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by AsianDadEnergy
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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- 2 days ago2h 10m
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
- 6 days ago17 min
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 IsA 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 TakeoffThe 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 InnovationI don’t think today’s AI needs genuine superhuman intelligence to b
- 1 weeks ago18 min
I Replaced Claude With Chinese AI?
I Let a Chinese AI Build My Entire AppMany months ago, I built an automation workflow that took my long-form Vlog videos, chopped them into short-form videos, and automatically posted those clips across social media.For months, almost nobody cared.Then something weird happened.Several of those shorts suddenly went viral.My TikTok, Facebook, and Instagram accounts started gaining subscribers, and I found myself spending more time on platforms I had previously ignored.And that’s when I discovered something incredibly annoying.Pretty much every social media platform wants you to have one link.Sure, some platforms technically allow multiple links. But there is usually one prominent link sitting right there on your profile.So I needed a simple webpage where I could put everything:YouTube.Substack.My various projects.Basically, one little page containing all the stuff I do on the internet.So naturally, I went looking for a solution.And that’s when I fell down the Link-in-Bio Rabbit Hole.The “Free” InternetThere are plenty of services that will happily let you create a page containing your links.Linktree.Beacons.Squarespace Bio Sites.And technically, many of them are free.But there’s a catch.They’re free in the same way that a casino buffet is free.They put their branding all over your page.Then they send you emails encouraging you to upgrade.And eventually you discover that removing their branding, using a custom domain, or getting some other basic functionality requires paying them significant money.Which got me thinking:Why am I paying a billion-dollar technology company to put six links on a webpage?I have been a software engineer for roughly a quarter century.I can build a damn webpage.So I decided to scratch my own itch.I would build my own link-in-bio SaaS.And then I decided to make the experiment more interesting.I was going to have a Chinese AI build it for me.Specifically, Kimi K3.Enter KimiKimi is an open-source/open-weight frontier AI model created by Moonshot AI.I’ve traditionally used Claude for most of my AI work, so this wasn’t about replacing Claude because I suddenly decided it was terrible.Quite the opposite.Claude is extremely good.But there’s something that bothers me about becoming completely dependent on a single AI company for increasingly large portions of our cognitive workload.If one company controls the models that we use to write software, analyze information, conduct research, and automate our businesses, then that company ultimately controls a surprisingly large piece of our productive capacity.So I wanted to see what an alternative looked like.And this was a perfect experiment.I’d build something real.I’d let Kimi do most of the work.And then I’d see what happened.How Do You Actually Build an AI Software Engineer?There’s a little more to this than simply opening a chatbot and typing:“Build me a SaaS application.”Modern coding agents are much more complicated.At the center of my setup was Kimi K3.Because I don’t have a giant AI data center sitting in my basement, I accessed the model through OpenRouter and used a U.S.-based reseller running the model on U.S. servers.Then I connected Kimi to an agentic harness.Think of the harness as the operating environment around the AI.The language model itself is the brain.The harness gives that brain memory, tools, and the ability to actually interact with the world.One important part of this is the context window.Kimi has a context window of roughly one million tokens.That’s a ridiculous amount of short-term memory.But even one million tokens eventually fills up.When that happens, the model can start losing important details about what it’s doing.The agentic harness helps solve this by maintaining persistent memory in Markdown files and periodically summarizing or compacting the conversation.It can even clear the context entirely and restart from those memory files.That’s basically the AI equivalent of:“Okay, I’ve forgotten everything. Let me read my notes.”The harness also gives the AI access to tools.A database.A browser.Remote APIs.Other software.And increasingly, these tools communicate through something called the Model Context Protocol, or MCP.Then there are skills.Skills are essentially reusable instructions that teach an agent how to perform specific tasks.And this is
- 2 weeks ago16 min
AI Just Destroyed the Internet?
A few weeks ago, one of my subscribers sent me a message that initially seemed like a pretty ordinary warning.Someone was impersonating me on YouTube.Apparently, scammers had created accounts using usernames and profile pictures that looked like mine. They were leaving comments on other people’s videos pretending to be me, offering investment advice and inviting people into WhatsApp and Telegram groups.Now, fortunately, some of the scams were hilariously obvious.Bad grammar. Weird phrasing. The sort of thing where you look at the comment and immediately think:“Yeah… Asian Dad Energy would never write this.”Except then I found some that were much more convincing.Much more disturbing.I found entire conversation threads where scammer bots were using phrases, words and mannerisms that I actually use.They weren’t simply copying and pasting some generic scam message.They were responding to people.They were following the conversation.They were gradually building trust and steering people toward private messaging groups.At that point, I started going down the rabbit hole.And what I found was considerably more unsettling than someone simply stealing my profile picture.Apparently, I Have a Digital Twin NowI discovered a TikTok account using an AI-generated picture of me.Apparently, AI-me is extremely happy, incredibly well dressed, and owns clothes that real me has never seen in my life.So that’s nice.My digital twin has better fashion sense than I do.The account was reposting clips from my videos along with content from other creators, apparently to generate advertising revenue.But then things got weird.I started finding AI-generated thumbnails and even entire videos featuring a person who looked nothing like me, but was sitting in a setting that looked remarkably similar to my home office.Someone had essentially taken my face, my content and even the visual environment around me and used AI to manufacture an entirely new version of me.And presumably, make money from it.This is where things get uncomfortable.Because there is something deeply unsettling about seeing an artificial version of yourself walking around the internet saying things you never said.It feels invasive.Like somebody took a piece of your identity, fed it into a machine and said:“Cool. Now make me some money.”And unfortunately, this isn’t just a problem for YouTubers.It’s a much bigger problem.Welcome to the Post-Truth InternetI think we’re rapidly approaching a point where AI-generated content becomes so convincing that the average person will no longer be able to reliably distinguish between what’s real and what’s fake online.And if that happens, the problem isn’t simply that we’ll have more scams.The bigger problem is trust.Imagine opening YouTube and having no idea whether the person speaking in a video actually exists.Imagine receiving a voice message from your mother and not knowing whether your mother actually sent it.Imagine reading a heartfelt post from someone you follow and having no idea whether they wrote it or an AI agent generated it.Imagine seeing a photograph of something happening in the world and having absolutely no way of knowing whether it actually happened.At some point, the question isn’t:“Is this fake?”The question becomes:“Can I trust anything?”And that’s where things get really interesting.Because the internet was built on an assumption of authenticity.We assumed that there was a real person behind an account.A real human wrote the comment.A real person recorded the video.A real photograph captured something that actually happened.AI is systematically destroying those assumptions.And there are three major reasons why.1. Digital Impersonation Has Become Ridiculously EasyGenerative AI has dramatically lowered the technical barrier required to impersonate another human being.Want to copy someone’s writing style?Give an LLM enough examples of their writing and it can identify their vocabulary, sentence structure, favorite expressions and recurring patterns.Want to clone their voice?There are services that can generate remarkably convincing synthetic voices from relatively small amounts of audio.Want to create a digital version of someone’s face?There are now countless AI services capable of generating realistic images and videos based on existing photographs and footage.In other words, your digital identity is increasingly becoming something that can be copied, modified and reproduced.And you don’t have
- 3 weeks ago14 min
Early Retirement Taught Me That We’ve All Been Sold a Lie
Nine months ago, I was laid off from my Big Tech job.After 25 years in the technology industry, I suddenly found myself outside the machine I had spent most of my adult life helping to operate.I call it involuntary early retirement.And I know how fortunate I am.I had spent years working in well-paid technology jobs, saving aggressively and investing. That eventually gave me financial independence, the ability to pay for my life without needing a traditional paycheck.So when the layoff came, I didn’t have to immediately scramble for another job.Instead, something strange happened.I got something most people spend their entire lives chasing:I got my time back.And with that time came a perspective I never had while I was working.For the first time, I was able to step outside the flow of normal life and actually watch it.And from that vantage point, I’ve started to wonder whether many of the things we’ve been taught about how to live are fundamentally wrong.Maybe we’ve been sold a pack of lies.Not necessarily by some evil mastermind sitting in a dark room plotting against us.Rather, these are ideas that have been passed down through families, schools, workplaces, media, and culture for generations.Ideas that seem so normal that we rarely stop to question them.Here are four of them.Lie #1: Compliance Will Keep You SafeMost of us are trained to comply from the moment we’re children.Get good grades.Listen to your parents.Get into the right school.Get a good job.Show up on time.Work hard.Follow the rules.Don’t cause trouble.I grew up in a fairly traditional East Asian family, and this conditioning was particularly obvious.Approval was often earned through obedience.Do what you’re supposed to do, and you are rewarded.Go against the expectations of your family or society, and you’re risking disapproval.And this conditioning doesn’t end when you become an adult.It simply changes form.At university, you’re expected to follow the curriculum.At work, you’re expected to follow instructions.And somehow, being constantly busy has become a proxy for being important.Look busy.Answer your emails.Attend the meetings.Hit your deadlines.Work late.Keep producing.Eventually, we internalize the idea that our value comes from our usefulness to the machine.But here’s the uncomfortable part:The machine doesn’t love you back.You can spend decades being the perfect employee and still get discarded when you’re no longer needed.A company can eliminate your position because of a reorganization.Your skills can become obsolete because of new technology.Your health can deteriorate.You can simply get older.And suddenly, after years of loyalty and compliance, you’re out.That’s what being laid off taught me.I had spent 25 years becoming increasingly valuable within the system.And then one day, the system simply decided it didn’t need me anymore.That’s when I realized something:Compliance was never a guarantee of safety.And it certainly wasn’t proof of my intrinsic worth as a human being.I was simply a replaceable component in a very large machine.Lie #2: More Freedom Automatically Increases Well-BeingThis one is much harder to accept.Because freedom sounds wonderful.Who wouldn’t want complete control over their time?Imagine waking up every morning with nowhere you have to be.No boss.No commute.No deadlines.No meetings.No one telling you what to do.This is supposed to be the ultimate reward for financial success.Work hard.Make money.Invest.Become financially independent.And eventually, you’ll be free.Except there’s a problem.Freedom doesn’t automatically give you a reason to use it.After my layoff, I had almost total autonomy over my time.And almost immediately, I started drifting toward the path of least resistance.Social media.YouTube.Video games.Television.Other forms of passive consumption.Basically, anything that required very little effort.And after a while, I found myself in an existential spiral.Because consumption isn’t the same thing as living.You can spend an enormous amount of time entertaining yourself without actually doing anything meaningful.That’s when I realized something uncomfortable:A job doesn’t just give you money. It gives your day structure.It gives you obligations.It gives you goals.It gives yo
- Aug 19, 202611 min
You’re Not Supposed to Live Like This?
Hello, world.I’m an unemployed former Big Tech software engineer with 25 years of experience in the technology industry.And one of the strangest things about early retirement is discovering something I never seemed to have enough of while working:time.Free time gives you the opportunity to notice things you were too busy to notice before.And once I slowed down, I started thinking about a paradox that I find increasingly difficult to ignore.The Good Life ParadoxLook at the news. Scroll through social media. Listen to people talk about their lives.It seems like we’re living through crisis after crisis.A cost-of-living crisis.A loneliness crisis.A mental-health crisis.A birth-rate crisis.A meaning crisis.And underneath all of these is a broader feeling that something has gone fundamentally wrong with our society.Yet here’s the strange part.If you look at our material standard of living, we are living in an age of extraordinary abundance.The average American today has access to things that would have been unimaginable to even the wealthiest people a few centuries ago.Our homes are heated in the winter and cooled in the summer.We have clean running water and electricity.We can eat food from virtually anywhere on Earth, year-round.We have modern medicine.We have instant communication with people on the other side of the planet.And we carry, in our pockets, access to an astonishing percentage of humanity’s accumulated knowledge.A relatively ordinary person today can watch a movie, have dinner, talk to someone across the world, summon transportation, access a library containing millions of books, and ask an AI to explain quantum mechanics, all before going to bed.By almost any historical measure, we live incredibly comfortable lives.And yet...A huge number of people don’t seem particularly happy.So how can a civilization achieve unprecedented material abundance while simultaneously experiencing widespread loneliness, anxiety, dissatisfaction, and a crisis of meaning?Maybe the problem isn’t that we have too little.Maybe we’re optimizing for the wrong things.So What Actually Makes a Good Life?This question is obviously not new.Human beings have been asking it for thousands of years.Philosophers, religious teachers, prophets, and ordinary people have wrestled with the same fundamental question:What does it mean to live well?What’s fascinating to me is how much overlap exists among traditions that developed independently of one another.Different civilizations disagreed about countless things.But when you strip away the cultural details, many of them keep circling around a remarkably similar set of ideas.I would boil them down to three things.1. Meet Your Basic Material NeedsFirst, you need enough material security to survive and live with dignity.Food.Shelter.Clothing.Safety.A place to sleep.You don’t need a mansion.You don’t need a Lamborghini.You don’t need the newest iPhone.You need enough.And historically, there was also an interesting connection between work and human dignity.The idea wasn’t necessarily that you needed to spend every waking hour maximizing your economic output.It was that contributing through honest work could be part of living a worthwhile life.The goal was never supposed to be endless accumulation.It was supposed to be having enough to live well.2. Build Strong RelationshipsThe second ingredient is relationships.Family.Friends.Neighbors.Community.People you love and people who love you.People who depend on you.People you can depend on.A human being isn’t designed to exist as an isolated economic unit.We need other people.We need to serve other people.We need to care for them.We need to forgive them.We need to be forgiven.We need companionship.We need belonging.And perhaps most importantly, we need to feel that we matter to someone.You can have an enormous bank account and still be profoundly lonely.3. Find Meaning and PurposeAnd then there’s the big one.Meaning.Purpose.A reason to get out of bed in the morning.Different cultures have different names for this.Ikigai.Raison d’être.Calling.Mission.Faith.Purpose.The terminology changes, but the underlying idea is remarkably consistent.Human beings need something that makes their existence feel meaningful.
- Aug 17, 20261h 59m
Asian Dad Energy LIVE Office Hours
Hello World!I'm finally scratching the live-streaming itch and putting my OBS Studio setup through its paces. This is basically a live test run of my cameras, microphones, scenes, overlays, and all the other Techy nonsense I've been tinkering with.While I'm here, let's talk about something a little more serious: Is the AI Bubble about to burst? I'll share my thoughts on the current state of the AI boom, whether we're in an actual bubble, and what I think could plausibly happen next.Come hang out, watch me potentially break my streaming setup in real time, and let's talk about AI, tech, and the future.No promises that everything will work. That's half the fun. 😅Timestamps:00:00 Introduction & Stream Setup Check06:54 The Biosphere & Boogie Worm Ecosystem12:35 Sound Effects & Diagnostics14:00 What is an "Office Hours"?18:15 Storytime: Dealing with Log4Shell in 202125:52 History & Evolution of LLMs29:18 Key Improvements in Modern AI (RL, Tools, Chain of Thought)34:37 Agentic Harnesses & Loop Workflows38:40 Do LLMs Reason? (The Jacobian Conjecture)41:40 Real-World Impact of AI on Software Engineering46:01 The 3 Pillars of AI: Compute, Algorithms, Data49:25 Limits of Silicon Compute & Moore’s Law51:10 Algorithmic Gains vs Compute Scaling56:10 US vs China AI Competition & Sanctions01:12:40 Semiconductor Blockade Workarounds01:17:40 Historical Context of Neural Networks01:23:30 Local AI Models, Quantization & Privacy01:32:40 Data Center Bottlenecks & Memory Supply Chain01:39:00 The AI Financial Bubble & Circular Financing01:42:20 Current AI Limitations & Bubble Risk01:48:40 Audience Q&A01:58:35 Wrap-up & Sign-off Get full access to AsianDadEnergy's Newsletter at asiandadenergy.substack.com/subscribe
- Aug 11, 202613 min
NVIDIA Is a Dead Man Walking? Here’s Why.
NVIDIA has become one of the great corporate success stories of the AI boom.Its GPUs power much of the infrastructure behind today’s frontier AI models. Revenue has exploded. Profitability has exploded. Its market capitalization has reached almost incomprehensible levels.And because NVIDIA sells the “shovels” during the AI gold rush, the conventional wisdom seems pretty straightforward:Even if the AI bubble eventually bursts, NVIDIA wins.After all, somebody still has to sell the picks and shovels.I’m not convinced.In fact, I think there is a scenario in which NVIDIA becomes one of the biggest casualties of the next phase of the AI revolution.Not because its technology suddenly becomes bad.But because the economics of AI could fundamentally change.NVIDIA’s Moat Depends on One Big AssumptionThe bull case for NVIDIA ultimately rests on a simple proposition:AI has an essential dependency on NVIDIA GPUs.Today, that proposition looks pretty damn convincing.Frontier models require enormous amounts of computing power. Companies like OpenAI and Anthropic have traditionally trained their models using massive clusters of NVIDIA GPUs inside enormous data centers.And NVIDIA’s data-center business is now overwhelmingly important to the company.The logic therefore seems almost circular:AI gets bigger → AI needs more compute → more compute requires NVIDIA GPUs → NVIDIA makes more money.But what happens if the amount of compute required to produce useful AI falls dramatically?What happens if frontier models become increasingly commoditized?And, perhaps most importantly:What happens if AI inference moves out of the data center and onto the devices sitting on our desks?That’s where things get interesting.The First Problem: Frontier AI Is Becoming CommoditizedOne of the most interesting developments in AI isn’t happening in Silicon Valley.It’s happening in China.U.S. restrictions on advanced NVIDIA chips have forced Chinese AI companies to become extraordinarily creative with limited computing resources. They’ve developed alternative hardware and software stacks while finding ways to train increasingly capable models with less compute.The result is a strange paradox.The harder the United States tried to restrict China’s access to advanced AI hardware, the stronger the incentive became for Chinese companies to figure out how to build AI without it.And we’re now seeing highly capable open-weight models emerge that can compete surprisingly well with leading proprietary systems.The important point isn’t whether one particular Chinese model is better than Claude or ChatGPT.The important point is what happens when the model itself stops being scarce.If someone can download a highly capable frontier-class model for free, the economic value begins moving somewhere else.The model becomes a commodity.And once the model becomes a commodity, the question changes from:“Who has the best AI model?”to:“Where should we host all of this AI?”That distinction could be enormously important for NVIDIA.The AI Revolution Has Two Different ProblemsThere’s a distinction that often gets lost in the AI discussion:Training is not the same thing as inference.Training is the process of creating the model.Inference is what happens every time you actually use it.Every time you ask ChatGPT a question, summarize a document, generate an image, write some code, or run an AI agent, you’re performing inference.And I think inference could become NVIDIA’s Achilles’ heel.Why?Because inference has a very different economic profile from training.For inference, the bottleneck isn’t always raw computational power.It can be memory.Consider a hypothetical near-frontier model with hundreds of billions of parameters.A mixture-of-experts architecture might only activate a relatively small portion of those parameters for any individual token. The actual computation required can therefore be surprisingly manageable.The problem is that the entire model still needs to reside somewhere in memory.That’s where things get interesting.What If Your Mac Can Run Frontier AI?Imagine you want to run Deep Seek V4 Flash, a roughly 284-billion-parameter model locally.You might need around 90–100 GB of memory to hold the model.NVIDIA’s obvious solution is to use an expensive data-center GPU with enormous amounts of high-speed VRAM.And if the model gets even larger?Add more GPUs.Connect them using NVIDIA’s proprietary high-speed interconnect technology.Add network
About AsianDadEnergy's Substack Podcast
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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