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

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]

The Real Reason They're Racing To Build AI?

The Future That Quietly Keeps Me Up at Night Hello world, For the first time in more than two decades, I suddenly found myself with something I hadn’t had in years: Time. After spending 25 years as a software engineer in big tech, I entered what I jokingly call my “involuntary early retirement.” And when your daily rhythms disappear, your mind starts wandering into strange places. Mine wandered into the future. Not my future specifically, but humanity’s future. Like many engineers, I have always believed that technology is fundamentally a tool. A hammer can build a home or become a weapon; the hammer itself has no morality. Technology seemed no different. Artificial intelligence, robotics, biotechnology, these were simply instruments extending human capability. But the more time I spent reading, researching, and following emerging technological trends, the more a question began nagging at me: What if we are no longer merely building tools? What if we are building our successors? That question led me into the world of Transhumanism. For those unfamiliar with the idea, Transhumanism is a philosophical movement advocating the use of advanced technologies to fundamentally enhance humanity itself. The goal is not merely to cure disease or make life more comfortable. The goal is to overcome biological limits altogether. Disease. Aging. Cognitive limitations. Possibly even mortality itself. For decades, this sounded like science fiction, the sort of thing reserved for late-night conversations between futurists and authors. Today it feels different. Because the technologies that make this possible are no longer imaginary. Brain-computer interfaces now connect neurons to machines. Genetic technologies such as CRISPR allow us to edit the very code of life itself, precisely. Artificial intelligence increasingly performs tasks that once required human expertise. Robotics grows more capable every year. Individually, each technology seems understandable. Collectively, they begin to feel transformative. Potentially civilization-transforming. And here is where things become unsettling. Because if these enhancements become possible, they will almost certainly begin as expensive technologies available only to a small number of people. Perhaps the wealthiest. Perhaps the most powerful. Perhaps the descendants of today’s technological elite. What happens then? Imagine two groups emerging within humanity itself. Not nations. Not races. Not classes. Species. One group possesses enhanced intelligence, longer lifespans, superior biological capabilities, and direct integration with AI systems. The other remains largely unchanged. How long would those groups remain equals? History offers a sobering answer: they probably wouldn’t. Humans have never had an especially impressive record of treating less powerful groups as peers. And if one group genuinely became more capable, stronger, smarter, longer-lived, the incentives become uncomfortable to think about. The enhanced population might eventually ask difficult questions: Why sustain billions of unenhanced humans consuming resources? Why preserve inefficiencies? Why maintain systems built for biological limitations that no longer apply? I know how insane this sounds. Trust me, I hear myself saying it. But what makes this thought experiment disturbing is not the futuristic imagery. It’s the possibility that we may already be seeing early hints of these pressures emerging. Consider the enormous expansion of AI. Hundreds of billions of dollars are being poured into data centers, chips, energy infrastructure, and computational power. The public narrative is productivity. Efficiency. Innovation. And perhaps that is entirely true. But another possibility exists: The systematic reduction of the need for human labor itself. If labor becomes less valuable, then what happens to people? We may already be seeing fragments of that answer. Mass layoffs. Shrinking opportunities for younger workers. An economy where many increasingly rely on gig work, subsidies, and algorithmically mediated systems simply to survive. Meanwhile, social structures that once stabilized human life, families, communities, churches, neighborhoods, appear weaker than they once were. Instead, many people increasingly exist within digital ecosystems designed to capture attention. We become consumers of endless content. Endless outrage. Endless distraction. And perhaps the most striking consequence is demographic. Across much of the industrialized world, birth rates are collapsing. Young people aren’t rejecting families because they hate children. Many simply cannot imagine stable futures for themselves. If life increasingly feels like survival, building the next generation becomes difficult. East Asian nations may be offering a glimpse into this future. Population projections in some regions suggest declines so severe they would have seemed unimaginable just decades ago. Canaries in the coal mine. Which raises a haunting possibility: What if these aren’t disconnected trends? What if they are pieces of a larger transition? Imagine the year 2100. AI and machines perform most productive work. A small enhanced population controls technological systems and resources. A larger population of ordinary humans receives sufficient resources to survive, perhaps through mechanisms like universal basic income, but exists largely dependent upon the system itself. From the outside, this civilization might look beautiful. Clean cities. Renewable energy. Little pollution. No visible poverty. Almost a solar-punk paradise. A Star Trek future. But beneath the surface lies a difficult question: If basic material needs are met, but human agency disappears, is that still freedom? I don’t claim that this future is inevitable. I don’t even claim it is likely. I may be completely wrong. I sincerely hope I am. But history suggests civilizations often drift into destinations they never consciously intended to reach. Not because of a master plan. Not because of hidden conspiracies. But because countless incentives quietly push society in one direction over time. Perhaps Transhumanism will ultimately free humanity from suffering. Or perhaps it will simply create a newer, more technologically sophisticated dystopia. I don’t know. I only know that the question itself has become difficult for me to stop thinking about. And maybe that’s the point. The future rarely arrives all at once. It arrives gradually one technology, one incentive, one compromise at a time. 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 Tech Layoff Crisis No One Wants to Talk About

Another week passes and another wave of layoffs crashes through the technology industry like a tidal wave. At this point, the disappearance of thousands of highly experienced engineers has become so common that it barely shocks anyone anymore. Entire departments vanish overnight. Decades of institutional knowledge disappear behind a carefully worded email and a severance package. But there is another group of people affected by these layoffs that nobody really talks about. The survivors. The people who remain employed are often viewed as the lucky ones. They still have a paycheck. Their stock grants are still vesting. Their LinkedIn profiles still say they work at a prestigious company. From the outside, they appear safe. But inside many large technology companies, surviving layoffs can feel like becoming trapped inside a pressure cooker. The workload grows heavier while job security becomes thinner. Teams shrink while expectations expand. Critical systems still need to function, deadlines still need to be met, and billions of dollars still depend on software that was often stitched together over decades by engineers who no longer work there. The survivors inherit all of it. In the summer of 2023, I found myself inside exactly this kind of situation. After a brutal round of layoffs at my company, nearly half of my organization disappeared. Senior leaders were gone. Teams were forcibly merged together into a larger organization built from the wreckage of the previous one. I went from serving as a Chief Architect to functioning as a Senior Enterprise Architect again. Professionally, it felt like traveling backward in time. I was angry about it. But I stayed. Like many people in tech, I had financial reasons to endure it. My next round of RSUs had not vested yet, and walking away meant leaving a significant amount of money on the table. So I convinced myself to keep pushing forward. Then came the project that nearly broke me. Our company operated a massive legacy platform that handled relationships with partner companies. The system processed billions in annual revenue, but under the surface it was a digital Frankenstein monster. Over twenty five years, dozens of separate web applications had been piled on top of one another until the entire thing barely functioned. Different fonts. Different navigation systems. Different visual styles. Some pages looked like they belonged to completely different companies. Yet somehow this fragile structure continued to support an enormous stream of revenue. Executive leadership decided that our newly reorganized department would completely replace this legacy system with a modern enterprise CRM platform in a single release scheduled only four months away. It was the kind of decision that sounds bold in a PowerPoint presentation and terrifying to the engineers responsible for actually delivering it. The challenge was not merely technical. The layoffs had already gutted the teams that understood how the legacy platform worked. Much of the institutional knowledge had vanished. At the same time, the replacement CRM platform required specialized knowledge that very few people possessed. So every day became a race against time. I spent countless hours trying to understand both systems simultaneously while also coordinating teams spread across multiple continents. Meetings started at six in the morning and stretched late into the evening because our squads were distributed between North America and India. Then there was the commute. Our company enforced a hybrid return to office policy that required me to travel into New York City every other day. The round trip took roughly three hours by bus. By the end of each commute, I often felt physically nauseated from the constant swaying motion. At one point, I realized I was regularly working more than twelve hours a day while also sacrificing weekends to keep the project alive. That is when the burnout truly began. People often describe burnout as stress, but burnout feels different. Stress still contains energy. Burnout feels like the complete absence of it. I felt mentally foggy all the time. Concentration became difficult. Solving technical problems that once felt routine suddenly required enormous effort. I forgot details. I lost focus. Even after taking several days off during Labor Day weekend, I returned to work still feeling exhausted. Emotionally, something stranger happened. I stopped caring. Projects that once would have energized me now felt hollow and meaningless. I became detached from the work, detached from the teams, and in many ways detached from myself. Sunday evenings filled me with dread. Then the nightmares started. Over and over again, I dreamed that my coworkers and I had somehow become low wage restaurant workers. The product manager became the greeter. The Chief Architect became a busboy. The engineering manager became the dishwasher. I was always the waiter. And in every dream, disaster struck. A customer would die after eating spoiled food. The restaurant would catch fire. Chaos would erupt. Every single time I woke up drenched in sweat. Looking back now, I think my subconscious was trying to tell me something important. Burnout does not simply exhaust the body. It destabilizes your sense of identity and security. It transforms your career from a source of meaning into a source of survival anxiety. Eventually I realized that if I continued living this way, something inside me was going to break permanently. So I made changes. I forced myself to sleep consistently. I stopped scrolling through devices late at night and began prioritizing seven hours of uninterrupted sleep. I exercised every morning, even if only for thirty minutes on a treadmill. That small amount of movement changed my mental state far more than I expected. I intentionally reconnected with people outside of work including family, friends, church groups, and online gaming communities. These interactions reminded me that my existence extended beyond corporate deadlines and Jira tickets. Most importantly, I began enforcing boundaries. I stopped working weekends. I stopped responding to messages at all hours. On office commute days, I refused early morning and late night calls. At first, saying no felt uncomfortable. Then it felt liberating. Over several weeks, the nightmares stopped. The anxiety softened. My concentration improved. I became functional again. Not perfect. Not fully recovered. But functional enough to finish the project and survive the experience. The modern technology industry celebrates resilience almost obsessively. We glorify hustle culture, constant availability, and productivity at all costs. But there is a dangerous difference between resilience and self destruction. A human being is not a distributed system designed for infinite horizontal scaling. Eventually the system crashes. And increasingly, I think many engineers are approaching that point simultaneously. The layoffs may dominate the headlines, but the deeper story unfolding inside the industry is psychological exhaustion. Thousands of survivors are quietly carrying impossible workloads while trying to convince themselves they should feel grateful just to remain employed. That is not sustainability. That is survival mode. And survival mode comes with a cost. Get full access to AsianDadEnergy's Newsletter at asiandadenergy.substack.com/subscribe [https://asiandadenergy.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4]