foundations of how entire systems operate. What AEYE Health is building belongs unmistakably to the latter.

At first glance, this might look like an eye exam innovation. But diabetic retinopathy is one of the most insidious and preventable causes of blindness. In 2020 alone, it left approximately 1.07 million people blind and nearly 3.28 million with moderate-to-severe visual impairment worldwide (source). Despite treatments that can prevent up to 98% of vision loss with early detection (source), only 25% of those at risk receive annual screenings.

It’s not that the science fails—it’s that the system fails to bring it to people in time. AEYE Health solves that failure.

The company’s autonomous, portable, FDA-cleared AI system has already been deployed in clinics, and it delivers a full diabetic retinopathy diagnosis in less than a minute, at the point of care, using a handheld retinal camera and a single image.

No human interpretation required.
No multi-week wait for results.
No patient lost in the referral maze.

A person walks into their primary care clinic, and by the time they leave, they know whether they’re at risk of losing their sight.

That shift—from delayed, specialist-only screening to instant, universal access is a significant improvement in eye care, as well as a potential blueprint for a new paradigm in healthcare delivery.

Think about what happens when diagnostics become instantaneous, autonomous, and ubiquitous:

• Access explodes. Every clinic, urban or rural, can deliver world-class diagnostics.

• Costs collapse. Early detection prevents the need for expensive late-stage disease management.

• Outcomes improve. Patients don’t disappear into the cracks of the system—they get answers and interventions when it matters most.

And diabetic retinopathy is just the beginning.

The eye is more than a sensory organ—it’s a biomarker-rich canvas of systemic health. Retinal imaging already shows predictive signals for cardiovascular disease, hypertension, glaucoma, and even neurological conditions. AI can see what human specialists cannot. AI screening technology could evolve into a universal diagnostic layer, where the eye becomes a window into the body, and early detection becomes the new default across general care.

The Larger Context

Healthcare has long struggled with a paradox: we spend the most when it’s too late. The promise of AI is to flip that equation—to put intelligence at the edge, where people actually live and seek care.

The momentum is staggering: The global AI in healthcare market was valued at $26.6 billion in 2024 and is projected to reach $187.7 billion by 2030, with a compound annual growth rate (CAGR) of ~38.6% (source). And the preventive healthcare technologies market is projected to grow from $295 billion in 2024 to $1.19 trillion by 2034 (source).

These are clear signals that AI will be foundational to preventive care.

Why I’m Helping

I’ve spent my career at the intersection of technology and human possibility. AEYE is a rare case where the impact is immediate, measurable, and deeply human: saving sight, preserving livelihoods, and showing us what it looks like to design healthcare that actually works for people.

That’s why I’m helping AEYE Health with its strategic growth. It’s not just about the eyes but about rewiring the way we deliver preventive care in the 21st century.

The future of healthcare will be written in the ordinary moments when a patient gets the right answer, at the right time, in the right place, not solely in labs or hospitals. And AEYE is building tools to make that future possible.

With belief,
Yon

👋 Hello! My mission with Beyond with Yon is to help solve humanity's greatest existential challenges and advance the human condition. Connect with me on LinkedIn and X.

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Earlier this week, researchers in China placed a lung from a genetically modified pig into a brain-dead man. It was the first time in history that a human being drew breath through the lungs of another species.

This was the culmination of decades of research into xenotransplantation—i.e., the use of animal organs in human bodies. For decades, the field was mired in scientific failure, ethical controversy, and reputational risk. The lung, of all organs, is considered the holy grail of difficulty. It is delicate, spongy, intensely vascularized, and extraordinarily prone to rejection.

For nine days (216 hours), the pig lung remained viable and functional inside the human recipient without hyperacute rejection or infection, according to a peer-reviewed study published in Nature Medicine. Edema and immune rejection began to appear after 24 hours and worsened on days three and six, but partial recovery was observed by day nine. Even under heavy immunosuppression, the experiment revealed both the promise and the fragility of this new frontier.

This is a pivot point. Just as dialysis machines once extended the lives of failing kidneys, and just as antibiotics once conquered lethal infections, this transplant potentially represents the beginning of a new epoch: one in which the scarcity of its own biology no longer constrains humanity.

The breakthrough of the pig lung transplant matters for three reasons:

1/ It proves complexity can be engineered. If we can engineer lungs (the most fragile and rejection-prone organ), hearts, kidneys, and livers will follow with far greater predictability.

2/ It validates decades of genetic engineering. Scientists have painstakingly edited pig genomes to remove genes that trigger rejection and deactivate retroviruses. CRISPR is no longer just a theory - it could actually save lives.

3/ It breaks the bottleneck of scarcity. Every year, thousands die on organ waiting lists. Pig organs could become a renewable, scalable supply chain for human life.

The risks are immense:

1/ Biological Unknowns. Pig retroviruses, immune cascades, and long-term rejection - none are fully understood.

2/ Reputational Fragility. A single high-profile death could delegitimize the entire field for a generation.

3/ Ethical Firestorms. Critics will frame this as transgressing natural boundaries, just as IVF and GMOs once were demonized.

4/ Systemic Risk. If public trust collapses, science will stall, and civilization will lose a critical tool for resilience.

But there’s an existential importance here. This is not just about medicine but about civilization’s survival architecture.

Civilization has always advanced by creating redundancies. We back up our power grids. We mirror our servers. We double-insure our financial systems. Yet our biology, the most fragile system of all, has no redundancy. If your lung fails, there is no backup.

Until now.

Pig lungs are not merely organs. They are backups. They are resilient. They are civilization saying in a way: “We will not let scarcity of biology dictate the boundaries of human life.”

In my recent essay, An Iron Dome for Immunity, I argued that humanity must build a defense system for the body as robust as Israel’s Iron Dome - an immune architecture capable of intercepting threats before they devastate us. Xenotransplantation is a pillar of that same philosophy.

The Iron Dome for Immunity is a defense.
Xenotransplantation is a supply.
Together, they form a civilizational biomedical shield.

Defense alone is insufficient if the system collapses. Supply alone is insufficient if the system cannot fight. The future of medicine is not merely treating illness but building systemic redundancy into life itself.

This is what civilization looks like when it takes itself seriously.

Every great biomedical leap was once considered heretical, dangerous, or “unnatural.”

Vaccination: In the 18th century, Edward Jenner inoculated a boy with cowpox to protect against smallpox. People mocked it as grotesque “cross-species” meddling. Today, vaccination is the bedrock of global health.

Organ Transplantation: In 1954, the first successful kidney transplant was seen as hubris. Now, tens of thousands of people live each year because of it.

IVF: In 1978, the first “test-tube baby” triggered global outrage. Today, millions of children have been born through IVF, including many who will shape the future of our civilization.

Pig lungs fit this lineage perfectly. Every epoch-shaping advance in medicine began as controversy. Within a generation, they become mundane.

What was once “playing God” becomes simply playing human better.

The existential importance of this breakthrough extends beyond its medical implications - it also has demographic, economic, and strategic implications.

1/ Demographic Survival: We are entering an era of collapsing fertility and aging populations. Fewer young people, more older citizens. Extending vitality is not optional. Without it, economies will shrink, care systems will collapse, and civilizations will become brittle. Pig lungs—and the broader frontier of regenerative medicine - offer a lever to extend life, not as a luxury but as a necessity.

2. Economic Productivity: An aging but still-vital population is a productive one. Imagine a 75-year-old surgeon still practicing because her engineered organs keep her healthy. Imagine an 80-year-old teacher still in the classroom - healthspan makes it possible.

3. Strategic Resilience: In a century of pandemics, climate shocks, and geopolitical volatility, resilience is everything. If humans can replace failing biology at scale, civilization is less vulnerable to shocks. Pig lungs are not just organs - they are infrastructure.

4. Silicon vs. Biology: As AI advances, humans risk being outpaced by silicon intelligence while our biology decays. Engineering resilience into biology is how we maintain parity. The future cannot be machines racing ahead while humans falter behind their fragile organic limits.

The lesson is simple: civilization cannot afford timidity in science.

We must push xenotransplantation forward. Fund it, regulate it responsibly, and prepare for public backlash with a long-term perspective in mind.

We must invest in synthetic biology. This is not the endgame - eventually, we will grow fully synthetic organs from scratch. But xenotransplants are the bridge.

We must build public trust. We must learn from the mistakes of GMOs and vaccines. Transparency, storytelling, and education will decide whether this science thrives or dies in controversy.

We must aspire to build civilizational moonshots. What if we treated human vitality as a national priority, akin to the Apollo Program? Not just patching illness, but redesigning biology to withstand the century’s risks.

We cannot let fear of the unknown outweigh the cost of inaction. Every life lost on a waiting list is proof of our moral failure to push science forward.

An Iron Dome for Immunity will defend us. Engineered organs will sustain us. Together, they are not just medicine but are the scaffolding of civilization’s survival.

Breathing through a pig lung is much more than a medical curiosity. It is a preview of the future we must build: one where scarcity of biology no longer dictates the boundaries of human life.

Our task is straightforward: We must continue to push the boundaries of scientific experimentation - relentlessly, recklessly. Every breakthrough buys us resilience. Every risk embraced buys us time. And every breath taken through an engineered organ is a reminder that the future of humanity depends on our willingness to build it.

With belief,
Yon

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"Civilization advances by extending the number of important operations which we can perform without thinking of them."— Alfred North Whitehead

The Big Idea

In 2011, a rocket launched from Gaza was intercepted in the skies above Beersheba, a Southern city in Israel. It never landed. Civilians never scrambled for cover. The Iron Dome had done its job: detect, target, neutralize. What would have been a tragedy became just another moment in the day.

That was the birth of a new kind of infrastructure: a shield in the sky.

Now fast-forward. Imagine if the same principle applied not to rockets, but to pathogens. Instead of running clinical trials after the world has shut down, what if humanity had a standing defense system - a dome of immunity that could sense new threats, design countermeasures in days, and deploy them before the outbreak ever spread?

The pieces to make it happen exist: AI that designs molecules, mRNA platforms that can be reprogrammed like software, global sequencing networks, and decentralized bio-manufacturing. What’s missing is the system.

The next pandemic is not a matter of if, but when. And if the 20th century taught us anything, it is that civilizations that invest in defense survive. So the question before us is, will we build an Iron Dome for Immunity, or wait to count the bodies?

Why It Matters

COVID was a tragedy and a warning shot. Nearly 25 million excess deaths. $28 trillion in economic losses. Entire school years disrupted, families fractured, institutions discredited. And that was from a virus with a 1–2% fatality rate.

What happens when the next pathogen isn’t “mild”? What happens when it kills 10%, or when it is deliberately engineered to spread faster than we can respond?

We are building civilization on a foundation that is far too fragile. Supply chains, cities, and financial markets are all dependent on the assumption that biology behaves. That assumption is wrong. The brutal truth is that our posture today is reactive. We wait until people are sick, then scramble to respond. That is hope masquerading as strategy.

The Iron Dome succeeded because Israel recognized rockets as an existential threat. Pathogens are no less existential. Immunity must be treated as infrastructure, as essential as electricity, water, and the internet.

Lessons from Iron Dome

When Israel built Iron Dome, it was ridiculed. Too expensive. Too complex. Impossible to scale. And yet it worked, because of three principles:

1. Existential threat recognition. Survival was at stake. Delay meant death.

2. Political will. Billions were invested before success was certain.

3. Technological convergence. Radar, real-time computing, and precision interceptors aligned at the right moment.

The parallel today is frightening:

• We have had COVID, SARS, Ebola, H1N1 - all reminders that pandemics are not rare “black swans.” They are recurring events.

• Governments pledge “never again” but lose interest when the news cycle moves on.

• The tools - AI biology, programmable medicine, and decentralized manufacturing are converging right now.

The question is whether we will act.

A Potential Blueprint

Some ideas for what pillars need to be in place to enable an Iron Dome for Immunity. Not all of them are applicable or practical, but worth calling out:

1/ Sensors: Detecting the Threat

• Global genomic surveillance of wastewater, air, farms, and hospitals.

• Wearable biosensors capable of detecting infections before symptoms.

• AI epidemiology models that simulate spread in real-time.

2/ Interceptors: Neutralizing the Threat

• Rapid vaccine printers built on mRNA/DNA platforms.

• Programmable antibodies drawn from modular libraries.

• AI-designed antivirals generated in days, not years.

3. Command & Control: Orchestrating the Defense

• A global Immunity Command Center, integrating surveillance data and auto-designing countermeasures.

• Regional nodes are empowered to manufacture and deploy locally without waiting for sluggish bureaucracies.

4/ Deployment: Delivering the Defense

• Decentralized bio-foundries, as ubiquitous as 3D printers.

• Autonomous logistics networks for cold-chain and delivery.

• Immunity-as-infrastructure (routine, universal, reliable).

This is about building a system that ensures no pathogen, natural, accidental, or engineered, can take humanity by surprise again.

This is the very definition of existential innovation: one that doesn’t just make life better but ensures civilization endures.

Consider the threats:

• Natural spillover. Climate change is accelerating zoonotic transmission. The frontier between humans and animals is eroding.

• Synthetic accidents. Automated DNA synthesis is spreading. Mistakes will happen.

• Deliberate design. Bioweapons are no longer limited to nation-states. A determined graduate student could unleash chaos.

Against these, “better hospitals” are not enough. We need civilization-scale defense. Just as nuclear deterrence and missile defense shaped the 20th century, immunity defense will shape the 21st.

Who Builds It?

That’s the hard part. Governments must fund it - without state-level investment, there will be no global dome. Entrepreneurs must invent it - Startups like Moderna, Ginkgo, and Recursion have already proven that private actors can move faster than public bureaucracies. Global institutions must coordinate it - A NATO for Immunity?

History tells us something uncomfortable: governments will under-invest until after a catastrophe. Which means the first Iron Dome for Immunity may come not from Washington or Brussels, but from visionary founders and investors who refuse to wait.

Why Now

Three forces make this moment decisive:

1. AI in biology. What once took years (protein design, compound screening) might soon take weeks.

2. mRNA revolution. Biology has become software. The “semiconductor moment” for medicine is here.

3. Distributed manufacturing. Bioprinters and micro-factories make local, on-demand production feasible.

This is the convergence moment. Radar, rockets, software - but for biology. We can build this type of Dome today.

The Economics: COVID proved the cost of doing nothing: $28 trillion. We don’t hesitate to spend trillions on defense contracts for jets and submarines. Why not invest in the system that could decide whether humanity survives the next biological attack?

Now What?

We face a choice - Do we keep treating pandemics as once-in-a-century flukes? Or do we recognize them as recurring shocks that demand civilization-scale defense?

Do we wait until bodies pile up again? Or do we build the shield now? The responsibility is shared…

• Founders: Build at the frontier of AI, biology, and defense. Civilization needs you.

• Investors: Fund existential innovation that moves humanity forward.

• Policymakers: Treat immunity as strategic infrastructure.

• Citizens: Demand it. Don’t accept fragility as destiny.

The truth is brutal: the next pandemic is already incubating somewhere. It may emerge from a forest, a farm, or a lab. When it does, the only question that matters is: Did we build an Immunity Dome in time?

Civilizations rise and fall on their ability to defend against the forces that would undo them. The Iron Dome saved Israeli cities because visionaries acted before it was too late.

An Iron Dome for Immunity could save millions of lives.

With belief,
Yon

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With the ISS scheduled to retire around 2030, NASA is shifting to buy services from commercial low-Earth orbit stations rather than operate its own. This has kicked off a race:

• Vast’s Haven-1: Target launch in May 2026 via SpaceX Falcon 9. Small (≈45 m³) but designed for sovereign astronaut missions, research, and early in-space manufacturing.

• Axiom Space: Attaching modules to ISS now, evolving into an independent station later this decade.

• Starlab (Voyager + Airbus): Large single-launch station with strong NASA alignment and a broad partner network.

• Orbital Reef (Blue Origin + Sierra): Marketed as a “business park” in orbit but trailing peers in schedule.

NASA is relaxing requirements and speeding contracting to ensure at least one station is ready before the ISS retires.

Why It Matters

• First to orbit = market anchor: The first operational private station will secure national space programs, research institutes, and high-profile tourism customers. Vast’s Haven-1 2026 date is aggressive but strategically important.

• From “missions” to “infrastructure”: Commercial stations are the industrial backbone for permanent human presence in low-earth orbit, shifting space from occasional exploration to ongoing economic activity.

• Existential innovation platform: Microgravity accelerates breakthroughs in biotech, advanced materials, and radiation medicine—fields critical to human resilience both on Earth and in space.

• Public–private alignment: The CLD model (NASA funding + private operations) could be a blueprint for tackling other civilization-scale challenges - exactly the fusion of mission and market that I argued for in Space: The Final Frontier.

The next five years will decide who controls the operating system for human life in orbit, and whether it’s optimized for tourism, science, or industry.

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“Out of this industrial chaos... a new society was slowly forming.”
- Richard White, Railroaded

In 1873, Mark Twain and Charles Dudley Warner published a novel titled The Gilded Age: A Tale of Today - a satire about political corruption, wealth worship, and moral decay. The title stuck, and it defined an entire era of American history: roughly 1870 to 1900 [1].

The Gilded Age was a time of staggering transformation and searing contradiction. The U.S. economy grew fivefold between the Civil War and the turn of the century [2]; steel production jumped from 77,000 tons in 1870 to over 11 million by 1900 [3], and railroads expanded from 53,000 miles to over 190,000 miles of track [4]. More than 20 million immigrants arrived between 1870 and 1910 [5]. But inequality soared, child labor was rampant, urban poverty exploded, and corruption touched nearly every institution [6].

It was an age of titans: Andrew Carnegie, John D. Rockefeller, J.P. Morgan, Thomas Edison, Cornelius Vanderbilt - men who built foundational infrastructure while accumulating unprecedented wealth.

It was also an age of activists, reformers, and visionaries who saw through the gilding: Jane Addams pioneered urban social work. W.E.B. Du Bois and Ida B. Wells exposed racial injustice. Upton Sinclair sparked food reform. Frances Perkins, later the first female Cabinet member, witnessed the Triangle Shirtwaist fire and fought for labor protections [7].

The Gilded Age shaped America, but more importantly, it reshaped the very idea of modernity. And today, we find ourselves in a similar rupture. We may just call it the Second Gilded Age - a time of exponential innovation, institutional decay, and existential stakes.

1. Chaos is Fertile Ground

In the 1870s, the U.S. was still healing from the Civil War. Trust in government was crumbling. Labor unrest shook cities, and corruption defined politics - from the Crédit Mobilier scandal to the Whiskey Ring [8]. Yet amid the chaos, an entrepreneurial class emerged to build the scaffolding of the modern world.

Edison didn’t wait for permission to electrify New York.
Carnegie didn’t wait for Congress to bless steel monopolies.
Alexander Graham Bell didn’t seek regulatory clarity before inventing the telephone.

Lesson: Breakthrough eras are rarely clean. They’re messy, unstable, and morally ambiguous.

We now face a polycrisis - AI acceleration, demographic collapse, institutional distrust, climate shocks, and it’s easy to freeze. But if the Gilded Age teaches us anything, it’s that we shouldn’t wait for order. We must build through disorder.

2. Private Enterprise Can Drive Civilizational Progress - If Directed

Rockefeller’s Standard Oil controlled 90% of U.S. oil refining by 1880 [10]; Carnegie Steel was producing more steel than all of Great Britain by 1900 [11]; J.P. Morgan orchestrated the consolidation of railroads, steel, and even bailed out the U.S. Treasury during the Panic of 1895 [12].

Were they monopolists? Absolutely. But they also understood scale. They built systems across energy, steel, and finance that laid the foundation for the 20th century.

Carnegie later gave away over $350 million (over $10B today) to fund libraries, universities, and peace efforts [13], guided by his essay The Gospel of Wealth (1889), which argued that “the man who dies thus rich dies disgraced” [14].

Lesson: Today’s billionaires need more Gospel of Wealth moments. Fewer yachts.

Existential innovation will not be funded by philanthropy alone. It must come from mission-driven builders who treat capital not as an end, but as a means to uplift civilization.

3. Infrastructure is Destiny

In 1869, the U.S. completed the Transcontinental Railroad, a feat of engineering and political compromise that united coasts and catalyzed commerce [15].

By 1900, the telegraph connected continents in seconds; Public water and sanitation reforms halved urban death rates [16]; Land-grant universities seeded the scientific revolution to come; And department stores transformed consumption, and assembly lines were emerging [17]

These were civilization-scale platforms, not just innovations.

Today, we need new infrastructure:

Open-source AI safety frameworks
Human-compatible robotics systems
Synthetic biology regulation
Reproductive health platforms
Cross-border digital identity and learning protocols.

4. Institutional Lag is a Civilizational Risk

The Gilded Age was defined by institutional breakdown: Tammany Hall and local machine politics subverted democracy [18]; The Supreme Court repeatedly sided with capital over labor; And there were virtually no protections for workers, consumers, or the environment.

It wasn’t until the Progressive Era (1900–1920) that reforms began: antitrust laws, food safety regulation, child labor bans, and the direct election of senators [19].

Lesson: Innovation always outruns governance, but the current lag is existential.

If AI capabilities accelerate unchecked while policy sleeps, civilizational misalignment isn’t a possibility - it’s a guarantee. As builders, we can’t ignore this. We must design governance alongside progress.

5. Moral Imagination Shapes Reality

In 1906, Upton Sinclair’s The Jungle described the meatpacking industry in such visceral terms that within months, Congress passed the Pure Food and Drug Act and the Meat Inspection Act [20].

Lesson: The future moves on story, shame, and soul - not spreadsheets.

Existential innovation must be paired with existential imagination, a public reckoning with what we want from humanity’s next chapters, with builders as storytellers too.

If we’re not shaping the story, we’re already losing it.

6. The New Builders Were Not the Old Elite

Thomas Edison was homeschooled and self-taught. Nikola Tesla arrived in New York with four cents in his pocket. Carnegie started as a bobbin boy earning $1.20 a week [21]. They were hungry outsiders, not Ivy League incumbents.

Today’s builders will be similar:

A 19-year-old biohacker in Lagos
A high school dropout training AI agents in São Paulo
A mom rebuilding the fertility ecosystem from scratch
An open-source coder designing a constitution for synthetic minds.

Credentials are dead. Capability is king.
We must fund, support, and protect those who build.

7. Gilding is Not Golden

Mark Twain didn’t call it the Golden Age for a reason, as beneath the wealth lay exploitation:

• Children as young as 5 worked 14-hour shifts [22]

• Black Americans faced state-sanctioned racial violence in the Jim Crow South

• Indigenous lands were seized and tribes displaced by Manifest Destiny

• Women were shut out of most legal, financial, and political power [23]

Progress was real, but exclusion was structural.

Lesson: We can’t make the same mistake.
A world built on AI, robotics, and synthetic biology must dismantle injustice, not gild over it.

Now What: Builders of the Second Gilded Age

We are living through another foundational moment with AI reshaping cognition, biotech rewriting the body, and space, climate, crypto, and robotics re-architecting civilization itself.

But history is watching.

Will we repeat the mistakes of the past - concentrating power, ignoring morality, externalizing harm?

Or will we rise - bold, urgent, and clear-eyed, into a new age of human-centered innovation?

We must build like it matters, and we must fund worthy quests.

With belief,
Yon

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References

1. Twain, Mark and Warner, Charles. The Gilded Age: A Tale of Today (1873).

2. Gordon, Robert J. The Rise and Fall of American Growth, Princeton University Press, 2016.

3. U.S. Census Bureau Historical Statistics: Steel production, 1870–1900.

4. U.S. Department of Transportation, Bureau of Transportation Statistics.

5. U.S. Citizenship and Immigration Services, Historical Immigration Statistics.

6. Trachtenberg, Alan. The Incorporation of America: Culture and Society in the Gilded Age.

7. Lemann, Nicholas. Redemption: The Last Battle of the Civil War, 2006.

8. Summers, Mark Wahlgren. The Era of Good Stealings. Oxford University Press, 1993.

9. Yergin, Daniel. The Prize: The Epic Quest for Oil, Money, and Power.

10. Nasaw, David. Andrew Carnegie, 2006.

11. Chernow, Ron. The House of Morgan, 1990.

12. Carnegie Corporation Archives.

13. Carnegie, Andrew. The Gospel of Wealth, 1889.

14. Ambrose, Stephen. Nothing Like It in the World, 2000.

15. Cutler, David and Miller, Grant. “The Role of Public Health Improvements in Health Advances.” Demography, 2005.

16. Leach, William. Land of Desire: Merchants, Power, and the Rise of a New American Culture, 1993.

17. Shefter, Martin. Political Parties and the State, 1994.

18. Hofstadter, Richard. The Age of Reform, 1955.

19. Brandeis, Louis D. Other People’s Money and How the Bankers Use It, 1913.

20. Sinclair, Upton. The Jungle, 1906.

21. Nasaw, David. Andrew Carnegie, 2006.

22. U.S. Department of Labor Historical Studies.

23. Takaki, Ronald. A Different Mirror: A History of Multicultural America.

Further Reading:

• The Gilded Age: A Tale of Today by Twain & Warner

• Railroaded by Richard White

• The Gospel of Wealth by Andrew Carnegie

• The Bully Pulpit by Doris Kearns Goodwin

• The Warmth of Other Suns by Isabel Wilkerson

• The Jungle by Upton Sinclair

👋 Hello! My mission with Beyond with Yon is to help solve humanity's greatest existential challenges and advance the human condition.

Connect with me on Linkedin and X.

Thumbnail image credit: PBS.

Wanted to share something I’ve been digging into that’s completely captured my attention over the past few weeks, simply as a builder trying to make sense of the tectonic shifts happening around us.

It started with a simple question: what powers the next decade? I mean it quite literally. What’s the energy source behind the chips, the factories, the data centers, the robots, the machines that think? The deeper I looked, the more one answer kept showing up: nuclear. More specifically: uranium. And there’s a company that I think is critical in this transition, yet quite unknown in the broader market - Cameco.

We talk a lot about AI models, compute, and chips, but very few people are talking about the real constraint: electricity. Stable, around-the-clock, low-emission baseload power that can run hyperscale infrastructure without collapsing the grid. Eric Schmidt is one of the people who’s alerting on this need:

Nuclear energy is a critical method that can do this at scale.

It’s not a popular opinion in some circles, but it’s quickly becoming an institutional one. The EU reclassified nuclear as “green” last year [2]. The U.S. DOE committed $1.5 billion to HALEU fuel for advanced reactors [4]. And the private markets are waking up: Sprott, Yellow Cake, and a few major ETFs are buying up physical uranium.

And here’s the kicker: 40% of global uranium supply comes from Kazakhstan and Russia [1]. In a post-Ukraine-war world, that’s a national security vulnerability for the West.

So who might Western utilities and governments trust in supplying this critical material?

Cameco.

Cameco isn’t just a uranium mining company. They own two of the highest-grade uranium mines in the world - McArthur River and Cigar Lake. But more importantly, they’ve built a Western-aligned, vertically integrated fuel chain. They convert and refine uranium. They hold long-term supply contracts with utilities. And they own 49% of Westinghouse, a leader building different types of reactors, among them SMRs (small modular reactors), which could become the equivalent of cloud computing for the energy grid.

Here’s a couple of data points that captured my attention about Cameco:

• ~28M pounds of uranium per year are already under contract through 2029 [6]

• Cameco’s share of Westinghouse’s EBITDA expected to hit $525–580M in 2025 [6]

The stock might be considered overvalued when currently trading at ~20x EV/EBITDA, but the business has pricing power and structural scarcity.

Folks might think that it’s a commodity company but the real play is actually infrastructure. And the company has been doing pretty damn good in the past 5 years:

We're entering what I’d call the uranium awakening.

Electricity demand is about to explode. AI is a power hog, and the grid isn’t ready. Hyperscalers are already planning nuclear Power Purchase Agreements. Governments are accelerating SMR projects. And the only thing scarcer than chips might be the fuel needed to run the machines.

What I’m seeing in Cameco is a setup for a company that enables existential innovation, one that quietly underpins next-gen energy, in the same way TSMC underpins semiconductors or NVIDIA underpins AI.

This is part of a bigger shift that’s clearly coming - one where energy, industrial capacity, and national sovereignty become the real battlegrounds of innovation. And Cameco, strangely enough, is a company that sits right at that intersection.

Energy is a major constraint, and Cameco is a key player in enabling an energy abundance.

Thanks for reading,
Yon

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Sources

1. IEA – World Energy Outlook 2023

2. World Nuclear Association – Performance Report 2024

3. McKinsey – Powering the AI Revolution

4. U.S. DOE – HALEU Fuel Program

5. Sprott – Uranium Outlook 2025

6. Cameco Q2 2025 – MD&A, Investor Relations, Earnings Call

7. Fiscal.ai - financial data

Disclaimer

This content is for informational and educational purposes only. It does not constitute investment advice, an offer, or a solicitation to buy or sell securities. Always do your own due diligence or consult a licensed financial advisor before making investment decisions.

“Our vision is to bring personal superintelligence to everyone.”
— Mark Zuckerberg, Meta

TL;DR

Meta is trying to out-Apple Apple by fusing LLMs, wearables, and context-aware agents into a new category of personal AI. But calling it superintelligence is a stretch. And betting the future on glasses alone may be premature. The battle for the next computing platform is on, but it’s still wide open.

Zuckerberg’s latest announcement is more about claiming a new paradigm than it is about a product update.

With personal superintelligence as its new north star, Zuckerberg is framing the next wave of AI less as a general-purpose utility and more as an intimate extension of the self. Always on. Always aware. Always yours.

On one hand, it’s a bold and compelling vision. Zuckerberg has consistently communicated AI through a consumer-first lens, positioning Meta as the builder of practical, usable systems that help people live, create, and connect. In that way, it’s a natural extension of Meta’s previous mission of bringing the world closer together.

But here’s the rub: we don’t even have consensus on what AGI is, let alone ASI (Artificial Superintelligence). So to suggest we’re on the cusp of handing out “superintelligence” to the masses feels more like branding than reality. It muddies the waters at a time when clarity (on capabilities, risks, and timelines) is more important than ever.

Then there’s the hardware. Zuckerberg argues that intelligent glasses - i.e., devices that see what we see and hear what we hear - will replace the smartphone as our primary computing interface. That’s plausible. Meta has every reason to shape this category and beat Apple to the punch. Their Quest ecosystem gives them a head start in spatial computing. Their custom Llama models offer control over the AI stack (albeit they are now rebuilding their AI strategy). And Ray-Ban smart glasses are slowly seeding the market.

That being said, I’m not convinced the phone, as a primary handheld device, is going away anytime soon. In a world of ambient, agentic AI, a pocket device remains the ultimate remote control. It’s private, powerful, always with us. And any truly personal AI, whether glasses-based or not, will still need to integrate with this foundational node in our digital lives.

Thanks for reading.
Yon

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Because it shows something staggering: We’ve already passed the peak. The number of babies born on Earth each year is falling. And if the UN projections hold, that decline is only accelerating.

By the end of this century, there will be 30 to 40 million fewer babies born annually.

Let that sink in.

It’s easy to think of the year 2100 as a distant horizon. But it’s not. My daughter is four. She’ll be 79 in 2100. If she chooses to have children, they’ll grow up in a world where birth is no longer the default. Where societies shrink instead of grow. Where playgrounds are quieter. Where siblings are rarer. Where life feels… thinner.

We are heading into a world with fewer children.

Which means less joy.
Less chaos.
Less renewal.
Less future.

The implications are within our lifetime and our influence.

This global decline in birth rates is a slow-motion collapse of possibility. What’s at stake?

1/ A slowing economic engine.
Fewer workers. Fewer consumers. Greater pressure on younger generations to support aging populations. Growth becomes harder. Collapse becomes easier.

2/ The innovation engine dims.
Fewer young minds mean fewer breakthroughs, fewer rebellions, fewer dreams of something radically better.

3/ The human spirit wanes.
A world with fewer babies is a world that, in some deep way, has stopped believing in itself.

We’ve built an entire civilization around productivity, efficiency, and consumption. But we’re forgetting to preserve the source of it all: life.

What if birth is the new moonshot?

For the past century, we’ve waged war on death. Now, maybe it’s time to fight for life.

What if the most revolutionary thing we can do in the 21st century is make it easier (emotionally, physically, economically, culturally) to bring new life into the world?

That means:

Reinventing fertility care, so IVF and egg preservation are accessible, personalized, and stigma-free.

Redesigning childcare systems so that raising children doesn’t require financial sacrifice or professional stagnation.

Rethinking economic incentives so that families are supported rather than penalized.

Embracing future-forward technologies, like artificial wombs, longevity-linked fertility solutions, and real-time hormonal diagnostics.

Restoring a cultural reverence for parenthood, as a bold, creative act, not a burden

Can AI help us flatten the curve?

If we approach this right, the AI era isn’t the death knell of birth. It could be the spark that could reignite it.

Look, I’m an entrepreneur. I can’t help but ask: What do we build now?

So what if we could build…

AI Fertility Copilots

LLMs trained on millions of fertility journeys could help women and couples navigate their path with precision: cycle tracking, hormone interpretation, embryo grading, treatment options, and emotional support.
→ The category is wide open.

AI-Native Healthcare Platforms

Personalized reproductive health tools could offer diagnostics, predictions, and care plans far beyond what fragmented systems deliver today.
→ From endometriosis to egg freezing decisions, AI can collapse complexity into clarity.

Artificial Wombs + AI Monitoring

The development of ex utero gestation will rely on real-time feedback systems - think AI to monitor fetal development, manage conditions, and optimize outcomes.
→ A birth revolution where biology meets code.

AI-Powered Childcare and Companionship

Imagine intelligent, loving, always-on copilots for new parents. Tools that help with sleep training, emotional resilience, and developmental insights.
→ To amplify care, not to replace humans.

The future will be shaped by the tools we create, by platforms, by belief systems, by stories, not just by policies or incentives.

We should build startups that make fertility care radically affordable and intelligent. Platforms that help parents navigate the journey with AI as their co-pilot. Consumer electronics that enable better reproductive health tracking and emotional wellness. Systems that lower the cost and burden of caregiving. And cultural movements that reconnect society with the joy of birth.

That chart above blows my mind because it reveals something so subtle, so slow, and so terrifying: That, in a way, the future is getting smaller.

But it doesn’t have to be.

We can change that curve. We can write a different story, one filled with noise, with laughter, with siblings, with mess, with miracles.

We can build a world where bringing life into the world is not an economic liability, but a sacred act of optimism.

A world where fewer people ask “Can we afford a child?”
And more people ask, “What kind of future are we giving them?”

So we need to flatten the curve.
And it starts now.

With belief,
Yon

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👋 Hello! My mission with Beyond with Yon is to ignite awareness, inspire dialogue, and drive innovation to tackle humanity's greatest existential challenges. Join me on the journey to unf**ck the future and transform our world.

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