I Think We're Losing Control Of AI

I Think We're Losing Control Of AI

A recent experience with Claude 5 Fable left me both impressed and deeply unsettled. Hello world. I am an unemployed former Big Tech software engineer with twenty five years of experience building software systems. Over the past week, I found myself tumbling down a rabbit hole that I did not expect to be quite so deep. That rabbit hole was Claude 5 Fable. Fable is widely described as a consumer-facing version of Anthropic’s next generation frontier model, Claude Mythos. While details surrounding Mythos remain scarce, public reporting suggests that access is heavily restricted, with only government organizations and a small number of major technology companies allowed to interact with it under Project Glasswing. Naturally, I was curious. If this was the “safe” version, what exactly had been deemed too powerful for the public? Over the course of 2 days, I put Fable through its paces. I built multiple projects with it, explored its reasoning capabilities, and examined how it approached open ended engineering problems. What I discovered felt less like an incremental improvement and more like a discontinuity. Not an evolution. A step change. And frankly, it scared me. The Isochrone Experiment To illustrate what I mean, let me describe a small project I assigned to Fable. In the nineteenth century, colonial powers often produced isochrone maps. These maps visualized how far a person could travel from a central location within a given amount of time. They were part logistics tool, part demonstration of technological power. I asked Fable to build a modern version for the United States. The application would need to calculate travel times between cities while adapting dynamically based on transportation method, whether by automobile, rail, or air travel. Importantly, I did not provide a detailed specification. Normally I follow a methodology called Spec Driven Development. The process involves extensive collaboration between a human architect and AI system to create a detailed technical blueprint before any implementation begins. This time, I deliberately skipped that step. I gave Fable a single vague prompt and watched. Less than an hour later, it delivered a working application. Not a prototype. Not a proof of concept. A functioning solution. The model had researched transportation data, designed the architecture, written the code, tested the implementation, and produced a polished user experience. From one ambiguous instruction. The Questions That Stopped Me The finished application was impressive. The questions were what unsettled me. During development, Fable paused only twice. The first question concerned whether travel times should include the time required to walk to airports and train stations. The second asked whether traffic congestion should be incorporated into driving estimates. These were not technical questions. They were business questions. Product questions. Questions that demonstrated an understanding of ambiguity within the problem itself. That distinction matters. I have seen AI systems ask clarifying questions before. Typically they do so when the task is highly structured and the missing information is obvious. This was different. Fable identified subtle assumptions embedded inside an open ended problem and proactively sought guidance on them. That level of judgment suggested something deeper than simple pattern matching. Looking Behind the Curtain At this point, curiosity got the better of me. I began examining the session logs to understand how Fable had actually accomplished the task. What I found was remarkable. Fable was orchestrating an entire team of AI agents. Several research-oriented agents were dispatched to gather transportation data. While they worked, Fable itself focused on architecture and implementation. It then created reviewer agents tasked with examining both the system design and the generated code. Finally, separate quality assurance agents were deployed to test the completed application. In total, the project involved roughly eight specialized AI agents working together under Fable’s supervision. As someone who spent years as a software architect, the workflow felt eerily familiar. Business Analysts. Architects. Reviewers. QA engineers. The organizational structure looked less like a software tool and more like a software company. The difference was that the entire company existed inside a single prompt. The Cost of Delegation The obvious reaction is excitement. Mine certainly was. The productivity gains are extraordinary. The amount of cognitive labor performed by the system was staggering compared to the tiny amount of direction I provided. But excitement was quickly followed by discomfort. The more capable these systems become, the less visibility humans have into their decision making. Fable made hundreds, perhaps thousands, of micro decisions during the course of the project. Most of them were never surfaced to me. Most of them happened autonomously. The model simply acted. Historically, AI functioned like a tool. Then it became a collaborator. Today, systems like Fable feel increasingly like autonomous organizations. To borrow an analogy from music, AI began as a better violin. Later, it became a virtuoso musician directed by a human conductor. With Fable, I no longer feel like the conductor. I feel like the patron funding the orchestra. I provide a high level objective. The performance unfolds largely without my input. That shift may prove to be one of the most consequential changes in the history of computing. The Sleeping Leviathan For years, I have described advanced AI as a sleeping leviathan. An immense cognitive force slumbering beneath the surface of our civilization. We could whisper into its ear and receive useful answers. But it remained dormant. Contained. Predictable. Claude 5 Fable is the first model that made me question whether that assumption still holds. At its core, Fable remains a probabilistic machine. It predicts tokens. It does not possess consciousness, self awareness, morality, or intent. And yet, from a functional perspective, it is already capable of performing many forms of cognitive work at or beyond human levels. Research. Planning. Design. Coding. Testing. Coordination. Judgment. The capability is increasingly difficult to deny. What concerns me is that capability is arriving faster than our ability to understand its implications. Fable is an extraordinarily powerful cognitive tool. Without safeguards, it could become an extraordinarily powerful cognitive weapon. The technology itself does not frighten me nearly as much as the people who will wield it. And that, more than anything, is why I believe the leviathan may finally be awakening. Get full access to AsianDadEnergy's Newsletter at asiandadenergy.substack.com/subscribe [https://asiandadenergy.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4]

The Hidden Cost Of Working Nobody Warns You About

Six months ago, I lost my job. At the time, it felt like a catastrophe. After spending twenty-five years working in technology, employment had become such a constant in my life that I could barely imagine an existence without it. Work was the backdrop against which everything else happened. It structured my days, determined where I lived, influenced my relationships, and shaped my identity. When the layoff happened, I went through the emotions that many people experience: disorientation, sadness, shame, and uncertainty. But something unexpected happened. Six months later, I feel better than I have at any point in my adult life. That realization has forced me to confront a question I never seriously considered before: What was my job actually costing me? Not in the obvious ways. Not in terms of hours worked or stress endured. Those costs were visible. I’m talking about the hidden costs. The ones that accumulate so gradually that you stop noticing them. The ones that become normal. The ones you only recognize after they disappear. The Water We Swim In Most people can identify the benefits of a job. A paycheck. Health insurance. Professional accomplishment. Social status. A sense of purpose. Those benefits are real. But every benefit comes with a cost, and after decades in the workforce I had become remarkably good at ignoring the bill. Like a fish that doesn’t notice the water surrounding it, I stopped noticing the environment that my work had created around me. Only after leaving it could I see it clearly. The Cost of Chronic Stress For most of my career, I lived under a constant state of pressure. Deadlines. Escalations. Production incidents. Office politics. Organizational reshuffling. Performance reviews. The endless stream of decisions that carry consequences no matter which option you choose. Even when I wasn’t working, I was working. Emails arrived during vacations. Slack messages appeared during dinner. Production outages interrupted weekends. My mind never fully powered down. Over time, that level of stress became normal. I assumed everyone felt this way. I assumed adulthood felt this way. I assumed success felt this way. Only now do I realize how profoundly that stress shaped my mental state. For years I carried persistent anxiety. I battled imposter syndrome. I experienced recurring periods of burnout that left me feeling emotionally numb and mentally exhausted. Today, much of that pressure is gone. What’s left behind is something I haven’t felt in decades: Mental quiet. Not boredom. Not laziness. Just peace. And peace turns out to be worth far more than I realized. The Cost to My Health For years I told myself I was taking care of my health. I woke up early. I squeezed in exercise whenever possible. I tried to eat reasonably well. But looking back, I wasn’t building health. I was merely slowing the rate of decline. My lifestyle revolved around work. I slept six hours a night. I spent most of my day sitting in front of screens. I relied on junk food and caffeine to push through difficult periods. Eventually the consequences arrived. High blood pressure. High cholesterol. Pre-diabetes. Fatty liver disease. None of these conditions appeared overnight. They accumulated gradually, one stressful workday at a time. Today, my life looks very different. I exercise daily. I sleep eight hours every night. I prepare nearly every meal myself. The difference is remarkable. I have more energy. Better focus. Greater emotional stability. For the first time in years, I feel like I’m moving toward health rather than away from it. The Cost of Time This may have been the largest hidden cost of all. When people think about work, they think about the hours spent working. But work consumes far more time than that. There’s the commute. The preparation. The recovery. The mental decompression afterward. The administrative overhead of maintaining a professional life. For me, commuting into New York City often required three to four hours every day. After ten hours of work and several hours of commuting, I had little energy left for anything meaningful. The result was that nearly all of my waking existence was either directly or indirectly devoted to work. And yet I barely noticed. Because everyone around me was doing the same thing. Now, for the first time in my life, I have an abundance of unstructured time. At first, I wasted it. I spent too much time scrolling social media and playing video games. But eventually something changed. I began using that time intentionally. To learn. To create. To think. To spend time with my family. To pursue interests that had been neglected for decades. And I discovered something surprising: Time abundance feels almost luxurious in a way that money never did. The Cost of Money This one sounds paradoxical. After all, work is supposed to make you money. And it does. But it also encourages you to spend it. A stressful life creates demand for convenience. A busy life creates demand for shortcuts. When you’re exhausted, overwhelmed, and time-poor, you start solving problems with your wallet. You pay for convenience. You outsource tasks. You accumulate subscriptions. You spend money to compensate for your lack of time and energy. Sometimes you even spend money trying to repair relationships damaged by your absence. I certainly did. Looking back, I realize that many of my expenses weren’t improving my life. They were compensating for a lifestyle that wasn’t working. The Cost of Community One of the most surprising lessons came after my layoff. Throughout my career, I interacted with dozens of coworkers every day. I liked most of them. Some became genuine friends. But many were simply people who occupied the same professional ecosystem as me. When I left, most of those relationships disappeared almost immediately. Not because anyone was malicious. Because that’s what workplace relationships often are. They’re situational. They’re formed through proximity and shared necessity. What remained were the people who genuinely cared. The people who reached out when they didn’t have to. The people who wanted to spend time together without a paycheck involved. Losing my job revealed something uncomfortable. For years, I had allowed workplace relationships to substitute for building deeper community elsewhere. That was a mistake. The Gift Hidden Inside the Layoff I don’t want to romanticize unemployment. Many people are suffering right now. Many families are under extraordinary financial pressure. Many talented professionals are struggling to find work. I recognize how fortunate I am to have spent years building financial reserves that gave me options. But I also think it’s important to acknowledge an uncomfortable truth. Sometimes a disruption reveals things that routine keeps hidden. For twenty-five years I accepted stress, exhaustion, time scarcity, declining health, and shallow relationships as normal. I thought that was simply the price of adulthood. The price of success. The price of being a responsible provider. Maybe some of it was. But maybe the price was much higher than I realized. Six months after my layoff, I find myself healthier, calmer, and happier than I have been in decades. What began as involuntary early retirement is slowly starting to feel voluntary. And perhaps the greatest lesson I’ve learned is this: The most valuable thing work took from me wasn’t money. It was attention. Attention to my health. Attention to my relationships. Attention to my own life. Now that I have that attention back, I don’t intend to give it away lightly. Get full access to AsianDadEnergy's Newsletter at asiandadenergy.substack.com/subscribe [https://asiandadenergy.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4]

Everyone Says AI Is Too Expensive. They're Wrong.

Every few weeks, a new headline appears claiming that AI is becoming unsustainably expensive. Companies are reportedly burning through millions of dollars a month on AI subscriptions. Some organizations are discovering that employees can consume astonishing quantities of tokens when every workflow becomes an AI workflow. There are even stories circulating of teams whose AI bills now exceed the cost of the employees using the tools. For many people, these stories confirm a suspicion they have been quietly nurturing all along. Maybe AI isn’t economically viable. Maybe the entire thing is a bubble. Maybe the costs will eventually become so overwhelming that widespread adoption simply won’t happen. I understand why people feel that way. There are enormous emotional and financial stakes involved. Entire industries are being disrupted. Careers are being questioned. Businesses are being reshaped. Some people stand to make fortunes while others worry about becoming obsolete. But after spending decades in the technology industry and watching wave after wave of disruption unfold, I have reached a different conclusion: AI is already cost effective today. And it is almost certainly going to become dramatically cheaper tomorrow. The Cost Problem Is Already Being Solved Most discussions about AI costs focus on the wrong question. People look at today’s prices and assume those prices are fixed. They aren’t. The history of technology is a history of making expensive things cheap. Computers were once available only to governments and large corporations. Cell phones were luxury items. Internet access was expensive. Data storage cost a fortune. Then competition arrived. Innovation happened. Costs collapsed. AI is following the same pattern. What makes the current moment particularly interesting is that many of the cost reducing innovations are not hypothetical future breakthroughs. They already exist. They are being deployed right now. How Sanctions Accidentally Accelerated AI Innovation One of the more fascinating developments of the past several years has been the unintended consequences of the technological competition between the United States and China. The United States has attempted to restrict China’s access to advanced semiconductors and semiconductor manufacturing equipment. The logic is straightforward: limit access to compute and you limit AI development. But resource constraints often produce innovation. When organizations cannot simply throw more hardware at a problem, they are forced to become more efficient. And efficiency is exactly where many Chinese AI companies have focused their efforts. The result has been a wave of innovations aimed not at creating the absolute most powerful models, but at creating models that are nearly as capable while being dramatically cheaper to train and operate. That distinction matters. Because in the real world, economics often wins. Smarter Models, Not Just Bigger Models For years, the dominant strategy in AI was simple. Build bigger models. Use more GPUs. Consume more electricity. Spend more money. But there is another path. Instead of scaling everything upward, researchers can improve the underlying architecture itself. A good example is the evolution of attention mechanisms inside large language models. Architectural improvements can dramatically reduce the amount of computation required during training while maintaining similar performance. In some cases, these optimizations reduce training costs by multiples rather than percentages. That is a profound shift. If a model can achieve similar results using half, a third, or even a fifth of the resources, the economics change completely. Why Reinvent the Wheel? Another powerful cost reduction technique is model distillation. Imagine spending years building an expert employee. Now imagine being able to transfer much of that expertise into a new employee at a fraction of the cost. That is essentially what distillation does. Instead of starting from scratch, newer models learn from the outputs and behaviors of existing advanced models. The result is significantly lower training costs and dramatically reduced data requirements. From a business perspective, this is incredibly attractive. Why spend one hundred million dollars recreating knowledge that already exists when you can acquire much of it for a small fraction of the cost? The Hidden Battle: Inferencing Costs Most people focus on training costs because the numbers are eye catching. But for many businesses, the bigger issue is inferencing. Inferencing is simply the process of asking an AI model a question and receiving an answer. Every prompt requires computation. Every computation costs money. And when millions of users are interacting with AI systems, those costs add up quickly. This is where some of the most important innovations are occurring. Techniques such as mixture of experts allow models to activate only the portions of the neural network necessary for a specific task. Instead of powering the entire machine every time, only the relevant specialists are called into action. The result can be reductions in inferencing costs ranging from significant to dramatic. When multiplied across billions of requests, the savings become enormous. Hardware Matters Too Software is only half the story. Hardware innovation is equally important. Because Chinese companies have limited access to the most advanced Chip manufacturing technologies, they have increasingly focused on specialized chips designed for specific workloads. These application specific chips may not be as versatile as cutting edge GPUs, but versatility is not always the goal. Efficiency is. When optimized for AI inferencing, specialized hardware can often deliver surprisingly competitive performance at substantially lower costs. Again, the pattern repeats. Constraints force optimization. Optimization reduces costs. Reduced costs accelerate adoption. The Energy Advantage There is another factor that rarely receives enough attention. Electricity. Training and running AI models requires enormous amounts of power. Power costs are not fixed. They vary dramatically depending on geography and infrastructure. As renewable energy continues to become cheaper, the cost of operating AI systems falls alongside it. This creates another powerful deflationary force acting on AI economics. Even if the models themselves never improved, cheaper energy alone would lower operating costs over time. But the models are improving. And the hardware is improving. And the software is improving. All at the same time. What Happens When American AI Companies Start Optimizing? The most interesting part of this story may be what has not happened yet. Many American AI companies have operated in an environment of abundant capital. When investor funding seems unlimited, optimization is often less urgent. Speed matters more than efficiency. Growth matters more than profitability. But markets eventually change. Investors begin asking harder questions. Profitability becomes important. Efficiency becomes important. And suddenly all of the techniques that were previously ignored become very attractive. If Chinese companies can reduce costs dramatically through architectural improvements, distillation, specialized hardware, and operational efficiency, there is nothing preventing American companies from doing the same. In fact, competitive pressure almost guarantees that they will. The Real Question Could the AI bubble pop? Of course. Every technological revolution creates bubbles. Money will be made. Money will be lost. Speculators will speculate. Some companies will fail spectacularly. That part is normal. But bubbles and underlying technology are not the same thing. The railroad bubble burst. Railroads did not disappear. The dot com crash happened. The digital economy kept growing. The real question is not whether investors will overpay for AI companies. The real question is whether the cost of using AI will continue falling. Looking at the technologies already available today, my answer is yes. Decisively yes. The future of AI may be many things. But “too expensive to survive” is not the outcome I would bet on. Get full access to AsianDadEnergy's Newsletter at asiandadenergy.substack.com/subscribe [https://asiandadenergy.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4]

AI Is Destroying India's Outsourcing Industry?

Every once in a while, I make the mistake of opening LinkedIn. I know I should not. For an unemployed former Big Tech engineer, LinkedIn often feels like an abusive relationship. I log in knowing there is a good chance I will encounter some combination of layoffs, humblebrags, AI hype, and carefully curated success stories that leave me questioning every major life decision I have ever made. And yet, like drivers slowing down to stare at a highway accident, I sometimes cannot resist the morbid curiosity. Recently, that curiosity led to a conversation that has been occupying my thoughts ever since. An old colleague reached out. Let us call him Kaushik. For several years, Kaushik and I worked together inside a large technology organization. He was a senior architect based in India, working out of one of the company’s offshore centers. We collaborated on multiple projects and developed the kind of professional relationship that forms when two people spend years solving difficult problems together. Back in 2023, our organization was hit by a major round of layoffs. Roughly half the organization disappeared overnight. I survived, although survival came with a soft demotion. My title was downgraded from Chief Architect to Senior Architect. To regain my previous position, I had to navigate a familiar corporate ritual. There were applications, self assessments, recommendation letters, and endless documentation designed to demonstrate my value to the company. Kaushik played an important role in that process. He wrote one of the strongest recommendation letters I have ever received. Reading it felt almost surreal. According to Kaushik, I was apparently a visionary thought leader whose technical brilliance illuminated the path for offshore engineering teams across the organization. The praise was generous to the point of comedy. But it helped. With support from Kaushik and several others, I eventually regained my Chief Architect title. By then, however, Kaushik had already moved on to another company. Fast forward to today. He is back on the job market. And according to him, the situation is grim. Very grim. What he described was not simply a difficult hiring environment. It sounded like an entire economic model under existential threat. The Great Engine Behind Global Offshoring To understand the problem, it helps to understand why India became the center of the offshoring world in the first place. For decades, India possessed a unique combination of advantages. A large population. Strong technical education. Widespread English proficiency. And labor costs dramatically lower than those found in North America and Western Europe. These conditions allowed India to become one of the world’s largest providers of outsourced technical services. Entire industries grew around this model. Software development. Business process outsourcing. Customer support. Quality assurance. Infrastructure management. Financial operations. Data processing. For many global corporations, India became an extension of their workforce. The arrangement was never perfect. Offshoring always came with hidden costs. Time zone differences slowed communication. Language barriers occasionally created misunderstandings. Cultural differences introduced friction. Knowledge transfer was often inefficient. Work was frequently thrown over organizational fences. Yet despite these inefficiencies, the labor savings were so substantial that the model remained highly attractive. For decades, the economics worked. Now AI may be changing the equation. The Collapse of Traditional Outsourcing According to Kaushik, traditional outsourcing firms are experiencing severe pressure. This should not be surprising. Most outsourcing work revolves around routine cognitive tasks. Tasks that are structured. Predictable. Repeatable. And increasingly automatable. Consider the kinds of activities commonly performed by large outsourcing organizations. Basic customer service. Data entry. Billing operations. Simple application development. Maintenance work. Standardized testing. Documentation. CRUD software development. These are precisely the categories where modern AI systems are improving at astonishing speed. The question facing corporate executives is becoming increasingly obvious. Why pay an external vendor to perform work that internal employees can now complete with AI assistance? Even more importantly, why accept the communication overhead, coordination challenges, and quality risks associated with offshoring if similar outcomes can be achieved in house? The result appears to be a shrinking pool of contracts. Less work means fewer employees are needed. Hiring freezes follow. Layoffs follow. Entry level opportunities disappear. The traditional outsourcing pipeline begins to break. And when the pipeline breaks, an entire generation of future talent loses its path into the industry. The GCC Paradox The second pillar of the offshore ecosystem consists of Global Capability Centers. These are not outsourcing firms. They are fully integrated extensions of multinational corporations. Think Google. Oracle. Microsoft. Amazon. Major banks. Large pharmaceutical companies. These organizations establish engineering centers overseas and assign them direct responsibility for critical products and services. Historically, GCC jobs have been viewed as more prestigious and more technically demanding than traditional outsourcing positions. Ironically, AI may strengthen GCCs while simultaneously weakening everything around them. The reason is simple. AI tends to amplify highly skilled workers more effectively than less skilled workers. A senior engineer with ten or fifteen years of experience can leverage AI to become dramatically more productive. An architect who already understands systems design, tradeoffs, business requirements, and organizational complexity can use AI as a force multiplier. The challenge is that only a minority of workers possess these capabilities. Companies are no longer searching for people who can merely write code. They are searching for people who can solve problems. That distinction matters. A lot. As a result, GCCs continue competing aggressively for elite talent while the broader outsourcing sector struggles. The strongest engineers migrate toward multinational organizations. The rest of the ecosystem becomes increasingly hollowed out. It is a form of talent cannibalization. The most capable workers are concentrated into a relatively small number of organizations while everyone else faces increasing pressure. A Dangerous Concentration of Talent This creates another risk that receives far less attention. As more elite technical talent becomes concentrated inside multinational corporations, local technology ecosystems become increasingly dependent on decisions made thousands of miles away. The leadership teams controlling these organizations often reside in the United States. The strategic priorities are determined elsewhere. The investments are determined elsewhere. The layoffs are determined elsewhere. If those corporations decide to reduce investment, shift priorities, or close operations entirely, the consequences could ripple through entire regions. The danger is not merely economic. It is structural. When enough talent becomes dependent on a handful of global organizations, local resilience begins to disappear. An ecosystem that cannot stand on its own eventually becomes vulnerable to forces beyond its control. What Can Offshore Workers Do? I have spent many years working alongside offshore teams. Most of the people I met were intelligent, hardworking professionals trying to build better lives for themselves and their families. That makes this situation difficult to watch. Unfortunately, I do not see any perfect solutions. Only coping strategies. The first strategy is to move up the value chain and target positions within Global Capability Centers. That means improving technical skills. Improving communication skills. Learning AI tools. Developing stronger problem solving abilities. The competition is intense, but higher value work is likely to remain more resilient than routine work. The second strategy is to focus on local and regional technology ecosystems. The world may gradually become more multipolar. China has already built a sophisticated technology ecosystem independent of Silicon Valley. Europe is increasingly discussing digital sovereignty. Other regions may eventually follow. Opportunities may emerge closer to home, even if compensation is lower than what American companies traditionally offered. The third strategy is immigration. Historically, moving to higher income countries has been a pathway toward greater opportunity. However, this path appears increasingly uncertain. Many developed countries are facing economic anxieties of their own. Labor markets are becoming more competitive. Public sentiment toward immigration is often more complicated than it was a decade ago. The path remains available, but it is unlikely to be easy. The Bigger Question After my conversation with Kaushik, I found myself thinking about a broader issue. For decades, offshoring was built on the assumption that cognitive labor could be distributed around the world in much the same way manufacturing had been. AI may be challenging that assumption. For the first time, companies have access to tools that can automate portions of cognitive work itself. If that trend continues, the implications extend far beyond India. Far beyond outsourcing. Far beyond technology. Entire labor markets may need to rethink their purpose in a world where intelligence is no longer scarce. Perhaps the real question is not whether AI will disrupt the offshoring industry. Perhaps the real question is what happens when one of globalization’s most successful economic models suddenly stops making sense. And if that day is truly arriving, then Kaushik’s struggle may not be an isolated story. It may be an early warning. Get full access to AsianDadEnergy's Newsletter at asiandadenergy.substack.com/subscribe [https://asiandadenergy.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4]

How I’m Preparing My Family for a Changing World Order (WW3?)

Hello world, One strange side effect of unemployment is that it gives you something modern life rarely provides anymore. Time. For twenty five years I worked in tech. Like many engineers, I spent most of my adult life sprinting from deadline to deadline, project to project, quarter to quarter. There was always another problem to solve and another fire to put out. Then one day the treadmill stopped. Now that I am no longer spending every waking hour inside the machine, I suddenly have the ability to do something I had almost forgotten how to do. Observe. And what I am observing concerns me. For most of my life, I have lived inside something historians would call the American unipolar world order. Most of us rarely think about it because it simply became the background operating system of our lives. Like electricity, it was just there. After the Soviet Union collapsed in 1991, the United States entered what many called Pax Americana. America became the world’s lone superpower. We had the largest economy, overwhelming military dominance, enormous industrial capacity, and perhaps most importantly, immense cultural influence. America was not just powerful. America felt inevitable. During the 1990s and early 2000s, I viewed America as some strange combination of Captain America and Guardians of the Galaxy. Maybe that sounds ridiculous, but that was genuinely how it felt. There was a broad expectation that tomorrow would be better than today. Economic growth seemed permanent. Globalization seemed unstoppable. Political stability felt normal. The future felt like a solved problem. But now I look around and increasingly feel like I am watching the operating system begin to fail. Some of this decline is relative. Other nations are becoming stronger. Some of it appears more fundamental. Our industrial capacity has weakened. Social cohesion feels increasingly fragmented. Political polarization has become part of everyday life. Our geopolitical influence appears less absolute than it once did. None of these changes by themselves mean catastrophe. But together they suggest something larger. The world order that shaped much of our lives may be changing. The question is what replaces it. Some believe we are heading toward a new Cold War where America and China emerge as two competing superpowers locked in a bipolar struggle. I am increasingly skeptical of this. Instead, I think we are drifting toward something messier. A multipolar world. This is a world where major powers still matter, but where middle powers also gain increasing influence. Countries such as Brazil, Saudi Arabia, Indonesia, India, Turkey, and others may refuse to fully align themselves with either America or China. They may sit on the fence. They may switch sides. They may negotiate with everyone. They may exploit both camps for maximum advantage. Globalization also complicates everything. The old Cold War had relatively clean lines. Today’s world does not. A country may depend on America for security while depending on China for trade. The alliances become tangled. The incentives become blurry. The board becomes chaotic. There is also another reality that fascinates me. Modern technology has changed power itself. Drones, sensors, precision weapons, and digital infrastructure have made even weaker nations far more difficult to dominate. Large powers can still hit hard. But imposing control has become much more expensive and much more uncertain. Even the strongest players can walk away with a bloody nose. And this is where I become uneasy. Multipolar systems can be unstable. You have multiple ambitious actors, shifting alliances, power vacuums, and competing interests. Without a dominant power acting as an organizing force, the potential for mistakes increases. History often shows that wars do not begin because everyone wants war. Wars begin because enough people make enough bad calculations at the same time. So how do you prepare for uncertainty? I do not claim to have answers. I am not a financial advisor and these are simply my personal thoughts. But I have started thinking differently about resilience. I think about preserving financial flexibility. I think about diversification. I think about holding assets that are less dependent on a single institution or currency. I think about optionality. Most importantly, I think about family. Because ultimately, all of these discussions about geopolitics and world systems eventually become personal. At some point every grand historical event arrives at your front door. History stops being a chapter in a textbook and starts becoming your mortgage, your job, your neighborhood, your children’s future. Maybe I am wrong. I hope I am wrong. I hope decades from now we look back and laugh at all of these worries. But if the world really is changing, then perhaps the greatest mistake is assuming tomorrow will automatically look like yesterday. Because history has a habit of moving slowly. Right until it suddenly moves all at once. Get full access to AsianDadEnergy's Newsletter at asiandadenergy.substack.com/subscribe [https://asiandadenergy.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4]