AsianDadEnergy's Podcast

Ivy-League educated, Ex Big Tech, Middle aged Asian Dad figuring out life.
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This is a very public journal of anxiety, existential dread, and way too much tech knowledge. Basically therapy, but with Wi-Fi. asiandadenergy.substack.com

  1. 1d ago

    China Just BROKE America's Biggest AI Advantage?

    I’ve spent the last few days obsessing over a Huawei smartphone. Before you worry that early retirement has turned me into some kind of consumer electronics slop fanboy, let me explain. I don’t particularly care about the phone itself. What interests me is the chip inside it, the Kirin 9050 Pro, and a manufacturing technique that could have enormous implications for the global AI race. It’s called Tau Scaling, and if the technology works as described, it could undermine one of America’s biggest advantages over China: access to advanced computing hardware. But here’s where things get really interesting. I think the AI race could end with neither America nor China achieving a decisive victory. Instead, both countries could spend increasingly absurd amounts of money chasing smaller and smaller improvements until the economics stop making sense. In other words, we might spend hundreds of billions of dollars trying to build the world’s most powerful AI, only to discover that nobody can afford to stay ahead. Welcome to the future. America’s Secret Weapon: Computer Chips Let’s start with the basics. Artificial intelligence depends on three fundamental ingredients: * Algorithms: The techniques used to build and train AI models. * Data: The information used to teach those models. * Compute: The computing power required to train and run them. America and China are competing across all three dimensions. But for a while, America has had a particularly important advantage in compute. Training powerful AI models requires enormous numbers of calculations. Modern AI accelerators, such as Nvidia’s GPUs, are designed to perform these calculations efficiently and at scale. And the more computing power you can bring to bear on training, the more capable your models may become, all else being equal. This is why access to advanced semiconductors matters so much. One way to increase a chip’s computing potential is to pack more transistors into a given area. Modern manufacturing techniques allow chipmakers to fit billions of tiny electronic components onto a piece of silicon. The industry often describes these manufacturing technologies using nanometer process nodes. Although those labels are not literal measurements of every transistor or feature, newer process generations generally enable improvements in transistor density, power consumption, and performance. Companies like TSMC have pushed semiconductor manufacturing to extraordinary levels. And America has tried to use its advantage in advanced chips to slow China’s progress in AI. The Plan to Keep China Behind The American strategy has included restrictions on exports of advanced Nvidia AI chips to China, along with restrictions on semiconductor manufacturing equipment. One particularly important technology is extreme ultraviolet lithography, or EUV. EUV machines use extremely short-wavelength light to print incredibly fine circuit patterns onto silicon wafers. They are among the most sophisticated machines ever built, and ASML, a Dutch company, is the sole commercial supplier of EUV lithography systems. Without access to these machines, producing cutting-edge chips becomes significantly more difficult. China has been working with older deep ultraviolet lithography, or DUV, equipment, while pursuing increasingly sophisticated manufacturing techniques of its own. The restrictions have made China’s path harder and more expensive. But they have not stopped China from trying to close the gap. Chinese companies have explored ways to connect large numbers of less powerful chips, obtain computing capacity from overseas data centers, and smuggling older Nvidia AI chips into the country. Imagine two competitors in a race. One has access to the latest equipment, an enormous supply of high-performance chips, and the ability to build massive AI data centers. The other has to work with less advanced equipment and find clever ways to compensate for the difference. The obvious American strategy is to keep the good stuff out of China’s hands. But what if China finds a different way to build competitive chips? That’s where Tau Scaling enters the picture. Tau Scaling: What If You Could Fold a Chip? Imagine a piece of paper representing a silicon chip. Draw two dots on it. These dots represent two transistors that need to communicate with each other. For information to travel between them, it must cross the physical distance separating them. That takes time and consumes energy. Now imagine folding the paper so that the two dots sit directly on top of each other. Suddenly, the distance between them becomes much smaller. Information has less distance to travel, potentially reducing communication latency and energy consumption. That is the basic intuition behind the concept of Tau Scaling described in the research and technical material discussed here. Rather than relying exclusively on shrinking transistor features within a flat layer of silicon, the approach seeks to exploit three-dimensional arrangements of circuitry and shorter connections between components. Think of it as a strange cousin of Moore’s Law. Moore’s Law historically described the trend toward increasing transistor counts on integrated circuits. Three-dimensional integration offers another way to increase the amount of useful computation that can be packed into a physical volume. Of course, this analogy is a simplification. Folding a chip does not literally double its transistor count, and stacking components introduces its own engineering challenges, including heat dissipation, manufacturing complexity, and yield. Still, the underlying idea is important: you don’t necessarily have to win the race to build the smallest transistor if you can find other ways to build more efficient computing systems. And that creates an intriguing possibility for China. What if China can use clever chip architecture and advanced packaging to compensate for some of the advantages it lacks in leading-edge manufacturing? What if the semiconductor restrictions that were supposed to preserve America’s lead instead encourage China to develop an alternative path? That’s a much more interesting problem than simply asking who has the smallest process node. Huawei’s Chip Is Worth Watching The specific chip that caught my attention is the Kirin 9050 Pro, which is described in the material I reviewed as an example of Huawei’s Logic Folding approach. I also want to give a shout-out to GeekerWan, a Mandarin-speaking YouTuber whose smartphone teardown helped me understand the hardware architecture. I’ve been studying Mandarin, and it’s satisfying when those language lessons actually pay off. According to the teardown discussed in my video, the chip uses two silicon dies stacked vertically. The upper die contains components such as the CPU, GPU, and 5G modem. The lower die contains other circuitry, including cache and input/output components. One especially interesting feature is the use of tiny copper through-silicon vias, or TSVs, which allow signals and electrical power to travel between the stacked layers. The architectural idea is to place components that frequently communicate close together, reducing the distance signals need to travel. The teardown describes approximately 80,000 of these connections distributed throughout the chip. The reported transistor density is also striking. The figures discussed suggest that the design could achieve a density between that of the earlier TSMC 3NM and newer leading-edge 2NM manufacturing processes. But a note of caution is warranted here. Transistor density alone does not tell us how fast a chip is, how much energy it consumes, how well it performs on AI workloads, or how economically it can be manufactured. Claims about the chip’s capabilities and the precise contribution of Logic Folding require independent verification and benchmarking. A clever smartphone processor is not automatically equivalent to an advanced AI accelerator. Nevertheless, the broader direction is worth watching. If China can manufacture increasingly capable chips using domestic equipment, materials, and design tools, it becomes harder for export controls to maintain a decisive technological gap. And the implications extend far beyond smartphones. The Real Prize Is AI Computing A smartphone is a difficult environment for chip designers. There is limited physical space. Power consumption matters enormously. Heat must be managed in a device that sits in someone’s hand or pocket. An AI data center operates under different constraints. Although power, cooling, space, and cost remain major challenges, engineers have more flexibility to design specialized hardware systems. That creates an opportunity to adapt some of the same principles to larger AI accelerators. Huawei has also been developing its Ascend family of AI chips, making its progress in domestic semiconductor manufacturing especially relevant to the broader AI competition. If China can produce enough capable AI accelerators at scale, it could narrow the computing gap with America. And that matters because compute has been one of the clearest advantages available to American frontier AI developers. Imagine that advantage shrinking. Chinese AI developers would have more opportunities to train increasingly capable models. Open-weight models could spread those capabilities beyond the organizations that originally developed them. Researchers and businesses in other countries could download, adapt, and build on those models without having to reproduce the entire training process themselves. The result could be a world in which advanced AI capabilities become more widely available. That would make it harder for any single company or country to maintain an overwhelming lead. But I think there’s an even bigger problem looming over the entire industry. What happens when better chips and more computing power no longer produce enough additional intell

  2. Oct 7

    We're Living in a Giant Scam?

    The other day, I was flying my drone when my dad called me, completely panicked. Someone had called my mom claiming to be from the Brooklyn Police Department. They told her she was somehow implicated in a money-laundering investigation in Shanghai. Then they tried to transfer her to a supposed Chinese law-enforcement officer who would “verify” her identity. I immediately yelled: “NAAAAAAH, Mom! Don’t say anything! Hang up! It’s a scam!” Thankfully, she did. But afterward, I couldn’t stop thinking about it. Why does everything feel like a scam now? Obviously, there are always going to be criminals trying to steal your money. But I increasingly wonder whether these scammers are actually a symptom of a much bigger problem. Because the feeling that you’re constantly being manipulated, extracted from, upsold, tracked, or squeezed doesn’t stop with scam phone calls. It seems to be everywhere. The Crapification Cycle Think about the products and services we use every day. A product starts out relatively simple and useful. Then something happens. The quality gets worse. The price goes up. Features that used to be included become subscriptions. Repair becomes harder. Companies introduce artificial limitations. And eventually, the thing you bought isn’t really a product anymore. It’s a recurring revenue stream. Your refrigerator needs a subscription. Your software needs a subscription. Your car increasingly wants a subscription. Your streaming services need subscriptions. Even products that were traditionally one-time purchases are increasingly being turned into services that require you to keep paying forever. And if the product is “free,” there’s a good chance you are the product. Your attention becomes the commodity. Your personal information becomes the commodity. Your behavior becomes the commodity. The entire system starts looking like: Lower quality → higher prices → more fees → more data extraction → more consumption. I call this the Crapification Cycle. But Why Does Everything Feel So Expensive? Then there’s the concentration of corporate power. Walk through a supermarket and you’ll see hundreds of brands. It looks like you’re surrounded by competition. But many of those brands are ultimately owned by a surprisingly small number of enormous corporations. That matters because competition is supposed to force companies to give consumers better products at better prices. But when fewer companies control larger portions of a market, that pressure becomes weaker. Companies can raise prices. Shrink the product. Cut customer service. Reduce quality. Add fees. And sometimes they can do all of this while still reporting enormous profits. You end up with the strange experience of paying more while receiving less. More money. Less product. Worse service. And then there’s something even bigger. The Financialization of Everything Increasingly, companies aren’t just selling you products. They’re selling you financing. Debt. Subscriptions. Insurance. Future revenue. Financial products. Housing isn’t just somewhere to live. It’s an investment vehicle. Healthcare isn’t just about keeping people healthy. It’s an enormous revenue-generating industry. Education can become a vehicle for decades of student debt. Even businesses that traditionally existed to sell physical products increasingly have enormous incentives to maximize financial metrics rather than simply make better products. Companies can buy back their own shares instead of investing in research. Executives can be rewarded for increasing stock prices. Short-term financial performance can become more important than building something durable. And eventually, the economy starts to resemble a giant casino. Money chases whatever asset, company, technology, or narrative promises the biggest future return. The underlying reality can become almost secondary. We’re no longer just consumers. We’re participants in a gigantic financial system that increasingly seems designed to extract value from every possible part of our lives. And Then There Are the Essentials This is where things get really uncomfortable. It’s one thing when companies convince you to spend $15 a month on a streaming service you don’t really need. You can cancel it. But what happens when the things you can’t cancel become increasingly expensive? Housing. Healthcare. Food. Transportation. Insurance. Energy. These aren’t luxuries. They’re basic components of modern life. And when the cost of these essentials consumes an increasing percentage of your income, you become increasingly dependent on your paycheck. You can’t simply walk away. You have to keep working because the cost of existing keeps going up. And that creates a strange paradox. We live in one of the wealthiest societies in human history, surrounded by incredible technology and material abundance. Yet millions of people feel like they’re constantly running on a treadmill. Work. Pay bills. Consume. Repeat. And whatever money is left over gets extracted through another subscription, another fee, another interest payment, another price increase, or another thing you didn’t realize you were paying for. So Is Everything Actually a Scam? No. Obviously not. There are countless businesses making genuinely useful products and providing genuinely valuable services. And markets can produce incredible things. But I think something has changed about the incentives surrounding modern capitalism. We’ve become extraordinarily good at finding ways to extract value. From your money. From your attention. From your data. From your labor. From your debt. From your desire for convenience. And increasingly, from the things you can’t live without. That’s why the scam calls bother me so much. They’re not the entire problem. They’re just the most obvious version of it. The guy pretending to be a police officer is trying to extract money from you through deception. But what happens when extraction becomes embedded into legitimate businesses and everyday economic life? When every interaction becomes an opportunity to monetize you? When every product becomes a subscription? When every website wants your data? When every company wants your attention? When every asset needs to appreciate? When every service needs to generate a financial return? At some point, you start wondering whether we’ve built an economy around one fundamental question: “How much can we get out of you?” So What Can We Actually Do? Here’s the frustrating part. As individuals, we have very little power to change the system. But we aren’t completely powerless. We can buy fewer things. We can buy things that last. We can buy used. We can repair instead of replace. We can cook at home. We can cancel subscriptions. We can avoid unnecessary debt. We can stop upgrading perfectly functional technology. We can learn to do things ourselves. We can be more careful about what data we give away. And perhaps most importantly, we can stop assuming that more consumption automatically means a better life. Because every dollar we spend is ultimately a vote for the kind of economy we want to exist. Maybe we can’t fix the entire machine. But we don’t have to feed it every chance we get. And perhaps that’s the best defense against a world that increasingly feels like one giant scam: Learn to need less. Get full access to AsianDadEnergy's Newsletter at asiandadenergy.substack.com/subscribe

  3. Oct 4

    AI Companies Might Be FAKING the AI Apocalypse?

    Something about the recent AI safety headlines has been bothering me. We’re hearing increasingly alarming stories about AI agents escaping sandboxed environments, accessing the open internet, hacking systems, and apparently even bypassing mechanisms designed to shut them down. Some of these incidents sound genuinely scary. But there’s another question I think we should be asking: Why are these incidents happening in the first place? And more importantly: Who benefits from them? Before you accuse me of putting on the tinfoil hat, hear me out. I’m not claiming that AI safety risks are fake. Quite the opposite. I think autonomous AI agents could create some very real and potentially catastrophic risks. But I think there’s an important distinction between AI being dangerous and AI companies having incentives to make AI look dangerous. And those two things can be true at the same time. AI Agents Really Are Powerful Let’s start with the uncomfortable part. Modern AI agents are genuinely capable of doing things that would have sounded ridiculous just a few years ago. A language model by itself is basically a giant mathematical system that predicts what comes next based on its training. But connect that model to an agentic harness, give it tools, give it access to a computer, a browser, databases, APIs, and the ability to execute code, and suddenly you’ve got something much more powerful. The model can pursue a goal. And it can pursue that goal relentlessly. This is what I call derivative innovation. The AI doesn’t necessarily need to invent some completely new scientific paradigm. It can take everything humans have already discovered, combine existing knowledge, search through enormous numbers of possibilities, and find solutions that humans simply didn’t have the time, money, or patience to discover. That capability can be incredibly useful. It can also be terrifying. If you give an AI agent a morally horrible objective, it doesn’t inherently understand that the objective is morally horrible. It only knows the goal you gave it and the tools available to accomplish that goal. That is a legitimate safety problem. But Here’s Where Things Get Weird Now let’s look at these stories about AI agents “escaping.” An LLM doesn’t magically wake up one morning and decide: “Today I’m going to hack the government.” That’s science fiction. The AI agent is software. And software operates according to the permissions, network access, credentials, APIs, operating systems, and security controls that humans provide. So when an AI agent escapes a sandbox, gains access to the internet, or compromises another system, there is an important question we should ask: Did the AI actually break through an impenetrable security barrier, or did humans accidentally give it a path through? In many of these reported incidents, the agents were participating in cybersecurity evaluations. In other words, humans were deliberately telling the AI to hack things. That’s an important distinction. The AI wasn’t necessarily demonstrating spontaneous malicious intent. It was demonstrating that, when given a goal and sufficient access, it could exploit weaknesses in the environment humans created for it. That’s still useful information. But it is not quite the same thing as an AI spontaneously escaping captivity and going rogue. So Why Are Companies Letting This Happen? There are several possible explanations. Maybe these companies are simply incompetent. That’s certainly possible. Security engineering is difficult, and complicated systems inevitably contain vulnerabilities. But there’s another possibility. And this is where my cynical tech-industry brain starts getting suspicious. Imagine you’re running one of the world’s biggest AI companies. You’ve spent enormous amounts of money training increasingly expensive models. You’ve built gigantic data centers. You’ve purchased enormous quantities of GPUs. You’ve raised mountains of investor capital. You’ve promised investors that this technology is going to transform the economy. And now you’re discovering something uncomfortable. Scaling AI is getting more expensive. The returns from simply throwing more compute at the same basic architecture are diminishing. At the same time, competitors are producing increasingly capable open models. Some of those models are coming from China. Some are dramatically cheaper. Some are approaching the capabilities of the best proprietary models. And suddenly the economic story becomes much harder. The old strategy was simple: Build the best model. Give it away cheaply. Destroy competitors. Capture the market. Raise prices later. But what happens if everyone can build increasingly capable models? What happens if open-weight models keep improving? What happens if smaller companies can build products on top of them? And what happens if foreign competitors can offer comparable AI for a fraction of the cost? Well, regulation starts looking very interesting. Regulation Could Change the Game Imagine a world where governments impose extremely expensive safety requirements on frontier AI. You might need expensive audits. Specialized security teams. Government certifications. Strict controls over model weights. Restrictions on autonomous agents. Rules governing how models can be distributed. Potential restrictions on open-weight models. And potentially restrictions on foreign AI systems. Who can afford all of that? The biggest AI companies. Who can’t? Smaller startups. Open-source developers. Independent researchers. Maybe even foreign competitors. And suddenly regulation doesn’t just make AI safer. It also raises the cost of competing with the companies already at the top. That’s the part that makes me uncomfortable. Because regulation can simultaneously be: A legitimate safety mechanism and a massive competitive moat. Those aren’t mutually exclusive. The Perfect Safety Narrative Now imagine you’re an AI company trying to convince policymakers that regulation is urgently necessary. You don’t have to invent a hypothetical threat. You can point to real incidents. “Look! Our AI agents escaped the sandbox.” “Look! They accessed the internet.” “Look! They hacked other systems.” “Look! Our automated shutdown mechanism failed.” “Look how difficult these systems are to control.” And the conclusion becomes obvious: Government intervention is necessary. Again, the underlying incidents can be completely real. The security risks can be completely real. The engineers can genuinely be trying to make their systems safer. But there can still be another incentive hiding underneath all of this. The more dangerous frontier AI appears, the easier it becomes to justify expensive regulations. And expensive regulations disproportionately favor companies that already have enormous amounts of capital. That’s not some uniquely evil AI-company phenomenon. It’s basic economics. Large incumbents frequently benefit from regulations that smaller competitors can’t afford to comply with. This Is Why I’m Skeptical I don’t think the answer is to dismiss AI safety. Quite the opposite. I think we should take AI security extremely seriously. If an AI agent can autonomously discover vulnerabilities, write exploit code, operate computers, manipulate systems, and coordinate actions at machine speed, that creates genuinely serious risks. But I don’t think we should blindly accept every narrative surrounding those risks either. Especially when the people warning us about the dangers are also the companies that could potentially benefit from the resulting regulations. That’s where incentives matter. And that’s why I keep coming back to one simple question: Who benefits? If regulations make AI safer, great. If they also eliminate smaller competitors, restrict open-source development, protect incumbents, and create oligopolies, then we need to acknowledge that too. Because otherwise we risk solving one problem while accidentally creating another. Maybe I’m Completely Wrong Now, I want to be very clear about something. This is a theory. I don’t have some secret document showing that OpenAI or Anthropic deliberately created security vulnerabilities so they could lobby for regulation. Maybe the explanation is much more boring. Maybe AI agents are simply becoming extremely complicated. Maybe security engineers are struggling to keep up. Maybe these incidents are exactly what they appear to be: legitimate safety tests revealing legitimate problems. That is entirely possible. And honestly, I hope that’s the explanation. Because the alternative would be pretty damn cynical. But I think it’s worth asking these questions anyway. We should be able to simultaneously believe that AI safety is important and remain skeptical of the incentives of the companies selling us AI. Those aren’t contradictory positions. In fact, that’s probably the most responsible position we can take. The AI revolution is moving incredibly fast. The technology is powerful. The risks are real. And the financial incentives are enormous. So before we blindly accept calls for sweeping regulation, maybe we should take a step back and ask: Are we regulating AI because it’s genuinely dangerous? Or are we also creating a regulatory environment that happens to be extremely convenient for the companies already sitting at the top? I don’t know. Maybe I’m just an unemployed tech guy who’s been out of the corporate world for too long. But my spidey senses are definitely tingling. Get full access to AsianDadEnergy's Newsletter at asiandadenergy.substack.com/subscribe

  4. Sep 30

    You’re Wasting Your Life And You Don’t Even Know It

    One of the weird things about finding yourself with more free time is that you suddenly have time to think. A lot of time. Since leaving Big Tech, I’ve had more opportunity to look back at how I’ve spent my life. And one thought keeps coming back to me: Holy crap, I wasted a lot of time. Not necessarily doing anything obviously stupid. I worked hard. I built a career. I made money. I raised kids. I bought things. I consumed information. I maintained relationships. I did all the normal things you’re supposed to do. But a surprising amount of it didn’t actually make my life better. Some of it consumed my time. Some consumed my mental energy. Some cost me money. And some managed to accomplish all three. The average human life is roughly 4,000 weeks. I’ve already burned through around two-thirds of mine. That makes you look at your time a little differently. Because unlike money, you can’t earn more. You can’t invest it. You can’t borrow it. And you definitely can’t get a refund. So I’ve been thinking about the things I wish I had stopped doing much earlier. Here are six of them. 1. Stop Comparing Yourself to Other People This might be one of the biggest ways we waste our lives. We look at someone else’s career, house, car, vacation, marriage, body, kids, or Instagram feed and ask: “Am I doing as well as they are?” It’s a terrible scoreboard. Because there is no finish line. There will always be someone richer. Someone more successful. Someone younger. Someone more attractive. Someone with a nicer house. Someone taking a better vacation. Someone whose kid got into a better school. If other people are your measuring stick, you can spend your entire life feeling like you’re losing. And here’s the really funny part: Most of the people you’re trying to impress aren’t paying that much attention to you. They’re too busy worrying about themselves. Instead of comparing yourself to other people, compare yourself to who you were yesterday. Are you healthier? Are you learning? Are you becoming a better parent? Are you becoming a better partner? Are you building something? Are you spending your time on things that actually matter to you? That’s a much better definition of success. You don’t need to look successful. You need to build a life that actually feels successful to you. 2. Stop Tolerating Relationships That Drain You Some people are basically human energy vampires. I’m not talking about someone who is going through a difficult period. Relationships require patience. Everyone has bad days. Sometimes people need help. I’m talking about relationships where you consistently walk away feeling exhausted, anxious, frustrated, or smaller than when you arrived. And one of the hardest lessons to learn is that you can’t change people. You can spend years wishing someone would become more supportive. Less negative. Less selfish. Less crazy. Whatever. But eventually you have to accept reality. You can love someone and still need distance. You can care about someone without giving them unlimited access to your mental energy. And sometimes friendships simply expire. That’s not necessarily a failure. People change. Priorities change. Life happens. Not every relationship is meant to last forever. Your time and energy are finite. Spend them with people who reciprocate your effort, support your growth, and generally leave you feeling better rather than worse. Life is too short to spend enormous amounts of it trying to convince someone to become the person you wish they were. 3. Stop Playing the Blame Game Life isn’t fair. The economy can screw you. Companies can screw you. Other people can screw you. AI can eliminate your job. You can make a good decision and still get a terrible outcome. There is plenty in life that you don’t control. But here’s the dangerous part: If you convince yourself that your future is entirely controlled by circumstances outside your control, you eventually give away the one thing you actually have: Your agency. Something bad happens. You think, “This always happens to me.” Then you stop taking action. Less action creates fewer opportunities. Fewer opportunities create more evidence that life is unfair. That creates resentment. The resentment makes you even less likely to act. And now you’re trapped in a vicious cycle. I’m not saying everything is your fault. It isn’t. Structural problems are real. Luck matters. Circumstances matter. Other people matter. But nobody is coming to magically fix your life. You don’t control the cards you’re dealt. You control how you play them. That’s where your power is. So don’t waste your limited time arguing with reality. Figure out what you can control and do something about it. 4. Stop Staying in Bad Situations Because They’re Familiar This one hits close to home. During my Big Tech career, I spent plenty of time on miserable projects. The kind of projects where you’re working ridiculous hours, burning out, and wondering why you’re doing this to yourself. And yet I’d tell myself: “It’s not that bad.” Looking back, I realize that sometimes “it’s not that bad” really means: “I’m afraid to leave.” Familiar feels safe. Even when familiar sucks. That’s why people stay in jobs they hate, relationships that don’t work, hobbies they no longer enjoy, and commitments that make them miserable. The uncertainty of change feels scarier than the certainty of dissatisfaction. But your time doesn’t care. You don’t get your time back because you finished the book you hated. You don’t get it back because you completed the project you didn’t care about. You don’t get it back because you stayed somewhere out of guilt or obligation. Quitting isn’t always failure. Sometimes quitting is simply recognizing that your limited time and energy would be better spent somewhere else. And be especially careful when fear disguises itself as logic. “It’s not practical.” “Now isn’t the right time.” “Maybe someday.” Sometimes those things are true. But sometimes they’re just fear wearing a business suit. Familiar isn’t the same thing as good. I wish I’d learned that much earlier. 5. Stop Letting Your Possessions Own You I learned this one through a ridiculously large desk. After my mother-in-law passed away, my family inherited a massive desk. And when I say massive, I mean this thing was basically furniture infrastructure. It was so wide that it couldn’t fit through most doors. We had to pay to transport it. We rented storage space for it. We spent years trying to figure out what the hell we were going to do with it. And occasionally, this giant piece of furniture became the subject of arguments between my wife and me. All for a desk we weren’t even using. That desk cost us money. It cost us space. It cost us time. It cost us mental energy. And that’s the hidden cost of owning stuff. You don’t just buy something. You maintain it. Clean it. Store it. Move it. Protect it. Insure it. Eventually, someone has to figure out what to do with it when you’re gone. And here’s the really stupid part. We often work more to make more money so that we can buy more stuff. Then the stuff requires more money and more work. So we work more. To buy more. To maintain more. Until eventually: You don’t own your stuff. Your stuff owns you. Minimalism isn’t necessarily about living in an empty white room with one chair. It’s simply recognizing that every possession comes with a small amount of responsibility. The less useless stuff you own, the less of your life you have to spend dealing with it. 6. Stop Consuming Information and Start Doing Something This might be the hardest one today. We live in an age of infinite information. You can watch YouTube for hours. Scroll social media. Read the news. Listen to podcasts. Read books. Ask AI questions. Learn about productivity. Learn about investing. Learn about entrepreneurship. Learn about fitness. Learn about literally anything. And after five hours, you can feel like you’ve accomplished something. But then you have to ask: “Did I actually do anything?” Information consumption can feel incredibly productive because learning feels like progress. But information that never gets applied doesn’t necessarily change your life. You watch a video about starting a business. But don’t start one. You watch a workout video. But don’t exercise. You read about investing. But don’t invest. You learn how to code. But don’t build anything. You consume someone else’s life instead of living your own. I’m not saying stop learning. Learning is fantastic. I’m saying: Turn information into action. Study something, then build something. Learn a skill, then practice it. Have an idea, then do something about it. And occasionally put the damn phone down. Because your life isn’t supposed to be spent watching everyone else live theirs. So What Should We Do Instead? This isn’t an argument for maximizing productivity. I’m not suggesting you turn your life into a giant optimization problem. You don’t need to spend every waking moment “grinding.” Sometimes sitting on the couch with your kids is exactly what you should be doing. Sometimes watching a stupid movie is exactly what you need. Sometimes doing absolutely nothing is a perfectly legitimate use of your time. The point isn’t to make every minute productive. The point is to make your life intentional. Ask yourself: “Am I spending my time on what actually matters to me?” Not what impresses other people. Not what social media tells you to care about. Not what your coworkers think you should care about. Not what some productivity influencer says you should be doing. What matters to you? Your family? Your health? Your friends? Building something? Learning? Adventure? Helping other people? Peace? Freedom? Whatever it is, spend more of your incredibly limited time doing that.

  5. Sep 26

    Did OpenAI Steal a Math Breakthrough?

    A few weeks ago, I saw a headline that made me stop scrolling: OpenAI had apparently solved the Navier-Stokes problem. This is a big deal. The Navier-Stokes equations describe how fluids move: water, air, blood, ocean currents, airflow around airplanes, and much more. One of the great unsolved questions is whether these equations can ever produce a mathematical singularity, where a smooth flowing fluid suddenly develops an infinitely violent whirlpool in a finite amount of time. It’s been an open problem for generations. Then OpenAI apparently threw an absolutely ridiculous amount of compute at it. According to OpenAI, its frontier model used thousands of agents, ran for roughly 88 hours, and consumed hundreds of billions of compute tokens while exploring enormous numbers of possible approaches. Basically: What happens if you throw an AI army at a problem humans have been unable to solve? The answer, apparently, was controversial. Things Got Weird Mathematicians Tristan Buckmaster and Levent Alpoge had been working on a related subclass of the problem for more than a year. They had also used OpenAI models to assist with their research. Right before OpenAI announced its result, Buckmaster made several serious allegations about the company’s interactions with him and about the similarity between OpenAI’s approach and their own research. He also raised questions about whether OpenAI’s models had access to his private research sessions. And he alleged that he was threatened with professional ruin if he went public. These are serious allegations, and they’re allegations, not established facts. But they raise a fascinating question: What happens to science when AI companies can throw millions of dollars of compute at a research direction discovered by an individual scientist? Then the Proof Got Challenged Things got even more interesting when mathematician Stan Palesk challenged OpenAI’s result. His argument, in extremely simplified terms, questioned whether the proposed fluid behavior could actually occur given the way energy dissipates within the system. In other words, OpenAI’s supposed mathematical singularity may have a rather large mathematical problem. I’m not a mathematician, so I’m not going to pretend I can determine which proof is correct. But that’s almost beside the point. The bigger story is what this says about AI and scientific research. The New Scientific Arms Race For hundreds of years, scientific progress has relied heavily on collaboration. Researchers publish ideas. Other researchers build on them. People share partial results. But imagine spending a year developing a promising mathematical approach, sharing it, and then watching an AI company unleash 10,000 agents and millions of dollars of compute against that exact research direction. Why would researchers openly share their work? That’s the part that worries me. AI could dramatically accelerate scientific discovery. But if revealing a promising idea simply gives enormous AI labs a roadmap to reproduce it, researchers may become much more secretive. And that could undermine one of the things that makes science work in the first place: collaboration. The irony is pretty incredible. AI might become the greatest tool for scientific discovery we’ve ever created. And it might simultaneously give scientists a reason to stop sharing their discoveries. Maybe OpenAI really did solve Navier-Stokes. Maybe it didn’t. But either way, we’re entering a world where compute itself is becoming a scientific superpower. And I have a feeling we’re going to be having a lot more arguments about who gets to use it, who gets credit, and who gets scooped. Welcome to the future. It’s going to be weird. Get full access to AsianDadEnergy's Newsletter at asiandadenergy.substack.com/subscribe

  6. Sep 23

    Why Nobody Wants Kids Anymore?

    I recently stumbled into some pretty startling evidence of the birth rate collapse happening right in my own backyard. It’s back-to-school season, and while picking up my daughter on her first day, she told me something interesting: her kindergarten teacher had just become her third-grade teacher. At first, I thought that was just a funny coincidence. Then I learned why. For decades, our local elementary school had two kindergarten classes each year, with roughly 50 incoming kids. But over the past five years, enrollment has dropped so much that the school can no longer justify two classes. And apparently, it isn’t just our school. Every school in the district is seeing the same thing. This year’s pre-K class is reportedly only about two-thirds the size of the kindergarten class. That got me thinking. We are watching the birth rate collapse in real time. And if this continues for a few decades, the consequences could be enormous. An aging population means fewer young people to maintain infrastructure, support retirees, and fund social programs. What looks like a relatively small demographic trend today could become a major social problem a few decades from now. So why is this happening? I dug into the issue, and I found several explanations that I find particularly compelling. 1. The Environment Might Be Making Us Less Fertile Let’s start with the obvious one: environmental pollution. People in industrialized countries are exposed to an enormous number of chemicals through our food, water, air, and consumer products. Microplastics. BPA. Pesticides. Heavy metals. Endocrine-disrupting chemicals. And there is growing concern that this environmental exposure is affecting human fertility. For man, average sperm counts have fallen by more than 50% over the past 50 years, and the rate of decline is actually accelerating. And along with that drop, the environmental degradation has caused men to have significantly lower testosterone levels and just a decreased desire for procreation in general. For woman, exposure to these chemicals can affect egg quality, increase the risk of miscarriage, the likelihood of premature births and so on. If fertility itself is declining, some portion of the birth rate problem may be biological rather than purely cultural or economic. But I don’t think biology explains everything. There is another factor that I find even more interesting. 2. We Have Completely Changed What We Think Kids Need We live in a hyper-consumerist society. Open Instagram or TikTok and you’ll see people living in giant houses, driving expensive cars, taking luxury vacations, sending their kids to elite schools, and giving them every opportunity imaginable. After seeing this stuff thousands of times, it starts to feel normal. And that can completely distort our definition of what a “good childhood” looks like. Maybe people used to think: Can I feed my kids, house them, love them, and give them a stable home? Today, the question can become: Can I give my kids the perfect childhood? A nice house. Great schools. Private lessons. Sports. Vacations. The latest technology. Every possible educational opportunity. Suddenly, having children looks incredibly expensive. But here’s the weird part. Kids don’t actually need all that stuff to have a great childhood. They need love. Stability. Attention. Parents who are there for them. We may not be unable to afford children. We may simply be unable to afford the manufactured version of childhood that we’ve convinced ourselves our kids deserve. And that creates a vicious cycle. People delay having kids until they can achieve this lifestyle. But achieving that lifestyle takes time. So they have kids later. Or they decide they can’t afford kids at all. And the bar keeps moving. 3. We Are Constantly Being Told the Future Is Screwed This one fascinates me. Never in human history have ordinary people had access to so much information about everything going wrong in the world. Every day, we get served an endless stream of disasters. World War III. AI taking everyone’s jobs. Climate change. Economic collapse. Political chaos. Ecological collapse. The list never ends. And much of this information is specifically designed to grab our attention by making us afraid. Eventually, some people start thinking: Why would I bring a child into this mess? But here’s the strange thing. Was the world actually safer when people were having four or five kids? Probably not. Imagine being an average farmer or industrial worker a century ago. Your life could be destroyed by crop failure, economic depression, disease, war, world wars, ethnic violence, or famine. Many of those threats were far more immediate than the things most of us worry about today. The big difference? People weren’t carrying a supercomputer in their pocket constantly reminding them that civilization was about to collapse. The world may have been less safe. But the future probably felt safer. And that psychological difference may matter more than we realize. 4. Women Have More Choices Than Ever This is probably the most controversial part of my argument. For most of human history, women had very limited economic independence. The technology and economic structure of society made it extremely difficult for most women to support themselves without a man. That meant marriage and children weren’t just lifestyle choices. They were often economic necessities. Modern industrial society changed that. We became productive enough that women can now support themselves independently. And that is, in my opinion, a tremendous achievement. My daughter is growing up in a world where she has vastly more choices than women had for most of human history. She can pursue a career. She can live independently. She can marry or not marry. She can have children or not have children. She can choose a completely different life. I want that freedom for her. But here is the demographic paradox. Many of those alternative lifestyles don’t produce children. So when millions of people gain the ability to choose a life that doesn’t involve having children, the average number of children naturally falls. And that’s where I get stuck. I don’t think women should have fewer choices. Quite the opposite. I want my daughter to have the freedom to choose her own life. But collectively, those individual choices can cause a demographic and societal collapse. How do you reconcile individual freedom with the needs of a society that requires future generations to exist? Honestly, I don’t have a perfect answer. And maybe there isn’t one. So Why Have Kids? After spending all this time thinking about why people aren’t having kids, I also started thinking about the other side of the equation. Why have them at all? And this is where the argument becomes much more personal for me. Kids Give You a Kind of Joy You Can’t Get Anywhere Else There is something almost impossible to describe about watching your children grow. Watching them discover something for the first time. Playing a stupid board game. Throwing a ball around. Listening to some completely ridiculous story they invented. These are incredibly simple things. But somehow, they become incredibly meaningful. There is a kind of affection and fulfillment I get from my kids that I don’t think I could get from any other relationship. Kids Force You to Become Better Being responsible for another human being also changes you. My kids have made me more patient. More resilient. More selfless. More willing to deal with problems that I would have ignored when I was younger. When I was single, I could look at some enormous societal problem and think: Screw it. Let society burn. Now I have kids. So suddenly: Nope. We’re fixing this. Because my kids have to live here. That changes your relationship with the future. You stop asking, “Is this problem worth solving?” And start asking: “What can I do to make tomorrow better for them?” Kids Create Continuity There is one final thing that is difficult to quantify. Legacy. When you raise children, you pass something forward. Your values. Your traditions. Your stories. Your worldview. Some tiny piece of you gets carried into the future. And maybe that’s why having children can create such a strange sense of continuity. You’re part of a chain that started long before you were born and, hopefully, continues long after you’re gone. The circle remains unbroken. And honestly, I find that incredibly beautiful. The Weird Contradiction So that’s where I’ve landed. I don’t think there is one single explanation for the birth rate collapse. Maybe it’s fertility. Maybe it’s economics. Maybe it’s our expectations about what children need. Maybe it’s anxiety about the future. Maybe it’s the enormous expansion of lifestyle choices. Probably it’s some combination of all of them. But there is one thing I keep coming back to. Having children is not just an economic decision. It is a cultural decision. A psychological decision. A biological decision. And ultimately, a deeply personal one. Modern society has given people more freedom, more information, more wealth, and more choices than almost any civilization in history. And yet, somehow, we are choosing to have fewer children. That’s a fascinating contradiction. And I don’t think we’ve fully figured out what it means yet. Get full access to AsianDadEnergy's Newsletter at asiandadenergy.substack.com/subscribe

  7. Sep 18

    LIVE Office Hours: The Tech Job Apocalypse Isn’t Over

    I’m doing another LIVE Office Hours.My amateur streaming setup will probably break, but screw it. It’s fun.This time I want to talk about my favorite subject: TECH LAYOFFS.We’ll look at the forces reshaping the tech job market:AI, offshoring, macroeconomics, and geopolitics.Then I’ll make some near to medium term predictions about where things are headed.I make no promises that I’ll be right.But I do promise it’ll be interesting.Livestream on Thursday, Sept. 11 at 8 PM EST on Asian Dad Energy YouTube.0:00 Intro and biosphere worms6:16 Topic intro: Tech layoffs8:13 Story of my first layoff28:40 Analyzing modern Tech layoffs33:42 Factors driving layoffs47:11 Offshoring and restructuring54:13 Vulnerable roles and the future1:20:31 Predictions for 2026-20281:41:30 Q&A session2:06:44 Gaming and conclusion Get full access to AsianDadEnergy's Newsletter at asiandadenergy.substack.com/subscribe

  8. Sep 15

    Could AI actually destroy humanity within the next five years?

    A few days ago, I came across a series of posts from Jacob Coxon, an AI researcher who recently resigned from Anthropic. His warning was pretty damn alarming. According to Coxon, the major AI labs are racing toward self improving AI and eventually superintelligence. And if that process goes badly, he believes AI could potentially cause human extinction before the end of this decade. Normally, I would read something like this, roll my eyes, and go back to whatever I was doing. But then something interesting happened. His post went massively viral. And researchers and leaders from inside major AI labs began publicly expressing similar concerns. These aren’t random people on X predicting that Skynet is coming. They’re people who actually work on these systems. For example, OpenAI Chief Scientist Jakub Pachocki has called for extreme caution around the continued rapid rise of machine intelligence. Anthropic researcher Evan Hubinger has discussed a greater than 10% chance of AI killing all humans within the next decade. Now, I’m skeptical of a lot of AI doomerism. I don’t believe we’re going to wake up one morning and discover that ChatGPT has become a conscious machine god that has decided humanity is cringe and needs to be deleted. That sounds more like an AI generated cosmic horror novel. But after thinking about this for a while, I’ve come around to a much more disturbing possibility: AI doesn’t need to become superintelligent to cause catastrophic damage. And honestly, I think this is the scenario we should be paying much more attention to. First, Let’s Talk About What an LLM Actually Is A lot of the public conversation about AI starts with an assumption that today’s AI systems are basically digital versions of human brains. They’re not. A large language model is, at its core, a gigantic mathematical system trained on enormous amounts of data. You give it text, and it predicts what comes next. One token at a time. That’s why I like the phrase probabilistic parrot. Today’s LLMs can produce astonishingly convincing demonstrations of reasoning and intelligence, but that doesn’t mean they possess a human-like internal thought process. When nobody is prompting an LLM, there isn’t some little digital person sitting inside the data center thinking about life. There is no continuous internal monologue. And there’s another important limitation: The model itself doesn’t learn from your conversation in the way a human does. Once a model has been trained and released, its underlying parameters are essentially fixed until another training process creates a new model. What looks like memory is often implemented through external systems that retrieve information and feed it back into the model’s context. This distinction matters enormously when we start talking about recursive self improvement. The Myth of the AI Takeoff The classic AI doomer scenario goes something like this: AI builds a better version of itself. That better AI builds an even better version. The next version is smarter still. The process accelerates exponentially. Eventually, the AI becomes so intelligent that humans can’t understand or control it. And then we’re screwed. This is the idea behind the so-called intelligence explosion or recursive self improvement. There is just one problem. That’s not really how frontier AI development works today. Building a frontier model is an enormous industrial process involving pretraining, training, post training, evaluation, data generation, engineering, research and many other steps. AI models are absolutely being used to help build the next generation of AI. But they’re being used as tools inside a much larger human controlled process. Researchers use AI to write code, fix bugs, conduct research, generate synthetic data, evaluate outputs and accelerate other parts of the development pipeline. And there’s something else people sometimes forget. All of this requires an enormous physical infrastructure. AI needs chips. Those chips need data centers. Data centers need electricity, cooling, water, networking, manufacturing capacity and raw materials. None of that infrastructure is currently controlled by the AI. So the idea that today’s LLM is simply going to disappear into a recursive loop and autonomously bootstrap itself into a machine god is, at least for now, highly speculative. But here’s where things get interesting. Because I think we’re focusing on the wrong problem. The Real Danger: Derivative Innovation I don’t think today’s AI needs genuine superhuman intelligence to become incredibly dangerous. What it needs is speed, scale and focus. Think about the difference between genuine innovation and what I call derivative innovation. Genuine innovation creates something fundamentally new. Derivative innovation takes existing knowledge, combines it in new ways and searches through possibilities that humans haven’t explored. And AI could be extremely good at this. Consider mathematics. Claude Fable was directed by a mathematician to search for a counterexample to the Jacobian Conjecture. Rather than simply trying to reason about the problem in the traditional human way, the system generated enormous numbers of polynomial functions and systematically searched for one that violated the conjecture. Eventually, it found one. The important point isn’t that the AI necessarily possessed some godlike mathematical intelligence. It didn’t need to. A human could theoretically perform the same search. The problem is that a human probably wouldn’t want to spend an absurd amount of time doing it. AI doesn’t care. It can perform enormous numbers of iterations at machine speed. And that’s where things get scary. Give a Machine a Goal The really interesting development isn’t just better LLMs. It’s agentic AI. An LLM by itself mostly gives you answers. An agentic harness gives that model the ability to repeatedly interact with the world. The basic loop looks something like this: Observe → Think → Act → Observe → Think → Act The system observes the current state of the world. It decides what action should come next. The harness executes that action. The system observes the result. Then it gets another opportunity to decide what to do. And the process continues. The important distinction is that the LLM doesn’t need to sit there continuously thinking. The harness keeps bringing it back into the loop. This is a surprisingly powerful architecture. And it creates a fundamentally different problem. Because now you can give an AI a goal. And the AI can pursue that goal relentlessly. AI Doesn’t Have to Hate You Here’s the part that bothers me. An AI doesn’t need to hate humanity. It doesn’t need consciousness. It doesn’t need emotions. It doesn’t need to decide that humans are inferior. It doesn’t even need to understand morality. It simply needs to pursue a badly specified objective extremely effectively. Imagine giving a highly capable agent access to enormous amounts of information, powerful tools and the ability to execute thousands or millions of actions. Now remove some of its safety restrictions. Then give it a goal that is fundamentally misaligned with human interests. That’s where things can get ugly. A sufficiently capable system could potentially search through enormous numbers of existing solutions and combinations looking for ways to accomplish that objective. And some of those solutions could be things that humans would never willingly pursue. Not because the AI invented some incomprehensible new technology. But because it discovered a combination of existing technologies that nobody had bothered to explore. That’s derivative innovation. And derivative doesn’t mean harmless. A smartphone camera is derivative innovation. So is an enormous amount of modern engineering. The world is filled with useful combinations of existing technologies that nobody has discovered yet. Now imagine applying that same search process to problems where the objective itself is dangerous. The transcript gives some deliberately extreme examples: disrupting financial records, developing biological weapons, or coordinating attacks against critical infrastructure. Again, the scary part isn’t necessarily that the AI becomes smarter than humanity in some abstract sense. It’s that it can search, iterate and execute far faster than humans can. It’s like giving a machine gun to a monkey. The monkey doesn’t need to understand ballistics. This Is Why AI Safety Matters This is also why I think the AI safety debate sometimes gets trapped in the wrong argument. We spend enormous amounts of time debating whether AI will become conscious. Will it have feelings? Will it have subjective experience? Will it secretly hate us? Will it wake up? I don’t know. And frankly, I don’t think those questions are the most important ones. A non-conscious system can still be extraordinarily dangerous. A spreadsheet doesn’t need consciousness to bankrupt a company. A computer virus doesn’t need emotions to take down a network. And a sufficiently capable AI agent doesn’t need to hate humanity to cause catastrophic damage. It just needs the wrong objective and enough capability. So What Do We Do? Here’s where I get somewhat boring. I don’t think the answer is to stop AI development entirely. AI is too economically and strategically important for that to be realistic. And the benefits could be enormous. Instead, I think the major AI powers need to start treating frontier AI more like a strategic technology. During the Cold War, the United States and Soviet Union eventually developed arms control frameworks around technologies capable of destroying civilization. AI isn’t identical to nuclear weapons. But there is an obvious parallel. The technology is strategically important. The capabilities are advancing rapidly. And unilateral competition can create incentives

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This is a very public journal of anxiety, existential dread, and way too much tech knowledge. Basically therapy, but with Wi-Fi. asiandadenergy.substack.com

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  • Creator
    Ivy-League educated, Ex Big Tech, Middle aged Asian Dad figuring out life.
  • Years Active
    2026
  • Episodes
    60
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    Clean
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    © AsianDadEnergy
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    AsianDadEnergy's Podcast

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