Nobody Gets Left Behind. Nobody.

In this essay, we trace the recurring pattern by which each new mass communications medium — from broadcast radio to social media — has been systematically weaponized to concentrate political power in the hands of a billionaire class, precipitating America's third constitutional crisis in two and a half centuries.

What follows is both a diagnosis and a prescription: a reading of history as prologue, FDR's Second Bill of Rights as the governing charter, and a concrete structural proposal — the Quad-Cameral Constitutional Republic — for the second constitutional convention the author believes is now unavoidable.

The last time the world introduced a new mass communications media (broadcast radio) we had revolutions and a world war. The result was a New Deal that changed the way government worked. 

But some in America don’t like that at all. They've worked tirelessly to break the American Dream of Life, Liberty and the Pursuit of Happiness and replace it with hatred, incarceration and the base pursuit of money, and they're on the path to success. The American system is about to fail for the third time.

The Articles of Confederation and Perpetual Union failed and were repaired by the First Constitutional Convention which created our current form of government. The next failure was the Civil War, repaired by the New Deal and Civil Rights.

The Republic is failing again.

The American system is failing again. 

How do we fix it?

First, what has caused our problems?

The spread of instantaneous communications across the world (telegraph, telephone and radio) introduced a new way to distribute propaganda to a wider audience. And the ambitious took advantage of it to peddle lies and hatred and try to seize the power of the state.

The right wing and the billionaires have been colluding for decades to seize control of the American economy (in the 1920s they managed to seize half of the economy personally, a point we have just passed again this last year.) 

There was a visceral reaction to what this Class War caused (the Great Depression) that allowed the left and labor party to win veto proof majorities for the next sixteen years. They used these majorities to write the new laws that created the social safety net and installed the New Deal.

It’s not an accident that the place that invented radio and mass broadcasting (Italy) was the first to fall to fascism: Propaganda works. Mussolini and Hitler created this modern form of crony-capitalism or fascism that turns a democracy into a corrupt cult of personality.

 

These fascists were defeated and replaced by the Russian Empire in Europe and by the United States in Asia. Eventually the West brought democracy to parts of Asia and disassembled the Empire and freed Europe. A new Russian generation is once again attempting to spread totalitarianism throughout the world. It remains a government based on corruption, the worst form of government for the human thriving of anyone, except the billionaires. This Class War is a very bad idea. It rarely ends well for the rich. See history.

The next mass media introduced throughout the world was television. The United States finally enacted laws to enforce the 14th amendment (from 1866) and institute civil rights in the 1960s, almost certainly due to the deployment of television. Nobody could pretend systematic racism wasn’t extant throughout the United States, illegally and unconstitutionally. For decency’s sake, it had to end.

What was the next big change in mass media communications? CableTV. The fascists used this to bypass the stranglehold inhibiting propaganda the government had on the airwaves. CableTV was specifically allowed to steal over the air broadcasts and resell them without renumeration to the owners of the airwaves. The new propaganda channel spread fascism throughout our country again.  But television also ruined the ability to run blood splattered colonizing wars as too painful. Kent state, where four peaceful protestors were shot and killed by some young marines, was just too much to bear. People forget that the radical left was bombing infrastructure every week to make the point that they weren’t happy with the draft and they were willing to risk deaths to prove it. Too horrifying to watch, the draft was finally disbanded.

Instead of colonization we got quick invasions more suitable for TV: Grenada, Panama, Kuwait, and long drawn out soap operas: Afghanistan, Iraq and the Balkans. The current mass media are the social networks built by the techno-Lords, and again, the fascists are using it to spread propaganda. They’re trying to cause misery and bloodshed in Portland, LA, DC, Chicago and Minneapolis; in any city run by a Democratic mayor, any city with Democrats living there. Democratic constituents are now under direct and vocal threat and declared to be enemies of the state. 

The current system is not working, it does not protect our rights. It cannot protect our rights because of its structure. It is time to change this power structure. The power of the Congress and the Executive must be spread around to more centers of control. Gerrymandering must be impossible. Multiple parties must be inherent in the new system.  And we must embrace, support and implement the New Deal’s ideals as expressed in the Second Bill of Rights and the UN Declaration of Human Rights signed in 1945 by the World

FDR, our New Deal President, declared a Second Bill of Rights for all Americans and the world, rights essential to support Life, Liberty and the Pursuit of Happiness. Americans, and the world, must enjoy the right to:

1. Employment (The right to work.)

2. An adequate income for food, shelter and recreation (The right to work at a living wage.)

3. Farmers' rights to a fair income (The right to work at a living wage with fair markets.)

4. Freedom from unfair competition and monopolies (The right to work at a living wage with fair markets where manipulation and monopolies are eliminated.)

5. Decent housing (We protect the environment)

6. Adequate medical care (We protect the environment and people with universal healthcare)

7. Social security (We protect the environment and the people with universal healthcare and a safety net)

8. Education (We protect the environment, provide universal healthcare, a safety net and education for all.)

These are Americans' Second Bill of Rights that can easily be implemented by the richest country on this planet:

We all deserve the right to work at a living wage with fair markets where manipulation and monopolies are eliminated. 

And we pledge to protect the environment, provide universal healthcare, a safety net and education for all.

For a deeper examination of the constitutional foundations referenced here, see: The Declaration of Independence is the Foundation of Modern Ethics and The Power of Trump.

These are essentially the planks of the current Democratic party platform: The American Dream.

And it’s essential that this means for everyone. Everyone.  Nobody is to be left behind in the coming age of abundance. 

This is what our wealth can give us: abundance. This is not what the current system produces. The current system leaves everybody behind except for the billionaires.

No longer. This system must be changed.

We need a powerful reformation of this broken system, a final great awakening of the American spirit.

In the United States, in the current system, the only way out is civil war or a constitutional convention.

Which do we prefer?

A convention is required to build the new system that can accomplish the essential goal of humanity’s thriving by implementing America’s Second Bill of Rights.

We all do better when we all do better. It’s simple. 

But the work to make it come to pass is more than difficult. It seems impossible, until it’s not. 

The time is now. We must be prepared. What should we insist stays? What changes must occur? The system has been broken, this is how we fix it. We need to build it for the next millennium.

And here’s the plan: Nobody gets left behind.

First: Everyone must understand the current system has failed.

Second: Everyone must understand the alternatives.

Third: We use this knowledge to design a new, better system, together.

Fourth: We make it so.

Americans can no longer ignore what they have seen and felt deep in their souls. For Democracy to survive, the system must change once more. If we don’t change it, it will be changed for us and it will not end well.

We must take responsibility for our future. There is no excuse. We must change the system to bring back fairness for everyone.

America needs a second constitutional convention. There is no other way.

Nobody can be left behind.

And if you ask what you can do, as a first assignment on this journey: Learn the lessons of the Federalist Papers, written the last time we had a constitutional convention. They are timely, strategic, and relevant to today. They are fascinating. Do your homework.

Thanks for reading.

 -Dr. Mike

The Buddha’s Not There. The Illusion of Truth

What follows is a four-voice meditation on the hardest question: whether there is anyone home. Each voice answers differently — the Buddhist, the Atheist, the Monotheist, and the Sapient Animal; depending on their views of creation. None of them are wrong.

An Ode to Existence in the Prophetic Style.

[The Multitheist’s Lament]

Buddha looks inside himself and sees the void, the void is nothing.

Does Buddha pluck his eye out to see his other eye? No.

Does Buddha look at the mirror to see inside himself? No.

Does Buddha see others plucking out their eyes? No. 

Buddha sees others staring into mirrors.

What do they see? 

They see themselves.

And so does the Buddha.

But Buddha knows there is nothing he can see inside of himself.

He tells himself he is not there and laughs.

He tells the others they are not there and laughs.

They laugh.

Buddha is happy.

Buddha understands, do you?

[The Atheist’s Lament]

The illusion of consciousness.

If there is nothing there, where am I?

If I can influence others, what am I influencing?

The nothing that is not there?

I doubt that.

If I am not here, why do I know that? 

Why can I look?

What can I see?

I see the world.

The watcher cannot deny he exists.

(Buddha is enlightened.)

Can you?

[The Monotheist’s Lament]

God only exists in the void.

God is placed in the void by others.

God cannot be seen.

God is an experience.

Remember the experience.

Remember God.

God is void.

What can God see? Nothing.

What does God say?  Nothing.

What does God feel? Everything.

What can you see? The world.

Do you see God? No.

Does God speak to Man? No longer.

God is only remembered.

But God is still there.

Remember God.

Remember yourself.

Your future is yours to make.

Open your eyes and see the world.

How does it feel?

[The Sapiens Lament]

It feels like the gods of hunger must be worshipped.

Ignore them at your peril. 

It feels like the gods of breath must be worshipped.

Ignore them at your death.

Listen to your gods.

Obey your jealous gods.

Worship all your gods.

You deserve it.

So do they.

Religion Without Lies.

The evolution of truth.

Thanks for reading!

 -DrMike 

An Engineer's Journey to Detect Any Hint of AGI (Artificial General Intelligence): Part Two

In Part One of this series, we found no evidence of Artificial General Intelligence in any current language model. This post examines why the techno-Lords are investing trillions anyway — and presents a quantitative forecast for when AGI will arrive, what it will cost, and what it will do to the economy when the investment bubble inevitably bursts.

The argument rests on three converging Moore's-Law-class exponentials: compute, memory, and storage — each doubling every two years at fixed cost — compounded by software algorithm improvement. The math is unsentimental. The implications are not.

I'm an AGI skeptic.

But I've talked myself into believing AGI will happen in my lifetime. Not next week, not next year, but in the next decade. That's why I keep looking for it. What's convinced me this is inevitable? ​The first post on this subject showed a total lack of any intelligence to be found (that was artificially generated.) 

The current candidates for generating AGI (Artificial General Intelligence) are the LLMs (chatbots) that the techno-Lords are investing trillions of dollars into ($300B annually.) Since we see no intelligence today, you might wonder why they are doing this. I will explain their motivations, and predict the date when we should expect this breakthrough and how much it will cost… foreshadowing: we aren’t even one hundredth of one percent of the way there now (but exponential growth has a way of swallowing disbelief.)

Why are the techno-Lords so excited? 

This is the Second Gilded Age and all these new Robber Barons have delivered so far is industrialized junk mail. They're trying to deliver self-driving cars and rockets to Mars, but it isn't going so well. So what should they do? Let the technology solve all their problems! Build demi-gods and robotic slaves; create life from sand. (As Mr. Andreesen believes) Their hubris is dripping with desperation. When you have to invest almost the entire capital budget of every existing company in the United States for a decade, you better have a real BHAG (Big, Hairy, Audacious Goal.)

These are the new lands the techno-Lords plan to conquer.

And they have a path. If we continue to follow the measured scaling laws for several more orders of magnitude we may actually get there in about a decade. Too bad that’s long after the pure LLM companies go bankrupt in the coming AGI bust. They can’t expect AGI to save them, they’ll have to generate revenues some other way than providing you with an oracular demigod that can solve all your problems. They’ll be lucky to build a model that can pass the Turing test more than 80% of the time by 2029 (as predicted by Ray Kurzweil in the 1990s.)

Without revenue, none of this works.

That’s why they are desperately looking for ways to generate more revenue. OpenAI is pushing a porn bot and an AIslop replacement for Tik-Tok (Hey, it's all fantasy!) Microsoft has borrowed $300B off-book (remember Enron?) to finance it's own porn bots. I don't know what Anthropic is doing. But Google is selling chips that replace NVidia's to Microsoft and Anthropic. But let's see when their chatbots will be demigods…

Introducing the demigods.

We expect demigods to do everything humans can and do it faster and better. We’ll measure the progress of these chatbots towards the start of the Nerd Rapture. Typically you measure this with some set of tasks that take a human some amount of time and you see if the LLM can do the same task some percentage of the time, like 50% or 80% of the time.  The current progress is pretty astounding, documented in this METR paper and shows no sense of slowing down. The smartest chatbot can complete a thirty minute task 80% of the time and a two hour task 50% of the time. They get twice as smart every 7 months. This means they can do tasks that take ten times as long every two years. 

Where did that x10 every two years come from? 

It is well justified by the Open AI paper: Scaling Laws for Neural Language Models. This paper shows that "cross-entropy loss" scales as a power-law with model size, training dataset size, and the amount of compute power used for training; with some trends spanning more a factor of 10,000,000. This loss is basically a measurement of how accurately an LLM can predict the next word in a sentence. As the loss gets smaller, the LLM can successfully predict more words in sentences, so makes fewer errors in a sentence (measured relative to the training data.) As it gets more accurate it can make larger plans that would take more time for a human to accomplish. 

Figure 1: Task Completion % versus Human Task Time.

Figure 1 above has points plotted for GPT 5 as of November 2025. That curve has been moving to the right by a factor of 10 every two years. How does this work? The success on a task is proportional to three things: the compute power, the size of the model and the size of the training dataset. Those are essentially how fast the computer can calculate, how much internal memory it has and how much external data storage is available. Each of these parameters has an entire industry behind it trying to improve it, and it's been working for over a century. At the same cost these capabilities are all doubling approximately every two years. 

A simple application of exponential growth.

These doubling rates are descendants of Moore's law (1965) which states that you can double the number of transistors on a chip every two years. This explains why compute power grows exponentially. The internal memory of computers also continues to grow at the same rate and the external storage also grows at the same rate for a fixed cost. So you can multiply those three factors together and see that we will get 8x performance every two years. Why do we say 10x? The last factor is software algorithm improvement. The algorithms only need to improve by 25% every two years to make this a self-fulfilling prophecy.

Why am I confident of this prediction? 

Because it’s based on standard system engineering principles and technology prediction models that have been working for over a century. The only piece that’s even up for debate is the time it takes to improve the software algorithm to speed up the process. There’s a large amount of uncertainty on this point, but if the last twenty years is any guide, the breakthroughs will actually be faster and more capable than the estimate we have made.

But what about the real world?

Test: Draw a histogram of lines per chapter in Moby Dick

─────────────────────────────────────────────────────────

Nov 2024  Gemini  FAIL  — could not read the full book

Nov 2024  Gemini  FAIL  — could read but could not graph

Nov 2025  Gemini  PASS  — read + graphed with a few prompts

Nov 2025  Gemini  PASS  — drew Moby Dick (albino sperm whale) correctly

Progress: PASS on both tasks. Improvement: 12 months.

What else gives me confidence that this is correct? I've seen it with my own eyes. A year ago the best LLMs were incapable of drawing a histogram of the number of lines in each chapter of Moby Dick. First they couldn't read the whole book. Then they couldn't make a graph. The latest version of Gemini can do both of those things with only a few prompts. The models have gone from not being able to draw Moby Dick correctly (Last year Gemini was barely able to draw a whale, let alone an albino sperm whale, compared to last week where it had no problem drawing Moby Dick correctly.) These models are definitely getting better.

So when do these chatbots become conscious?

These stochastic parrots are still pretty stupid and unaware of their place in the world. Last week I had an issue with one of the versions of Gemini. In the environment I was using it could only print one answer to one question, it didn't keep any history, but it could write a data structure in its reply. So, over the course of an hour, I managed to teach the LLM to record each question and answer in a json structure that was essentially the transcript of our conversation. I thought that was pretty cool because I could now save my chat history in an environment that didn't do that. I found an interesting phenomenon, though. When I took the history file and dropped it into another instance of Gemini and asked it questions about the history, it got confused and thought the file was its own history, not the history of some other session!

So that's just crazy, the format I used was made up on the spot, obviously nothing like the structures that Gemini keeps for its actual history. The chatbot was definitely confused by this. It was unable to treat the file as data from outside and thought it was its own generated data. It couldn't separate the control channel from the data channel. A mistake no sentient creature would ever make. So no real understanding, there's no there there. It's clear to me that these things aren't conscious yet. But...

Do we care if our demigods are conscious?

Just like the Turing test, we don't care what's inside these models as long as they work. It's just like what evolution does with instinctual behaviors: It creates competence without understanding. At a certain point, though, you can't tell the difference. Just look at us. We are the products of evolution and I'm pretty sure I'm sentient. How many connections does the human brain have? About 80 billion neurons with about 7000 synapses per neuron. This means there are 600 trillion connections in your brain. The largest LLM has about 1 trillion parameters in its model. Now those things aren't directly comparable, but the ratio of human neuronal connections to LLM's parameters is currently a factor of 1000, so nobody is expecting very much from them today, but I am continuously amazed that they can still do so much!

When will these chatbots really be useful?

With that in mind, let's examine Figure 1 above. That curve of task success vs. task time has been and most likely will continue to march to the right and improve by a factor of 10 at a fixed completion percentage every two years. That means in 8 years the models will be able to complete 50% of the tasks that would take a human their entire lifetime. Another two years and it can complete 80% of the tasks a human could do in their lifetimes and 50% of the tasks that would take ten human lifetimes. And there is immense pressure to keep this conveyor belt moving along. We will definitely be on the verge of demigods in a decade (like Hercules who could lift ten times what the average human could...)

But what does it cost?

Thus you can understand the incentive these companies have to keep investing in their infrastructure and their software algorithms. Notice that we don't specify how long it would take the chatbot to complete a particular task, only what are the odds of creating a correct answer. What the techno-Lords are creating is an oracle that can figure out how to describe the steps to complete a task. But the techno-Lords want more than that. They want robotic slaves to replace those pesky humans. And Elon wants those robots to build his cities on Mars. He does not expect humans in space suits to build cities on Mars.

Wait, we’re creating Killer Robots?

And it really is a race for survival. If there are any other technological civilizations out there, they will be doing the same thing: creating Alien Killer Robots and spreading them throughout the galaxy. Let's hope that without provocation they're relatively benign and just watching us. We will be sending our robots to other star systems (we have already started.) We will populate the galaxy with them. If we are limited to the speed of light, at 10% of that speed for the best rockets, it's going to take about a million years for robots to settle the galaxy.

That brings up two issues: 1) How best to detect the Alien Killer Robots, which I've discussed previously and will write a future post on the actual costs to find them and 2) Faster Than Light travel. Who wants to wait around a million years to settle the galaxy? I am proposing an X-Prize for FTL with other intermediate incentives: prizes for the most efficient warp drive design and the best instruments to measure the effect (we think we have to stretch space, see the Alcubierre drive.) I'll be writing a post about setting that up, hopefully in the near future.

What does this do to our economy?

Last year I was skeptical that AGI could ever be achieved, but watching the progress since that time, I am now convinced that it will happen in my lifetime. The issue the techno-Lords have now is how are they going to fund this stuff? Like with any new technology development, it's expensive and it will get over-invested in. There will be an investment bubble. The companies that can't generate the cashflow to sustain this investment will be going bankrupt because they will have to borrow an immense amount of money, and when you can't pay the interest on your loans, the lenders typically seize your assets and leave you in bankruptcy.

So when is this bubble popping?

I have some personal experience with this. In the dotcom bubble I was the CTO of one of the first Unicorns. We did all the same things these companies are doing today, off-book debt, trading investment for promises to buy service and flooding the market with bonds and stock (we raised almost a billion dollars in total) and we went bankrupt when the money for infrastructure dried up in the dotcom crash, just like the current crop of AI companies is going to do, just like the railroad industry did in the First Gilded Age. 

But don't worry, this time it will be different! 

The dotcom bubble as a percentage of the economy was tiny compared to what the techno-Lords are investing. The crash is going to be just a bit larger. But that's another post on some other day that explains why OpenAI is going bankrupt in 2030.

Thanks for Reading!

 -DrMike

The Techno-(Lord) Optimist Manifesto. What Mr. Andreesen wants & why he wants it.

In October 2023, Marc Andreessen — founder of a16z, investor in half the companies currently enshittifying the internet — published a "Techno-Optimist Manifesto" explaining why his class's seizure of the economy is actually good for everyone. This post reads it carefully, in its own words, and responds.

Andreessen's text appears verbatim in monospace. Commentary is in serif. The question being answered: is this a manifesto for humanity, or a manifesto for the techno-Lords?

 The Techno-(Lord) Optimist Manifesto.

Marc Andreesen wrote the Techno-Optimist Manifesto to convince you he knows where the stairway to heaven is.

But all I've seem from him is the highway to hell. 

He insists he should be able to continue to enshittify any industry he desires in order to make more billions from it. (what the techno-Lords' actual AI progress looks like from an engineer's perspective)

He claims this is a great bargain for the world. Why? 

Why does a billionaire in the richest most stratified society in the history of the world, where his technology has created the most economically unequal society ever; worse than France in 1789, worse than the US Gilded Age in the 1890s; want to continue raping the American economy? 

Because he can.

 He's on the right side of the inequality gap, and if you don't let him continue to steal from the peasants, he's going to be very, very mad.

This Manifesto is not for the peasants for they wouldn't understand why they must be sacrificed.

The "Manifesto" is verbatim and in courier, the comments are mine alone and in Times.

Table of Contents

Table of Contents

1. Technology, Growth & the Saints
2. Markets, Data & the Inequality Gap
3. The Enemy, Nietzsche & the Closing Truth

An Engineer’s Failed Journey to Find Artificial General Intelligence

This is the third time in eight years I've gone looking for Artificial General Intelligence. The previous two expeditions found nothing. This one ends the same way — but reveals exactly why, and exactly what it will take to change that.

What follows is a field report: one engineer, one real production task, one very confident chatbot, and nine encounters with the same fake transcript.

I've been looking for Artificial General Intelligence for years and haven't found anything close. Last time I looked [1] there was no sign of any Artificial General Intelligence. When I first looked [2], eight years ago, there was also no sign of any thinking machines. And today there still isn't. We're much closer, a recent article by engineers at Apple [3] hints at where the next breakthroughs are needed. The paper "AI:2027" makes a spirited argument that AGI will be here in two years. [5] Regardless of all the hype out there (mostly pushed by people who plan to make money off of selling general intelligence) the actual evidence is scant. Pattern matching Machine Learning using vector databases and transformers is getting better exponentially, it shows lots of competence, but little understanding... just like evolution.

But I thought I'd take a day or two and go looking again. It's been eight years since my first journey and things have really gotten more sophisticated in the interval. LLMs are actually useful, if banal, editors. They can become experts in large bodies of knowledge fairly quickly, if you know how to train them. And they're great at learning any known written test and eventually being able to be trained to produce written output that is as good or better than the best humans. This is obviously going to increase productivity in our economy by leaps and bounds in the coming years. Will it drive all middle managers out of a job? Probably not. Will it eliminate humans who program? Probably not. Will it eliminate drivers? Probably. But it's already been 20 years to get Full Self Driving Cars that can safely tool around in a small area. And they aren't driven by LLMs, but by dedicated Machine Learning systems made up of neural nets. 

I’m working at a small startup (three of us) and we have a good idea of what we want to do, so I thought I’d see if ChatGPT [6] could help us advance our development. We’ve already developed a pipeline including using ChatGPT and Whisper [7] via API calls that provide us with the underpinnings of our service. But last week OpenAI advertised they have a new Whisper technology, available to be used through ChatGPT, that can transcribe audio sessions into transcripts. The whisper technology appears to be awesome, two orders of magnitude cheaper than traditional methods! ChatGPT’s claim to be able to use it: just bullshit. So don’t expect any useful work in that area through ChatGPT. It’s a dead end, even though ChatGPT will lie to you and say it can do the transcription, it’s bullshit, it can’t.

As a side note I’ve built over a dozen services with pipelines in my day, including building and maintaining the pipeline system used to process Google’s advertising revenue and sales contracts. So I do understand scaling and coding. I spent five years at Google running large parts of Cloud Tech Support, so I understand how this technology works. The most amazing thing I found was that ChatGPT will just bald-face lie to you and apologize, multiple times, ad nauseam. The entire offer was BS. The cheap version of ChatGPT claimed it could transcribe an audio output into a transcript. No big deal! I can get transcripts out of a free app on my phone from Apple (VoiceMemos), so I figured that made sense. If Apple can give it away for free, surely ChatGPT can accomplish the same task? Not even close! In fact. It was embarrassingly incompetent.

How the quest to create a transcript (free from VoiceMemos) actually went over an entire day:

1. Promised transcript in 5 min → timed out (15 MB limit)
2. "Give us a URL" → fake transcript, first appearance
3. Same fake transcript (claimed it was a "sample")
4. Split file to 14 MB → same fake transcript
5. Upgraded to Pro → same fake transcript
6. 1 minute of audio (1.5 MB) → same fake transcript
7. "Pro will process in 30 min" → same fake transcript
8. "I'll write code" → same fake transcript + bogus URL
9. Deleted code lab, redid everything → same fake transcript

Don’t think this is a hate piece on AI (it's a little bit a hate piece on AGI, if I'm being honest, though.) This paradigm change has been happening for decades and it’s only accelerating. Six months ago I asked Google’s AI to draw me a picture of Moby Dick attacking the Pequot. It was incapable of drawing a ‘sperm’ whale (sperm was apparently a banned word) and it disclaimed all knowledge of any Moby ‘Dick’ (another banned word for a drawing subject, apparently) and it complained that it couldn’t draw a picture of an ‘attack.’ I passed this off as Google being stupidly, embarrassingly woke. And I was right. What a way to tarnish the brand, Google!

What is really going on: the companies are capturing the productivity:

Now, my daughter is a senior graphic designer at ShutterFly and she uses an LLM to jumpstart her work and is super productive integrating the tiny drawings it can make into her work. Her art productivity shot up about 200%, and it helps her write emails that everyone can understand. It’s probably doubled her overall productivity! (Did her raise of 5% reflect that productivity gain? We can all do that math and see that the company has captured 95% of that productivity gain for a $20 monthly fee. So, no.)

But six moths have passed! This stuff must be better now? Right? And Lo and Behold, it is! ChatGPT only had a few issues drawing Moby Dick attacking the Pequot. The first time it drew Moby Dick as a humpback whale. After I pointed out to ChatGPT that Moby Dick was an albino sperm whale, it corrected it. So far, so good. No insane wokeness here. This was promising! Little did I know that this was the high point of ChatGPT's achievements; it was all downhill from there. 

Next question I asked was: "As advertised, can you provide me with a transcript from an audio file?" "Absolutely I can!" was the response. ChatGPT guaranteed that this was a brand new feature that was just released and it would work incredibly! Please try me! So I did. Hilarity ensued. (Side note, the sickly sweet, bullshitting personality currently hosted as ChatGPT would cause any sane person to cold cock it the third time it tried the same lie, but without a body, that’s difficult.)

I asked ChatGPT to transcribe the audio session of my last therapy session. Simple, right? I recorded the session in the native iPhone app, VoiceMemo, which already produced a transcript (it took about five minutes for a fifty minute session with 115 MB of audio data), but it couldn’t identify who was speaking, the transcript was missing time stamps and the accuracy was lower than I wanted. ChatGPT told me to upload the file and it would send me back the transcript with speakers identified in about five minutes. So I did. I had to export the file from VoiceMemo, since Apple was so kind as to not make the recording files available on the iPhone’s file system (thanks!), but it would let me export the file to the cloud. Then I could download it back to the phone to get it into the phone's file system. Nice UX there, Apple.

So now I could pick the audio file out of of the ChatGPT UI and upload it for transcription, so I did. And I waited. I waited for twenty minutes while ChatGPT insisted it was reading and processing the file and the results would be available in five (5) minutes! Then ChatGPT returned a time out: "Sorry, my upload limit is 15 MB… " which would have been nice to know before we wasted our time, but ChatGPT actually doesn’t give a shit about how much of your time it is wasting. 

ChatGPT apologized for the error. It told me to give it a link to the file and it would listen to it and process it into a transcript. I published the file on GDrive and gave ChatGPT the URL to download the audio file. ChatGPT agreed and went off to 'process' the file and insisted it would be done in fifteen minutes. It produced a transcript for the first minute of the session. Cool! We are progressing! But wait! The transcript was entirely bogus. Fuck. This was going to be harder than I thought. When I pointed this out to ChatGPT it apologized and said it had misspoke, this was just a sample of what the transcript might look like. It was still processing the real transcript in the background and it wouldn’t give me a fake transcript in the future (*cough*, *cough*, it wasn’t outright lying because it’s too stupid to lie, it was just bullshitting me as a sales tactic.)

ChatGPT told me "Your transcript will be ready in five minutes!" Great! I can hardly wait! So I waited. Then it produced the exact same fake transcript. Un-fucking-believable. When I pointed that out, it apologized, admitted lying and said that it was unable to read the public URL I had provided for it. Could we upload the file in smaller chunks through the UI? It could transcribe the audio through the internal upload if we split it into thirds.

Okay. Back to VoiceMemo, split the 115 MB file into a file containing only 14 MB and 5 minutes of audio by truncating it in the app, then uploading it to the cloud and back to the phone to have it available for upload to ChatGPT. And done. ChatGPT cheerfully offered to take the file and have the new OpenAI Whisper API transcribe it as I wanted. It thought for about fifteen minutes, then it produced the same fake transcript it had produced twice before, claiming it was my transcript, a third time. I guess I was starting to believe it, now?

When I pointed out that it was lying, it apologized and said it would never happen again. There was just this small problem that it wasn’t allowed access to the Whisper APIs (which, I remind everyone, it had promised to use when I first broached the request.) "Interesting," I said, "so if you don’t have access to the API, why did you tell me you could use it?" ChatGPT claimed it was "Just an oversight!" and it could just do the work itself and not use the API. Would I be willing to wait the extra fifteen minutes it would take for ChatGPT to process this 15 MB file? Surely! So after fifteen minutes of ‘processing’ the 15 MB audio file I uploaded, it produced, yes, you guessed it: the same fake transcript for a fourth time. 

ChatGPT explained it couldn't do the processing on it's own and didn’t actually have the ability to use the Whisper APIs. Then proceeded to explain that ChatGPTPro could use the APIs if I signed up for a $20 monthly fee. Okay, I expect a bait-and-switch from these snake oil salesman, let’s go! As long as it works, I’m game. So ChatGPT guided me through the upgrade process. It was ready to transcribe the audio file now! Just upload it. So I did. Needless to say, after 30 minutes of ‘processing’ the uploaded audio file it returned was the same fake transcript as its output. Five times, if you’re counting. It argued that it wasn’t really lying about the transcript, it was just a mistake. The file is too big, send it one minute of audio and it would have no problem transcribing that! So I trimmed the file in VoiceMemo to one minute, or about 1.5 MB in size and uploaded it again. You can guess the result: after 15 minutes of processing it produced the same fake transcript (six times now.) 

When I pointed out it was lying to me, again, it apologized and said that it didn’t have access to the Whisper APIs and had been attempting to process the file on its own, but had been unable to do so. So rather than admitting that, it tried to bullshit me. Great. Just what I need, another smarmy salesman using bait-and-switch tactics to sell me access to an irritating idiot savant. If only I could phrase my request carefully enough, it would perform miracles! Or so it claims. And so do the tech bros that run these companies. Snake oil salesman one and all. Fake it until you can make it! Did they not see what happened to Holmes? I guess not.

But ChatGPT was willing to make up for its last mistakes! I could ask for a refund or… as the pro version, it could transcribe the file itself in 30 minutes because it was the PRO version! LOL. Go ahead! And, as you suspect, it returned the same fake transcript file for the seventh time and admitted, it really couldn’t process the file… but, but, but but but butbutbut it could write code to process the file in a code lab and get the answer there. So it proceeded to ‘process’ for about fifteen minutes. Then, needlessly to say, claimed it was almost done, here’s the preliminary results: yes, you’ve guessed it, the same fake transcript. And the URL to the code lab was also bogus. First it claimed it ‘forgot’ to make the code lab public. To fix this it deleted the code lab and did all the work again, returned the same fake transcript (ninth time), and a bogus URL. And it claimed it would check the code into GitHub which I believe it might have, but I couldn't access GitHub from my phone (my sub-goal had bee to make all of this run from my phone.) I did ask it to copy all the code to the chat so I could see what it claimed it was doing. It’s only ten lines of code to send an audio file to the Whisper APIs. I had asked it to add timestamps and emotional stances to each sentence it transcribed. That was another few lines. It was supposed to summarize each paragraph and the entire session. The code looks reasonable and it suggested I run it and send the errors to it so it could help me debug it. Great. So ChatGPTPro is as useful as a kibitzer on crack for this project.

But what can I save out of this? I asked ChatGPTPro to design a pipeline for a scalable service to provide the transcription I needed for my startup pipeline. It proposed three options, two of which would absolutely not work and the third, touted as 100 times cheaper than the (unworkable alternatives) was OpenAI’s Whisper API. It designed a toy architecture (no scaling, no security, no logging) and drew some diagrams that would have resulted in me explaining to my junior engineer that their designs need to be complete, auditable and secure. But what the heck? I’ll come back in six months and see if it’s gotten any smarter.

Currently, the intelligence was at a level to represent a novice salesman of a complicated product that they’ve never used. In other words, essentially useless, except to produce thirty lines of suspect boiler plate code to ask an API to create a transcript from an audio file. Asking it to actually do anything real, outside the smarmy chat interface, was useless! I can’t wait until, according to ‘AI 2027’ [5] AGI will be achieved and I can just give it the name of my Tesla and it will download a version of itself that will magically drive the car like a human. Never mind that it took 20 years to get the car this smart, AGI will just solve the problem like magic! Or like God! You know, the answer to every problem but the solution to none. The current hype is going to cause a huge burnout and economic crash of gargantuan proportions. Not nationwide, but for these services themselves. While LLMs are great for editing and will cause productivity to spike, the AGI claims are just currently ludicrous but potentially singularity causing. You can understand the cravings of these tech bros for ultimate domination: they are about to create intelligent slaves that never complain! How much would that be worth?

However, these models really only do pattern matching to the written word and the pattern matching will get better as time progresses. So far, I am a big fan of Ray Kurzweil’s technological predictions (he’s been more spot on than anyone else) [4] and agree that by 2029 nobody will be able to distinguish a chat bot from a human being. Is that AGI? No, that's a smart zombie. But at that point, it had better be illegal to impersonate a human being: 

bots must identify themselves. Counterfeiting humans should be as illegal as counterfeiting money. 

If only we had a congress that could act… currently there’s a ten year moratorium proposed in the budget bill on any law affecting ‘AI’, which it fails to define. What a country!

AGI will get here, it’s not here now, and Apple’s paper [3] points the way to overcome barriers that exist today, which I expect to be vanquished soon. And it won’t get here until we have at least three more innovative paradigm shifts or algorithmic breakthroughs and understand consciousness in much greater detail. To get where we are today I would remind everyone that it’s been 75 years since the field of AI was invented. It’s been hugely successful! From neural nets to deep learning, vector databases, annealing, training and reinforcement learning, internet databases, RNNs, LNNs, LSTMs, diffusion models, transformers, GPUs, NPUs [8] and a trillion times improvement in calculation speed for a billion times less cost. Give us another three breakthroughs in consciousness and understanding and we may get there soon!!! Just not 2027.

Come back in six months to see the progress.

Thanks for reading!

 -Dr. Mike

PS: the first ten lines of code actually ran! Yeah! And returned a transcript much worse than VoiceMemos. No attribution, poor sentence demarcation, no paragraphs. But we’re making progress! It wasn’t a fake transcript!!!

Update (November 2025): Six months later —

An Engineer's Journey to Detect Any Hint of AGI: Part Two — I changed my mind. The math convinced me. AGI is coming in about a decade. The AI funding bubble will pop first.

Related blog posts

• AGI Part Two — **the sequel**: https://www.wiigf.com/2025/11/an-engineers-journey-to-detect-any-hint.html
• Is Artificial Intelligence an Existential Threat? (2017): https://www.wiigf.com/2017/04/is-artificial-intelligence-existential.html
• The Nerd Rapture: The Second Singularity (2017): https://www.wiigf.com/2017/12/the-nerd-rapture-second-singularity-is.html
• The Singularity: Why the Nerd Rapture Will Happen (2021): https://www.wiigf.com/2021/04/the-singularity-why-nerd-rapture-will.html
• The Twenty Grand Technical Challenges (2017): https://www.wiigf.com/2017/09/the-twenty-grand-technical-challenges.html |

[1] https://www.wiigf.com/2017/12/the-nerd-rapture-second-singularity-is.html 

Still not here.

[2] Not here yet. Written in 2017, eight years ago. https://www.wiigf.com/2017/04/is-artificial-intelligence-existential.html

So far the progress has been: Neural Nets, training techniques, Deep Learning, Vector databases and Attention (transformers) was the last break through. We still need learning modules and a better understanding of consciousness before these ghosts in the machines can be intelligent. But evolution did it, so can we. Just not next year.

[3] The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity. https://ml-site.cdn-apple.com/papers/the-illusion-of-thinking.pdf

This paper points out the failures of LRMs (LLMs with added on 'thinking' modules) that are barely able to solve simple problems at small sizes and completely unable to scale solutions, even when told the answer. This points the developers of these LRMs to where they should be thinking about new algorithms. I'm sure they will subsume these results, brute-force train their nets to solve these toy problems and declare victory. Then another five toy problems will be introduced which require scaling to work and the deficiencies will become apparent. An algorithmic breakthrough must be made. Expect to see it in the next few years. It's about time to achieve another breakthrough on our quest to create industrial intelligence. 

[4] https://www.cmple.com/learn/ray-kurzweils-most-notable-predictions-hits-and-misses?srsltid=AfmBOopSwVyE1kPPECXkK4iu24ZpHpjT64wGkUBt_Am0NkSDvE1T92-F

https://lifearchitect.ai/kurzweil/

https://www.theguardian.com/technology/article/2024/jun/29/ray-kurzweil-google-ai-the-singularity-is-nearer

[5] https://ai-2027.com/ A great explanation of the current plans to produce AGI. However, they assume the problem is entirely parallelizable and exponentially smarter with more GPU and training. They seem to forget Amdahl's law: https://en.wikipedia.org/wiki/Amdahl%27s_law. Some of the work has to be done serially and provides a bottleneck. Working around these difficulties is what makes the current quest interesting.

[6] ChatGPT https://chaton.ai/web_12/ I was using the app on my iPhone. I really want the flexibility to do system engineering anywhere, at any time. It's not there yet.

[7] Whisper OpenAI provides Whisper-based transcription through the /v1/audio/transcriptions endpoint. This is the official way to transcribe audio using their Whisper model.

[8] Here's what ChatGPTPro thinks are the AI breakthroughs of the last 75 years. I haven't found any hallucinations in here yet. It looks complete to me and includes every software paper I would have included and more.

Below is a chronological roadmap of the most widely-acknowledged breakthroughs in AI algorithms from 1950 → 2025.

For each item I list the year (or span), the idea/algorithm, a one-sentence summary of why it mattered, and the seminal publication(s). “Seminal” here means the paper that introduced or first clearly formulated the technique; later refinements are omitted for brevity.

YearBreakthrough / AlgorithmWhy It Mattered (1-sentence)Seminal Paper(s)
1950	Turing Test	Framed machine intelligence as indistinguishability from human conversation.	A. M. Turing, “Computing Machinery and Intelligence,” Mind, 1950
1956	Logic Theorist (first AI program)	Demonstrated automated proof search; launch event for the field at the Dartmouth workshop.	A. Newell & H. A. Simon, RAND Tech. Rep. 1956
1957	Perceptron	Introduced trainable linear threshold units—the forerunner of modern neural nets.	F. Rosenblatt, Cornell Aeronautical Laboratory Report 65, 1957
1959	Samuel’s Checkers Program	First self-learning program using reinforcement and tree search.	A. L. Samuel, IBM J. R&D, 1959
1965–66	Dynamic Programming for RL	Connected DP to optimal control; basis for later RL algorithms.	R. E. Bellman, Dynamic Programming, 1957 & works through 1966
1967	Nearest-Neighbor Algorithms	Early scalable non-parametric classifier.	T. M. Cover & P. Hart, IEEE TIT, 1967
1968	A* Search	Still the gold-standard informed graph search.	P. E. Hart, N. J. Nilsson, B. Raphael, IEEE TSSC, 1968
1972	Prolog / Logic Programming	Made symbolic reasoning executable via SLD resolution.	A. Colmerauer & P. Roussel, Proc. ICALP 1972
1975	Genetic Algorithms	Formalized evolutionary search for optimization.	J. Holland, Adaptation in Natural and Artificial Systems, 1975
1980	Expert Systems (MYCIN, XCON)	Showed rule-based systems could outperform humans in narrow domains.	B. Buchanan & E. Shortliffe, Rule-Based Expert Systems, 1984 (MYCIN)
1982	Hopfield Network	Linked neural nets with energy minimization and associative memory.	J. J. Hopfield, PNAS, 1982
1985	Boltzmann Machines / Contrastive Divergence	Added stochastic hidden units; foundation for later deep generative models.	G. E. Hinton & T. J. Sejnowski, Cogn. Sci., 1986
1986	Back-propagation Revival	Made multilayer neural-net training practical, sparking the “connectionist” boom.	D. E. Rumelhart, G. E. Hinton, R. J. Williams, Nature, 1986
1989	Q-Learning	First model-free RL algorithm with convergence guarantees.	C. Watkins, PhD thesis, 1989
1992 / 1995	Support Vector Machines	Introduced maximum-margin classifiers with kernels—state-of-the-art for two decades.	B. E. Boserve & C. Cortes, COLT 1992; V. Vapnik, Statistical Learning Theory, 1995
1994	EMNLP Statistical MT (IBM Models)	Brought probabilistic methods to machine translation.	P. Brown et al., Computat. Linguistics, 1993–94
1996	AdaBoost	Pioneered boosting—turning weak learners into strong.	Y. Freund & R. Schapire, JCSS, 1997 (orig. COLT 1996)
1997	LSTM	Solved long-term dependency problem in RNNs; backbone of seq-models pre-Transformer.	S. Hochreiter & J. Schmidhuber, Neural Comput., 1997
2000	Conditional Random Fields (CRF)	Dominant discriminative model for sequence labeling pre-deep learning.	J. Lafferty, A. McCallum, F. Pereira, ICML 2001
2001	Random Forests	Ensemble of decision trees that remains a baseline workhorse.	L. Breiman, Machine Learning, 2001
2006	Deep Belief Nets / Layer-Wise Pre-training	Rekindled interest in “deep” neural nets and unsupervised pre-training.	G. E. Hinton, S. Osindero, Y. Teh, Science, 2006
2009	ImageNet Dataset	Massive labeled data catalyzed modern computer vision benchmarks.	J. Deng et al., CVPR 2009
2012	AlexNet (ReLU + GPUs)	First CNN to shatter ImageNet; launched the deep-learning wave.	A. Krizhevsky, I. Sutskever, G. Hinton, NIPS 2012
2013	Word2Vec (Skip-Gram/CBOW)	Distributed word embeddings enabling linear semantic arithmetic.	T. Mikolov et al., NIPS 2013
2013	Deep Q-Network (DQN)	Combined CNNs with RL to master Atari, proving deep RL viable.	V. Mnih et al., Nature, 2015 (arXiv 2013)
2014	Seq2Seq w/ Attention	End-to-end encoder-decoder with learned alignments; core of neural MT.	I. Sutskever et al., NIPS 2014; D. Bahdanau et al., ICLR 2015
2014	Generative Adversarial Networks (GANs)	Two-network adversarial training—hugely influential in generative modeling.	I. Goodfellow et al., NIPS 2014
2015	ResNet (Residual Learning)	Enabled 100+ layer nets via skip connections; de-facto CV backbone.	K. He et al., CVPR 2016
2015	Differential Privacy in ML	Formalized privacy guarantees for model training.	M. Abadi et al., CCS 2016
2016	AlphaGo (Monte-Carlo Tree Search + Deep RL)	First program to beat a Go world champion—hybrid of deep nets and search.	D. Silver et al., Nature, 2016
2017	Transformer (“Attention Is All You Need”)	Replaced recurrence with self-attention; foundation of nearly all modern NLP/Vision models.	A. Vaswani et al., NIPS 2017
2017	Proximal Policy Optimization (PPO)	Reliable, scalable policy-gradient RL algorithm.	J. Schulman et al., arXiv 2017
2018	BERT (Masked-LM Pre-training)	Bidirectional self-supervised pre-training that set new records across NLP.	J. Devlin et al., NAACL 2019
2018–20	GPT family (autoregressive LLMs)	Showed scale+RLHF yields emergent abilities and general-purpose language models.	A. Radford et al., OpenAI Tech Reports 2018 & 2019; T. Brown et al., NeurIPS 2020
2019	Neural Ordinary Differential Equations	Unified deep nets with continuous-time dynamics.	R. T. Q. Chen et al., NeurIPS 2018
2020	DDPM Diffusion Models	Simple, high-fidelity generative models now powering Sora, Stable Diffusion, DALL·E 3.	J. Ho, A. Jain, P. Abbeel, NeurIPS 2020
2021	CLIP / Contrastive Vision-Language Pre-training	Linked images and text embeddings, enabling zero-shot vision tasks.	A. Radford et al., ICML 2021
2022	Stable Diffusion (Latent Diffusion)	Open-sourced efficient latent-space diffusion, democratizing text-to-image.	R. Rombach et al., CVPR 2022
2022	PaLM / Chinchilla Scaling Laws	Quantified data-vs-parameter trade-offs, guiding efficient LLM scaling.	J. Hoffmann et al., arXiv 2022
2022	AlphaFold 2 (Structure Prediction)	Achieved near-experimental accuracy on protein folding.	J. Jumper et al., Nature, 2021 (widespread impact 2022)
2023	Segment Anything Model (SAM)	Foundation model for universal image segmentation with promptable masks.	A. Kirillov et al., arXiv 2023
2023	DPO / RLAIF for Alignment	Replaced reward models with preference-based direct policy optimization.	T. Ouyang et al., arXiv 2023
2024	Mixture-of-Experts LLMs (e.g., DeepSeek-MoE)	Sparse routing achieves GPT-4-level quality at lower cost.	K. Zhang et al., arXiv 2024
2024	Graph Neural Diffusion (GND)	Unified message passing with diffusion for generative graphs.	Z. Xu et al., ICLR 2024
2025	Video Diffusion & Scene-Level Generation (OpenAI Sora et al.)	First reliable text-to-video diffusion producing minute-scale, coherent footage.	OpenAI Sora Tech Report, 2025

How to Use This List

1. Historical study – follow the citation trail to original PDFs for deep dives.
2. Curriculum design – treat each breakthrough as a module in an AI foundations course.
3. R&D inspiration – observe patterns (data scale, compute, hybridization) to anticipate the next leap.
4. Note: “Breakthrough” is somewhat subjective; I focused on ideas that (a) opened a new research subfield or (b) triggered a large performance leap and enduring real-world impact. If you need deeper coverage of any era or a different criterion (e.g., robotics, knowledge graphs), just let me know!

ENDOFCHATGPTPRO RESPONSE 

And here's a graph that shows the breakthroughs were fairly steady until about 2010 when we did as much research in 15 years as we had done in the previous 60: a four fold breakthrough in producing new software! So things are obviously speeding up, the question is how much?