This year, Mr. Alman said that AGI was

imminent. Now, he says that the term AGI

itself is not useful.

Every few months, someone in big tech

declares that we are on the edge of

artificial general intelligence. And

every time that line gets blurred, a few

more billions get raised. I have never

talked about AGI on my channel, but

lately it keeps showing up in the

comments. So, I thought, wouldn't it be

symbolic to share my thoughts and

research on the theme of AGI? Especially

now that we're approaching the end of

the year and I'm wrapping up my series

on the business economics of AI. I will

now be dissecting the technical

feasibility of AGI and when or whether

it'll see the light of day. This is

completely outside of my line of work. I

am not an AI researcher. I would like to

dig into the business of pretending that

we're close to AGI and the economics of

this narrative. Let's dive in.

It is considered that there are three

dimensions so to say in the realm of

artificial intelligence AI,

AGI and ASI.

AI stands for artificial narrow

intelligence. The purpose of the A and I

is to perform a single specific task

extremely well like the way Claude is

generating text or Siri recognizing

voice to a very certain extent or your

phone recognizing your face. This is the

only type of AI that currently exists in

2025. AGI stands for artificial general

intelligence.

AGI is a theoretical term. The operative

word here being theoretical. AGI would

supposedly match human level

intelligence in the ability to learn,

reason or transfer knowledge and ideally

solve novel problems without having to

be retrained. The closest example of Agi

that you might have seen is a human

resembling robot in the movies. And ASI

which stands for artificial super

intelligence is a purely hypothetical

concept of AI that would surpass human

intelligence across all domains. ASI

would hypothetically be able to solve

problems and learn far faster than any

human alive and have the ability to

improve itself autonomously which is

essentially technological singularity if

I may. Let's come back to the topic of

this conversation which is AGI. The

problem of AGI is that very few people

can define what it actually means. Even

prominent AI researchers and scientists

cannot land on a single definition of

this thing. But ironically, there are

quite a few people in the general

public, so to say, who would gladly tell

you the definition of AGI, thinking that

they're well aware of what it is and

what it's supposed to do. Now, if you

search for the definition of AGI, the

word that you're going to come across is

going to be consciousness. Something

like AI with consciousness or conscious

AI. So, some kind of magical AI entity

with cognition. But the question that

isn't really being answered is what is

consciousness?

The paradox is that the people who do

PhD level research when talking about

AGI do so with extreme caution without

any specific timelines and they're doing

so exactly because the definition of AGI

is extremely vague and hardly

quantifiable. But those who do speak

freely and confidently about a GI

usually cannot properly explain what it

means and what consciousness means in

the context of artificial intelligence.

Nevertheless, the noise that they create

is loud enough to induce anxiety into

the part of the population who mistakes

confidence for credibility.

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the video. Here's a chart of predictions

from the most prominent names in AI

research who are way less optimistic or

certain about the timeline for the AGI

and the predictions he offer are very

different from the media narrative that

makes you believe that AGI is imminent.

the podcast episode that I referenced in

the previous videos in which Dwaresh

Patel interviewed Andre Karpathy and

Cararpathy said that we are at least 10

years away from AGI and he clearly

states that even that prediction is

based on pure extrapolation and

speculation really. I covered

extrapolation bias at length in one of

the previous episodes and in my opinion

the extrapolation bias is one of the

biggest contributors to the current

bubble that is present even among

professional investors. Sam Alman's now

famous statement that he made when

giving a talk at Stanford summarizes the

delusion of chasing AGI whether it makes

sense or not. He said that he doesn't

care if they burn $500 million or $5

billion or $50 billion a year. They're

making AGI and it's totally worth it. So

the way AGI is being described today is

LLM's

multimodality,

some kind of magic

equals AGI. So we're definitely making

AGI. We're burning billions and it's

totally worth it. Except we don't know

what it is and what it really takes.

This framing makes an idea that would

typically be labeled as unsustainable

delusion into a vision.

OpenAI projects burning 115 billion

through 2029 despite posting a 13.5

billion loss against a 4.3 billion in

revenue during 2025 first half. And the

AGI narrative justifies this.

This wouldn't fly for anybody else. But

when Alman says it, it's fine. And in my

view, there is another layer to this.

When we say that we must control AI for

safety,

this argument becomes justification for

market concentration.

Because how do you apply anti- monopoly

rules when critics argue that safety is

used for monopoly and proponents argue

that concentration is necessary for

safety and the argument follows a

structure where AGI is inevitable.

Therefore, whoever gets there first will

capture extraordinary value. Therefore,

current losses don't matter. Now map

this to the problem that I will once

again remind you of. We still cannot

define what AGI is. I found the

following snippet in a fortunes article.

Among the biggest factors in AGI's

sudden fall from grace seems to have

been the roll out of OpenAI's GPT5 model

in early August that landed with a thud.

Yes, the release of GBT5 had mixed

reviews to say the least and I actually

was among those who did not like it at

all at first. And then there is research

from Yale Law Policy Review on how this

anti- monopoly approach to AI governance

shows concentrated control across the AI

stack. The semiconductor market is

dominated by Nvidia with 92% market

share. cloud computing, AWS, Azure,

Google Cloud controlling 63% globally.

What the AGI safety narrative is doing

is that it helps to legitimize this

concentration by framing it as

protective

rather than extractive. This purely

hypothetical conversation about the

imminence of AGI creates superficial

urgency and the urgency benefits

fundraising. And that is why it is being

lobbied so much. For example, Ilia

Sutskver's safe super intelligence

raised $2 billion at a 12 billion

valuation without a working product or

revenue. And Thinking Machines Labs

secured billions purely on AGI promises.

I want to make a quick pause here

because I've been noticing lately that

people in the comments choose to hear

what they would like to hear and I want

to clarify the lens through which I

approach all of my content and all of my

research. I analyze every single topic

from product and business perspective.

My interest is purely academic and I'm

studying how narratives translate into

business. The point I'm trying to make

is that this would never fly in normal

circumstances. This is not about safe

super intelligence. Whether I like it or

not, I have immense respect for Mr.

Saskca and his work. What I'm

highlighting here is that we're talking

about billiondoll investments

justified by an idea that lacks a basic

definition. Another popular fear is how

AGI will affect the labor market. In

fact, I got inspired for this episode

after seeing the comments under my own

videos where people were saying, "You're

so cynical now, but wait till AGI comes

for your job." So, I was like, "You know

what? Maybe I'm the one delusional here.

Let me do the research." So, I did some

digging, found a bunch of AGI related

future of tech, future of job reports,

and after analyzing them for a week, I

stand by what I said. Let me show you.

Goldman Sachs and IMF 300 million jobs

globally could be affected which is 9.1%

of global workforce. My next logical

question is is 300 million a lot

comparing to major historical crisis

from the past. Yes, it is a lot. For

example, great depression, jobs lost 15

million and that was only in the US. The

unemployment rate was 25% at peak. The

duration was 4 years to peak and then 10

plus years afterwards. It really

required World War II mobilization to

recover from the Great Depression. 2008

financial crisis. Jobs lost 27 million

globally. The peak unemployment was 15

million in the US. The recovery took 5

to seven years with wage stagnation. And

lastly, COVID 33 million increase in

global unemployment in 1 year.

Unemployment peak 13%. 6.5 globally. The

recovery pattern was different. It was

K-shaped. High earners gain jobs but low

earners lost. If we step away from the

crisis analogy and consider normal

annual churn in the US, for example,

we're looking at 50 million job

separations every single year in the US

alone. But these are immediately

replaced by the new hires. But the

prediction around AGI and its impact on

employment is net loss, not replacement.

And the more I was reading, the more I

kept questioning what is the likelihood

of this prediction fulfilling itself

without AGI because the entire research

is based on the assumption that AGI

exists. But it doesn't. It doesn't

exist. The report analyzes how

generative AI could impact 300 million

jobs. But the displacement it describes

requires AGI level functionality

performing general cognitive work across

entire occupations replacing fully

replacing human workers at scale

operating without any sort of

supervision. That is not A&I. Their

research objective and the methodology

is solid. They analyzed 800 plus

occupations for automation potential.

But the predictions are based on the

technological assumptions that AGI

exists, which again it doesn't. I'm not

implying that this research is not

substantive or that it isn't worth your

attention. All I'm trying to say is that

it assumes that AGI exists. And remember

how I told you in the last video that a

lot of people hear almost works and

extrapolate that to works. And as a

result of this extrapolation, there is a

lot of general anxiety about something

that doesn't exist. Compared to the

whole AGI narrative, the AI bubble with

its inflated expectations and deceptive

marketing pales in utter insignificance

because at least the technology exists.

You can touch it, you can smell it, you

can use it, you can see it.

People dramatically overestimate current

AI job displacement. The claims about AI

job displacements are highly

questionable, and I made a series of

videos dissecting layoff data. But the

problem is that the AGI narrative keeps

fueling that anxiety. There is a recent

analysis from Brookings and Yale Budget

Lab, great piece of work, by the way. I

highly recommend that you guys read it.

That found that there was no detectable

labor market disruption from GPT's

release, which took place 33 months ago.

There was no disruption on the

economywide level. So, what this

essentially implies is that there is no

documented labor market disruption from

AI,

but we're already worried about AGI. And

this anxiety stems from highly visible

tech layoffs that dominate the headlines

and the fact that there is a lot of

blame being put on AI without noting

that traditional economics like

inflation, interest rates or

restructuring drive business decisions a

lot more than any AI restructuring. And

the last angle that I want to dissect

here is the financial angle because as I

was doing the research I made an

observation that the investment dynamics

around AGI is a bit different from the

ones associated with AI SAS and inflated

VC capital. OpenAI can burn $ 8.5

billion annually and raise 40 billion

more.

Every AI SAS company that you see in

this list has a high multiple. As a

refresher, a multiple is valuation

divided by revenue. For Perplexity, for

example, 18 billion divided by 300

million is 60 times revenue. This means

that investors are paying $60 for every

$1 of annual sales that Perplexity is

generating. All of AI SAS companies

operate on extraordinarily high

multiples, but at least they have a

product. forget whether it brings ROI to

the client or not. At least there is a

product and hopefully a path to

profitability.

But for AGI, there is no product. The

AGI narrative isn't about getting

premium multiples. AI SAS companies get

those automatically. AGI language buys

permission.

Permission to lose money at

extraordinary scale.

The real premium here is tolerance for

loss.

Just so you can compare, open eyes

revenue $15 billion. Annual losses $8

billion, which is 53% of the revenue.

Monthly burn $78 million.

Profitability target 2029, four years

away. 2025 funding raised $40 billion.

Valuation $300 billion. And these are

the numbers for cursor and mjourney.

Both profitable, both with small teams.

Mjourney is actually bootstrapped. This

is what I mean by the business of

pretending we're close to AGI. Not

because it's imminent, but because it

pays off.

So where does this leave us? In my

humble opinion, AGI as it stands is

somewhere between magical thinking and

Terminator.

The irony is that the people closest to

the actual cutting edge research are the

most cautious about promises and

timelines, while the people furthest

from it get intimidated by the

narrative. 33 months after the release

of GBT, we still haven't seen the labor

market disruption, but a new definition

of what AGI means that changes quarterly

is tied to another round of investment.

So, until someone can at least clearly

define what AGI is supposed to mean,

maybe it's time we all just collectively

chill. I mean, holidays are coming. Why

don't we worry a little bit less about

something that doesn't exist?

We hope this was helpful. We'll see you

next time. Bye.