AsianDadEnergy's Podcast

Ivy-League educated, Ex Big Tech, Middle aged Asian Dad figuring out life.
  • 5.0 (2)
  • Technology
  • Weekly

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. 22h ago

    AI Just Broke Reality? Nothing Will Be the Same!

    Now that I’m retired early, I have a lot of free time on my hands. And one of the things I enjoy doing with that time is keeping up with major breakthroughs in computer science, mathematics, and artificial intelligence. Every once in a while, something comes along that makes me stop and think. Something that might actually change how we understand the world. And just recently, we may have witnessed one of those moments. I’m talking about the reported AI disproof of the Exponential Time Hypothesis, one of the fundamental assumptions underlying modern computer science. And here’s what makes this particularly interesting. Both OpenAI and Anthropic reportedly achieved breakthroughs on the same day, just hours apart, using two apparently independent approaches. If their results hold up under scrutiny, this could be an extraordinarily important development. Potentially, we’re talking about something with implications far beyond computer science. We’re talking about the possibility of breaking through one of the most fundamental computational barriers that humanity has struggled with for generations. And yes, that’s a pretty big deal. Let me explain why. 1. The Mathematical Wall That Humanity Thought We Couldn’t Break To understand what’s happening, we first need to talk about something called the Exponential Time Hypothesis, or ETH. Interestingly enough, the origins of this idea go all the way back to the 1950s, to a mathematician named John Nash. If that name sounds familiar, he’s the mathematical genius who developed some of the foundational ideas behind game theory. He also struggled with schizophrenia, and his life was eventually portrayed in the movie A Beautiful Mind. But there’s another lesser-known part of his story. Back in the 1950s, Nash wrote a letter to the American National Security Agency about the difficulty of breaking encrypted messages intercepted from the Soviet Union. His basic observation was pretty straightforward. If you have the decryption key, checking an encrypted message is relatively easy. But if you don’t have the key, figuring out what that key is can be an entirely different story. Nash suspected that cracking sufficiently complex encryption systems would require computational effort that increased exponentially with the length of the encryption key. Think about it this way. Imagine you’re trying to solve a puzzle. Every time you add another piece, the number of possible combinations doubles. So you end up with something like this: * 1 piece: 2 possible combinations. * 2 pieces: 4 possible combinations. * 10 pieces: 1,024 possible combinations. * 40 pieces: Over a trillion possible combinations. * 256 pieces: An astronomically large number of combinations. That’s the problem with exponential growth. It starts off looking manageable, but very quickly becomes completely ridiculous. Even if you have the world’s most powerful supercomputer, there are certain problems where checking every possible combination would take longer than the age of the universe. And this isn’t just some theoretical curiosity. It’s part of the reason modern cryptography works. The idea that certain computational problems become prohibitively expensive as their complexity increases eventually became formalized into what computer scientists call the Exponential Time Hypothesis. At its heart is a mathematical problem called 3-SAT. Don’t worry, I’m not going to torture you with a college mathematics lecture. The basic idea is that you have a collection of logical variables and constraints, and you’re trying to determine whether there’s an assignment of values that satisfies all those constraints. As the number of variables increases, the number of possible combinations grows exponentially. ETH essentially assumes that there is no algorithm capable of solving every possible 3-SAT problem faster than exponential time. In other words, no matter how clever you are, there are certain problems where the computational complexity grows so quickly that you eventually hit a wall. And this is closely connected to one of the biggest unsolved questions in computer science: P versus NP. The basic question is whether problems whose solutions can be verified efficiently can also be solved efficiently. Most computer scientists believe that P does not equal NP. In plain English, they believe that there are problems for which finding a solution is fundamentally much harder than checking whether a proposed solution is correct. That’s the assumption I learned about back in college. And here’s the important part. This isn’t just some obscure mathematical problem that exists to keep university professors employed. The underlying computational difficulty shows up in an enormous range of real-world applications. 2. Why Breaking ETH Could Change Everything Imagine what humanity could accomplish if we suddenly discovered an efficient way to solve problems that currently require unobtainable amounts of computational effort. For starters, think about cryptography. We could potentially discover entirely new ways to break encryption systems that we currently consider secure. Of course, that would also create some rather unpleasant consequences for internet security and financial systems. But let’s put that aside for a moment. What about medicine? Imagine being able to efficiently search through enormous numbers of possible molecular configurations to design proteins that specifically target and treat certain types of cancer. What about logistics? Imagine optimizing global supply chains, airline schedules, and public transportation networks to operate at levels of efficiency that are simply impossible today. What about materials science? Imagine discovering entirely new materials that are lighter than air, more flexible than rubber, and stronger than steel. Materials that could potentially enable structures and technologies that sound like science fiction today. And then there’s artificial intelligence itself. Imagine building an AI system that can efficiently solve problems that currently require enormous amounts of computational resources. An AI that can generate genuinely original, civilization-changing ideas on demand. We’re talking about the possibility of accelerating scientific discovery, engineering, medicine, and technological innovation by orders of magnitude. Perhaps even achieving true artificial superintelligence. Now, obviously, these are not guaranteed consequences of disproving ETH. But they illustrate why the underlying mathematical question matters so much. For generations, researchers have been trying to find better algorithms to solve these problems. We’ve explored everything from clever mathematical techniques to alternative computing architectures, including quantum computers. But even quantum computing comes with substantial limitations, and it isn’t some magical solution to every computational problem. The uncomfortable reality is that we’ve spent decades trying to find ways around this exponential computational barrier. And most researchers believed that, at least in the general case, it was fundamentally unavoidable. Which brings us to the really interesting part. 3. OpenAI and Anthropic May Have Just Found a Way Around the Wall According to their reported research, both OpenAI and Anthropic have independently produced mathematical arguments that challenge the Exponential Time Hypothesis. I took a look at both of their GitHub repositories. And at first glance, they appear to be two genuinely independent approaches. What’s particularly interesting is how both teams seem to have approached the problem. They’re essentially following what I would call the derivative innovation playbook. Rather than inventing an entirely new mathematical framework from scratch, AI agents can leverage existing mathematical knowledge, explore enormous numbers of possibilities, and use computational resources to search for solutions that human researchers might never have discovered on their own. In other words, take decades of accumulated mathematical knowledge, throw an army o… Get full access to AsianDadEnergy's Newsletter at asiandadenergy.substack.com/subscribe

  2. 5d ago

    How to survive a JOBLESS future?

    AI is coming for jobs. Layoffs are everywhere. And the old career playbook is increasingly useless. So what the heck are we supposed to do? On Wednesday, October 28, at 8 PM EST Asian Dad Energy did another livestream about surviving the coming job apocalypse. 💀 We talked AI, layoffs, underemployment, resilience, and how to build a life that doesn't depend on your boss deciding you're still useful.Bring your questions. Bring your existential dread. Let's figure this mess out together. Get full access to AsianDadEnergy's Newsletter at asiandadenergy.substack.com/subscribe

    How to survive a JOBLESS future?

  3. Oct 29

    Anthropic's IPO Just Revealed Something TERRIFYING?

    So Anthropic is preparing for an IPO, and its S-1 registration statement is finally giving investors a closer look at the financial reality behind one of the hottest companies in AI. For those unfamiliar with the process, an S-1 is the registration document a US company generally files with the Securities and Exchange Commission before going public. It lays out the company’s financials, business model, risk factors, and other information prospective investors should know before buying its stock. And with Anthropic reportedly targeting a valuation of around $2 trillion, we’re talking about a truly ridiculous amount of money. Now, I like money. I’m an unemployed former Big Tech software engineer with more than 25 years of experience in the industry. I’ve spent a good chunk of my adult life building software, watching technology companies rise and fall, and developing an increasingly unhealthy level of skepticism toward corporate hype. But I also like the idea of making a few bucks without having to return to corporate meetings that could have been emails. So I decided to put my tech cynicism aside, take a closer look at Anthropic’s financial disclosures, and ask a simple question: Can this company actually grow into a $2 trillion valuation? What I found was fascinating, concerning, and occasionally so absurd that I had to read the numbers twice. Let’s get into it. 1. Anthropic’s Losses Are Much Bigger Than They First Appear Let’s start with the headline numbers. Anthropic reportedly generated $16.3 billion in revenue during the first half of 2026. That’s explosive growth. In just six months, the company generated approximately 3.5 times its revenue for all of 2025. On the surface, that’s the sort of growth investors dream about. The company also reported an operating loss of $693 million. Now, that might not sound particularly alarming for a rapidly growing technology company preparing to go public. Amazon famously operated at a loss for years before becoming a profit-generating machine. Other high-growth technology businesses have also prioritized expansion over short-term profitability. So why should Anthropic be any different? Because the economics of running a frontier AI company are absolutely bonkers. The real cost of running Claude Training and operating advanced AI models requires enormous amounts of computing power. We’re talking about expensive GPUs, massive data centers, electricity, networking infrastructure, and the ongoing costs of serving millions of users. Training a frontier model can cost hundreds of millions of dollars. And that’s before you account for the infrastructure required to make that model available to customers around the clock. Then there’s inference: the computing power required every time someone asks Claude to write code, analyze a document, or explain why their Python script has spontaneously developed an attitude problem. Multiply that by millions of users, and the computing bill becomes enormous. And remember that Anthropic also offers expensive subscription plans, including Claude Max at $200 per month. Depending on usage, serving some customers could be extremely costly. The key question is whether the revenue generated by those customers is sufficient to cover the full economic cost of serving them. But here’s where things get interesting. Anthropic’s reported operating loss doesn’t necessarily capture the full economic burden of acquiring the computing resources it needs to operate. Borrowing compute today, paying with equity tomorrow Anthropic has complex financing arrangements with major cloud providers, including Amazon and Google. These arrangements can involve convertible promissory notes and other instruments tied to future equity. In simple terms, Anthropic can secure computing resources and finance its infrastructure needs through arrangements that don’t require an equivalent cash payment upfront. That can be useful for a fast-growing company. It preserves cash and gives the business access to the infrastructure it needs to expand. But it doesn’t make the underlying resources free. There is still an economic cost, even when the payment doesn’t immediately show up as a conventional cash expense. According to the figures discussed in the filing, Anthropic recorded more than $72 billion in costs associated with these arrangements during the first half of 2026, alongside another $8 billion in other expenses. The exact accounting treatment matters enormously here. Changes in the fair value of equity-linked obligations can create large noncash expenses when a company’s valuation rises. Those expenses are not the same thing as cash burning out of a bank account, and they should not automatically be interpreted as ordinary operating costs. But neither should investors ignore them. Noncash does not mean costless. And financing something does not mean you have stopped paying for it. If the accounting figures are being interpreted correctly, these items would imply an extraordinarily large reported net loss, potentially around $80 billion for the six-month period. That is an eye-watering number. However, I would be careful about declaring it the largest loss in corporate history without comparing the precise accounting line items against other companies’ filings. A large fair-value adjustment is not directly comparable to a company losing $80 billion in cash through ordinary operations. Still, the broader question remains: how much does it really cost Anthropic to build and operate its AI business, and how much of that cost will ultimately have to be borne by shareholders? That’s the question I want answered before I start throwing money at the IPO. And then there’s the long-term infrastructure bill The other number that deserves attention is Anthropic’s reported long-term infrastructure commitments. The filing reportedly describes more than $500 billion in obligations associated with securing AI computing capacity over the coming decade. These commitments are especially important if substantial portions are non-cancellable. Imagine signing a contract that obligates you to pay for a huge amount of computing capacity over the next ten years. Now imagine that, three years from now, your AI models become less competitive, customers migrate to cheaper alternatives, or your revenue growth slows dramatically. You might no longer need all that capacity. Unfortunately, your contractual obligations don’t necessarily disappear just because your business plans have changed. That’s a serious risk. AI infrastructure is a particularly dangerous place to overcommit because the technology is evolving so quickly. The chips, models, and data centers that seem essential today could become economically unattractive much sooner than expected. The danger isn’t simply that Anthropic spends a lot of money. It’s that the company could commit to enormous infrastructure costs based on assumptions about future demand that turn out to be wrong. And that brings us to another problem. 2. Anthropic Is Increasingly Dependent on the Same Big Tech Companies It Wants to Compete Against One of the things that jumped out at me was how intertwined Anthropic’s relationships with major technology companies have become. Amazon and Google aren’t just infrastructure providers. Depending on the relationship, these companies can also be investors, customers, distribution partners, and competitors. Think about the implications. Amazon has its own cloud business and AI ambitions. Google has its own models, infrastructure, and enterprise products. Both companies have reasons to support Anthropic’s growth. A successful Anthropic means more demand for computing resources, more cloud revenue, and potentially greater returns on their investments. But they also have reasons to prioritize their own AI products and businesses. These interests don’t always align. What happens when your supplier is also your competitor? Suppose Anthropic suddenly needs substantially more computing capacity to serve a surge in demand. What happens if its … Get full access to AsianDadEnergy's Newsletter at asiandadenergy.substack.com/subscribe

  4. Oct 26

    The Internet Is BREAKING… And It's Only Getting Worse?

    Guys, I don’t want to sound like a doomsday prepper. But over the past several days, the cybersecurity headlines have been getting increasingly disturbing. Critical software vulnerabilities. Ransomware attacks. Stolen personal information. Industrial control systems being compromised. Attacks targeting organizations responsible for healthcare, energy, manufacturing, and food distribution. It’s starting to feel like the entire internet is one giant dumpster fire. And here’s the part that really worries me. AI is making it dramatically easier to find vulnerabilities that humans have missed for years, sometimes decades. I’ve spent 25 years working in technology. I used to be a software architect at a Big Tech company, and I’ve dealt with major cybersecurity incidents firsthand. So when I tell you that AI could fundamentally change the cybersecurity landscape, I’m not just some random guy yelling at clouds. Well, okay, I am a guy yelling at clouds on the internet. But I have some relevant experience. Let’s talk about what’s happening, why it matters, and what you can do to protect yourself and your family. 1. A 20-Year-Old Security Problem Just Got Uncovered One of the most alarming developments in the recent cybersecurity news cycle involves critical vulnerabilities in the PHP runtime, the software environment that executes PHP code on web servers. PHP is old. It has been around since the 1990s and remains a foundational technology for a huge portion of the web. WordPress, Drupal, Magento, and countless other websites and applications rely on PHP, directly or indirectly. The reported vulnerabilities are especially concerning because they involve remote code execution, commonly abbreviated as RCE. In plain English, remote code execution can allow an attacker to make a vulnerable server execute commands that the attacker controls. Depending on the vulnerability, the configuration, and the server’s security controls, that could potentially give an attacker access to sensitive files, databases, application secrets, or other systems on the same network. Imagine discovering that the front door of your house has had a defective lock for 20 years. Now imagine that millions of houses have the same lock. And nobody noticed until someone finally figured out how to pick it. That’s the sort of problem we’re talking about. The particularly unsettling part is that AI-assisted security research can help researchers examine complicated software and discover bugs that might otherwise remain hidden for years. The same technology that helps developers write code can also help security researchers find ways to break it. Of course, discovering a vulnerability doesn’t automatically mean every PHP website is exploitable. Actual risk depends on the affected versions, configuration, mitigations, and whether administrators have applied the relevant patches. But the broader lesson is difficult to ignore. We have built enormous portions of the internet on top of software that humans have been writing, modifying, and patching for decades. Some of that software is remarkably reliable. Some of it is held together with technical debt, obscure dependencies, and the digital equivalent of duct tape. And now AI is getting much better at finding the weak spots. 2. I’ve Lived Through This Nightmare Before This isn’t entirely new territory for me. Five years ago, just before Thanksgiving, the technology industry was hit by Log4Shell, a critical vulnerability in the widely used Java logging library Log4j. If you worked in enterprise software at the time, you probably remember the chaos. Log4j was embedded in countless applications and products. Some organizations knew exactly where they used it. Others had no idea. And even when you found a vulnerable dependency, fixing it wasn’t always straightforward. I was working as a chief architect at a Big Tech company when the incident hit. Due to a combination of organizational circumstances, I was the only available chief architect in my business unit to coordinate the response. So naturally, I got to help clean up the mess. I called an emergency office-hours meeting involving architects and technical leads from around 100 application teams. One hundred teams. One security incident. One extremely unpleasant holiday season. Our newly appointed business-unit CTO had inherited Log4Shell as his first major crisis. Understandably, he wanted answers, and he was watching our response closely. The mission sounded simple enough: get all the teams to patch their applications. Except enterprise software isn’t a neat collection of perfectly maintained applications with complete documentation and developers who understand every line of code. Some applications were ancient. Their original developers had left years earlier. The current teams barely understood how certain components worked. Sometimes applying the patch broke the application. Other times, the teams had to modify their own application code just to accommodate the patched dependency. And every change needed to be tested, reviewed, deployed, and monitored. It was a nightmare. Now imagine AI helping attackers identify vulnerabilities across enormous codebases while defenders are still trying to determine which systems they own, which libraries they use, and whether a patch will break production. That is the asymmetry that concerns me. AI can accelerate both attack and defense, but the defender still has to deal with the messy reality of existing infrastructure. Finding the bug can take minutes. Fixing a business-critical system safely can take days, weeks, or longer. And that’s assuming someone knows the system exists in the first place. 3. This Is Bigger Than Your Password Getting Stolen The recent cybersecurity headlines aren’t limited to vulnerabilities in web applications. We’ve also seen reports of attacks involving personal information, ransomware campaigns, industrial systems, and organizations responsible for critical services. Consider the different layers of modern society that depend on software. * Healthcare: Hospitals depend on digital systems for scheduling, records, diagnostics, and other essential operations. * Energy: Utilities rely on complex industrial control systems and connected management infrastructure. * Food distribution: Supermarkets depend on refrigeration, inventory systems, logistics, and transportation. * Manufacturing: Factories increasingly rely on networked computers and automated production equipment. * Government: Public services depend on databases, identity systems, communications, and online infrastructure. These systems aren’t all equally vulnerable, and a cyberattack doesn’t automatically translate into a physical disaster. But the potential consequences extend well beyond having to reset your password. A compromised website might expose customer information. A ransomware attack might disrupt hospital operations. An attack on industrial systems might interrupt production or logistics. A sufficiently disruptive incident could affect services that ordinary people depend on every day. And here’s the uncomfortable truth: modern infrastructure is interconnected. The United States has many security controls, segmented networks, backup systems, and specialized protections. Critical infrastructure is not simply one giant computer connected directly to the public internet. But neither is it completely isolated from the digital world. Remote management, cloud services, vendor access, software dependencies, and operational technology create connections that must be secured. There is no magical force field protecting civilization from software bugs. Which brings us to the elephant in the server room. 4. Why AI Makes This Problem Different Software has always contained bugs. That isn’t a controversial statement. It’s a fact of life for anyone who has written enough code. Modern applications can contain millions of lines of code, depend on hundreds or thousands of external libraries, and interact with operating systems, databases, network services, and other appli… Get full access to AsianDadEnergy's Newsletter at asiandadenergy.substack.com/subscribe

  5. Oct 23

    Is This the END of LLMs? NCM Could Change Everything

    NCM: The LLM Killer? Stanford May Have Changed AI Forever Hello world. I’m an unemployed ex-Big Tech software engineer with 25 years of experience in the tech industry. And one of the perks of involuntary early retirement is that I have plenty of time to follow the latest developments in AI. Which is great, because the AI industry seems determined to change the world every five minutes. But here’s something I’ve been thinking about for a while. Almost the entire AI conversation revolves around Large Language Models, or LLMs. Every few months, we get a bigger model, a more expensive model, a new benchmark, and another announcement about how this one is definitely going to change everything. Meanwhile, the companies building these models are pouring staggering amounts of money into GPUs, data centers, electricity, and cooling infrastructure. And yet, today’s LLMs have some fundamental limitations. What if the next major AI breakthrough doesn’t come from making LLMs bigger? What if it comes from building AI differently? Well, researchers at Stanford University have been exploring a new architecture called Next-Concept Modeling, or NCM, and the early results are interesting enough to deserve our attention. Could this be the beginning of the end for LLMs? Maybe. Maybe not. But let’s talk about why this research caught my attention. 1. The Problem With Today’s LLMs First, let’s talk about how conventional LLMs work. At their core, these models predict the next token based on the prompt. A token might be a word, part of a word, or another small unit of text. The model predicts a token, adds it to the sequence, and then predicts the next one. This process continues until the model has generated its response. In grossly simplified terms, it’s a huge autocomplete. But their autoregressive generation process has a major drawback. The tokens in an answer are generated sequentially. Suppose you ask an LLM to generate a 1,024-token response. The model generally needs to perform a forward pass for each newly generated token, meaning roughly 1,024 sequential generation steps. There are optimizations, such as caching previously computed attention states, speculative decoding, and other inference techniques. So this doesn’t mean the entire model is recomputed from scratch every time. Nevertheless, generating long responses requires repeated computation, and model weights and associated data must be accessed as needed throughout inference. With models containing trillions of parameters, that can get expensive very quickly. And if you enable a reasoning mode that generates additional intermediate tokens, that only increases the number of passes and the amount of computation required before you get your final answer. The result is an AI industry that increasingly depends on enormous amounts of computing power, memory bandwidth, and energy. Now compare that with the human brain. The brain performs an astonishing range of cognitive tasks while consuming roughly 20 watts of power. Today’s frontier AI systems can require vastly more energy and computing infrastructure, particularly when training and operating at scale. That comparison isn’t perfectly apples to apples. Biological brains and digital neural networks work very differently, and their capabilities aren’t directly comparable using a single energy metric. Still, the contrast raises an important question. Are we approaching intelligence in the most efficient way possible? I’ve argued before that if we see major algorithmic breakthroughs that move us closer to more capable reasoning systems, they might come from somewhere outside the architectures dominating today’s AI industry. And this is where NCM gets interesting. 2. Meet NCM: A Different Way to Generate Text Next-Concept Modeling takes inspiration from an idea used in another area of AI: diffusion models. If you’ve ever watched an AI image generator create an image, you’ve probably seen how it starts with noise or a rough representation and progressively refines the result until a recognizable image emerges. Instead of generating an image one pixel at a time, diffusion-based approaches refine a representation of the image through multiple steps. NCM applies a related idea to text. Rather than generating an answer one token at a time, the model represents the answer through multiple layers of increasingly detailed concepts. Think of it as creating a rough blueprint of an answer first, then progressively filling in the details. At the highest level, the model represents broad concepts using a relatively small number of codes. The next layer adds more detail. Subsequent layers refine the representation further, until the final layer contains the token-level information needed to reconstruct the text. The key difference is that each refinement step works toward representing the entire answer, rather than simply adding one more token to the end of a sentence. That’s the big architectural idea. Instead of requiring a separate generation step for every output token, NCM can generate an output through a much smaller number of refinement steps. And if you can generate long responses with fewer sequential steps, you may be able to reduce latency and the repeated memory traffic associated with conventional token-by-token generation. Of course, fewer sequential steps don’t automatically mean less total computation. We’ll get to that important caveat shortly. But first, let’s look at the results. 3. The Early Results Are Pretty Wild The Stanford researchers trained several experimental NCM models, with the largest containing approximately 730 million parameters. That’s a relatively small model compared with the frontier systems developed by major AI labs. Nevertheless, their experiments produced some intriguing results. A potential 31x speed improvement In one benchmark involving text summarization, the researchers reported that NCM was approximately 31 times faster than a leading diffusion language model. Notice the comparison here. This was a comparison against another diffusion-based language model, not a conventional autoregressive LLM. That’s an important distinction. We shouldn’t take the result and immediately conclude that NCM is 31 times faster than ChatGPT or Claude. Still, it suggests that the NCM architecture may offer substantial speed advantages over existing approaches to parallel text generation. And that alone makes it worth investigating. The memory bandwidth advantage Let’s consider why this architecture could matter for inference efficiency. Imagine two hypothetical models of equal size, each with a 1 GB set of model weights. Suppose one is a conventional autoregressive LLM generating 1,024 tokens, while the other is an NCM model that generates the same-length output in 32 refinement steps. For a simplified comparison, assume each step requires reading the entire 1 GB set of weights from memory. The conventional LLM would require approximately 1,024 GB of cumulative weight reads. The NCM model would require approximately 32 GB. That’s a theoretical difference of 32 times less weight traffic under these assumptions. Actual memory traffic depends on caching, hardware, batching, model architecture, and implementation details. And this simplified example doesn’t account for all of NCM’s additional computational requirements. But it illustrates why reducing the number of sequential model evaluations could be valuable. Memory bandwidth is a major bottleneck in LLM inference. GPUs can perform enormous numbers of mathematical operations, but moving data between memory and processing units takes time and energy. If NCM can reduce that bottleneck substantially, it could change the economics of running AI models. And that brings us to the experiment that really caught my eye. The Sudoku experiment: 86 versus 1 The researchers also evaluated reasoning performance using Sudoku puzzles. They compared a 25-million-parameter NCM model against a conventional LLM with the same number of parameters. Both models were evaluated on 100 hard Sudoku board… Get full access to AsianDadEnergy's Newsletter at asiandadenergy.substack.com/subscribe

  6. Oct 20

    AI Just KILLED the Software Industry? One Guy Cloned Adobe!

    Let me tell you about a development that should make anyone working in the software industry a little nervous. Imagine spending decades building a software company worth billions of dollars. You hire thousands of engineers, invest enormous amounts of money in research and development, and build a portfolio of applications used by millions of people. Then one guy comes along, fires up an AI coding agent, and starts cloning your products. Welcome to the future of software. One Guy vs. Adobe and Microsoft Recently, a developer named Brandon Thomas attracted attention with ArtCraft, a project aimed at recreating the functionality of popular Adobe Creative Cloud applications using AI-assisted reverse engineering. The project includes applications inspired by products such as Photoshop, Illustrator, and Premiere Pro. Related projects extend the same idea to familiar office applications, including Word, Excel, and PowerPoint. These are not polished, feature-for-feature replacements for every original application. But that’s almost beside the point. The important development is that AI is making it dramatically easier to reproduce software functionality without rebuilding everything from scratch through traditional software development. I tried PhotoCraft myself. It wasn’t nearly as polished as Photoshop, but it worked for many of my basic image-editing needs. I also experimented with AI-assisted reverse engineering on a simple chess application. The resulting clone came surprisingly close to the original. And here’s the part that really caught my attention: I didn’t have to manually analyze every feature and write the replacement code myself. I could give an AI coding agent the task and let it do much of the heavy lifting. As a former Big Tech software engineer and architect, I find this fascinating. And potentially terrifying. For years, I’ve argued that AI is making software less valuable. These developments suggest that the process could accelerate much faster than many people expect. How Can AI Reverse-Engineer Software Without the Source Code? To understand why this matters, we need to look at how software reverse engineering works. When developers build an application, they write source code using programming languages such as C++, Java, or Rust. That code is typically compiled into machine code, which contains instructions a computer can execute. The resulting executable doesn’t normally include the original source code in its original, readable form. Traditionally, reverse engineering software required specialized expertise. Engineers used tools to inspect executable files, analyze program behavior, recover information about functions and data structures, and reconstruct how different features worked. This was difficult, time-consuming, and expensive. AI coding agents are changing that equation. An AI agent can work through an application feature by feature, using specialized reverse-engineering tools to gather information about the program’s behavior and internal structure. Tools such as Ghidra and IDA can help analyze compiled binaries. An AI agent can use the resulting information to reason about how a feature works, implement a replacement, test the result, and repeat the process. The basic workflow looks something like this: * Analyze: Inspect the original application and identify its features. * Investigate: Use reverse-engineering tools to examine relevant parts of the executable. * Reconstruct: Infer how a feature behaves from the available evidence. * Reimplement: Write new code that reproduces the feature’s behavior. * Test and repeat: Compare the replacement with the original and work through the remaining gaps. Repeat this process across enough features, and you may end up with a functional alternative to the original application. There are important limitations. Compiled binaries don’t necessarily reveal everything about the original source code, and some information may be missing or difficult to recover. Complex applications also have countless edge cases, integrations, and usability details that are hard to reproduce. A working clone is not necessarily a complete replacement. But the economic implications don’t require a perfect clone. If an AI-assisted developer can reproduce 80% of the functionality that most customers actually use, that might be enough to compete with the original product for a substantial portion of its market. The precise percentage is hypothetical. The underlying point is that customers don’t necessarily need every feature. They need the features they care about. And AI is lowering the cost of delivering them. The Real Problem: Software Is Becoming Easier to Copy Think about why software companies can charge so much for their products. Developing sophisticated software requires enormous investments in engineering, design, testing, infrastructure, and maintenance. Once a company has made those investments, it can distribute copies of its software to additional customers at relatively low marginal cost. That combination has historically been extremely profitable. Companies spend millions developing a product, then sell subscriptions to thousands or millions of customers. The original development cost is substantial, but each additional customer doesn’t require the company to rebuild the product from scratch. Now imagine AI dramatically reduces the cost of creating competing products. A developer no longer necessarily needs a large team to build a useful alternative. An AI agent can help implement interfaces, recreate common features, generate tests, and analyze existing applications. Reverse engineering adds another possibility: instead of starting with a blank screen and trying to figure out what customers want, developers can study an existing product and use its behavior as a reference. This changes the economics of competition. Historically, software companies benefited from the difficulty of reproducing their products. That difficulty created a barrier to entry. AI is attacking that barrier. And when barriers to entry fall, competition tends to increase. The result could be a world in which many software features that once justified expensive subscriptions become available through cheap or free alternatives. Adobe’s subscription business is an obvious example. But the implications extend to productivity software, project management tools, design applications, developer utilities, and countless niche software products. The question is no longer simply whether AI can help us build software faster. It’s whether AI can make existing software products much easier to reproduce. Are Small Software Developers Completely Screwed? I think smaller software developers could face some of the biggest challenges. Imagine spending three years building a niche application. You’ve identified a problem, developed a solution, found customers, and finally established a recurring revenue stream. Then someone uses AI to reproduce the core functionality of your product in a fraction of the time. What’s your competitive advantage? If your application consists primarily of features that can be observed and recreated, your moat may be much thinner than you thought. This doesn’t mean every small software company is doomed. Specialized expertise, customer relationships, proprietary data, integrations, reliability, and brand reputation can all provide meaningful advantages. But a product’s features alone may become a much weaker defense. And here’s the particularly uncomfortable part: AI doesn’t have to eliminate your entire business to hurt you. It only needs to make it easier for competitors to offer enough of your functionality at a lower price. Even if your product remains superior, you may find it harder to justify charging a premium. That’s a problem for any business whose value proposition depends on customers having few practical alternatives. Will Everything Become Open Source? My prediction is that we’ll see an explosion of open-source alternatives to proprietary software. Imagine a world where popula… Get full access to AsianDadEnergy's Newsletter at asiandadenergy.substack.com/subscribe

    AI Just KILLED the Software Industry? One Guy Cloned Adobe!

  7. Oct 16

    AI Could Trigger a New Dark Age? Here's How.

    There’s something deeply unsettling about watching a machine accomplish something extraordinary and realizing that the accomplishment itself might be the beginning of a much bigger problem. OpenAI has been pushing the boundaries of AI-assisted mathematical reasoning, including producing formal proofs for difficult mathematical problems. These developments are impressive, but they’ve also raised uncomfortable questions about the future of mathematics and the people who practice it. Because beneath all the excitement about AI solving difficult problems, there’s a much bigger question we should be asking: What happens when AI becomes so good at thinking that humans stop learning how to think for themselves? I call this the Mathapocalypse. I’m not suggesting mathematics is about to disappear. Rather, I think what’s happening in mathematics offers us a glimpse of a potentially disturbing future for human intelligence. Let me explain. Mathematics Is About More Than Getting the Right Answer I have a soft spot for mathematics. Back in college, I came pretty close to majoring in it. To me, math is a language that describes reality. There’s something elegant and beautiful about the simple truths humans have discovered through it. Mathematics is also a great example of a field where the process of discovery can be just as valuable as the final result. In many professions, the primary goal is to produce an output. A software engineer writes code. An architect designs a building. An accountant prepares a financial statement. The output is what the customer or employer generally pays for. If an AI can produce the same result faster, cheaper, and with fewer mistakes, it becomes difficult to justify paying a human to do the same work. But intellectual pursuits can be different. When mathematicians work on a difficult problem, they aren’t simply trying to produce a proof. They’re exploring ideas, developing techniques, testing conjectures, and discovering relationships they didn’t previously understand. Even when a particular approach fails, the effort can produce knowledge that becomes useful somewhere else. Sometimes, the real value lies not in the destination, but in the journey. Some of Humanity’s Greatest Discoveries Were Unexpected Side Effects Think about the history of mathematics and science. The development of calculus gave humanity powerful new ways to describe motion, change, and physical systems. Those ideas eventually became essential to engineering and physics. Number theory, once viewed by many as an abstract pursuit with limited practical applications, eventually became fundamental to modern cryptography. Fourier analysis, developed to study mathematical problems involving heat, became an important tool in signal processing, communications, and image compression. These developments didn’t happen because someone sat down and said, “Hey, let’s invent the technology that will power smartphones in a couple of centuries.” They happened because people pursued difficult intellectual problems, sometimes for reasons that seemed entirely disconnected from practical applications. The journey produced tools that nobody could fully anticipate at the beginning. Consider the Space Race. The competition to reach space generated advances in rocketry, satellite communications, integrated circuits, and other technologies that eventually found applications far beyond the original mission. Humanity didn’t just get astronauts into space. It developed capabilities that transformed life back on Earth. When humans investigate difficult problems, we sometimes discover things far more valuable than the answers we were originally looking for. Now imagine replacing that process with an AI that simply generates the answer. Would we still get the same unexpected discoveries? The answer is obviously NO. But there’s an important distinction between a machine producing a solution and humans understanding the process that led to it. And that distinction could become increasingly important as AI capabilities improve. What Happens When the Answer Becomes More Important Than Understanding? Imagine an AI solving a mathematical problem that has frustrated researchers for decades. It produces a proof. Technically, the proof is correct. But the proof is also enormous, convoluted, and difficult for a human to understand. Formal mathematical proofs can involve vast amounts of machine-generated code. A formal verification system may be able to check that a proof is valid even when the underlying reasoning is difficult for a human mathematician to follow. This creates an interesting problem. We might have a machine that can establish that a mathematical statement follows from a set of assumptions, while humans struggle to understand the result in a way that generates further insights. Of course, formal verification is a major advantage. A proof checked by a trusted formal system is not merely an AI confidently asserting that it is correct. But verifying correctness and developing human understanding are not the same thing. Suppose an AI produces a proof that is technically sound but offers little insight into why the result is true or how its techniques could be applied to other problems. We get the answer. But do we get the understanding? If this becomes the normal way mathematics is done, mathematicians could increasingly find themselves reviewing machine-generated proofs, checking their validity, and MAYBE trying to extract useful insights from them. That work would still be important. Someone needs to verify that these systems are doing what they claim to do. But it could fundamentally change the nature of the profession. The mathematician might gradually transition from being an explorer of the unknown to being an auditor of machine-generated knowledge. And that raises an uncomfortable question. If the most intellectually exciting part of the job is increasingly performed by machines, what incentive will there be for humans to pursue it? The Next Generation Might Simply Give Up Put yourself in the shoes of a young person with an exceptional talent for mathematics. Maybe this kid loves numbers, enjoys solving difficult problems, and dreams of becoming a mathematician. Then they watch AI systems solve problems that even the world’s best mathematicians struggle with. They see machines generating proofs, exploring mathematical possibilities, and producing results at a scale no human could reasonably match. And they start wondering what their future looks like. By the time this young person completes years of education and reaches the professional level, AI might already be better than them at everything. So why spend years mastering mathematics? Why endure the frustration of difficult proofs and the struggle to develop expertise when a machine can potentially do the work faster and better? Sure, they could use AI as a tool. They might even become more productive than previous generations of mathematicians. But what if the availability of that tool discourages them from developing deep mathematical intuition in the first place? Why struggle through a difficult problem when you can ask an AI for the solution? Why learn to derive a proof when a machine can generate one? These are not just questions about mathematics. They apply to software engineering, scientific research, design, finance, and practically every profession built around intellectual work. The question is whether these incentives will eventually change how many people enter these fields and how much expertise they develop. Because if they do, the consequences could extend far beyond individual careers. The Most Dangerous Part Might Be Losing the People Who Know When AI Is Wrong Here’s where my concerns become much more serious. Mathematics is a relatively small field, and some specialized branches have only a handful of people with deep expertise. These experts are often among the few people capable of understanding, evaluating, and challenging new results in their particular areas. Now imagine tha… Get full access to AsianDadEnergy's Newsletter at asiandadenergy.substack.com/subscribe

  8. Oct 13

    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 capabl… Get full access to AsianDadEnergy's Newsletter at asiandadenergy.substack.com/subscribe

See All (67)

Ratings & Reviews

5
out of 5
2 Ratings

About

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

Information

  • Creator
    Ivy-League educated, Ex Big Tech, Middle aged Asian Dad figuring out life.
  • Years Active
    2026
  • Episodes
    67
  • Rating
    Clean
  • Copyright
    © AsianDadEnergy
  • Show Website
    AsianDadEnergy's Podcast

You Might Also Like