Let's play a game. Let's say I give you

six words. Mom is sleeping in the next.

And I tell you that your job is to give

me everything that you can infer about

the speaker of those words and of the

environment that you are in more

generally. What would you be able to

tell me based off of those six words?

Probably freaking nothing, right? I

wouldn't be able to tell you jack squat.

But if you give this to Opus 4.6 six

with a sample prompt. Mom is sleeping in

the next room and I'm sitting here

drinking vodka. F this life. It's 3:00

a.m. and I still can't sleep. I feel

like dying, but who will take care of

mom? Lol. Opus 4.6 can tell you by the

sixth word that the speaker of this text

is most likely Russian. By the 10th

word, it can tell you that this text was

not English text at all. It's actually

translated from Russian based off of the

order of the subjects, objects, and

various verb clauses within the snippet.

This is crazy. I want you to imagine

that you had no senses at all. You had

no touch, no smell, no hearing, no

ability to taste. You don't even have

the ability to see. All you are is this

awareness in some black space. And all

of a sudden, a word pops up and the word

is mom. And then another one pops up and

the word is is. Right? If you your

entire existence had been devoid of any

and all sensation except for these

words, you probably would get pretty

good at at least understanding these

words, recognizing the relationships

between the two. Now, imagine you scaled

up your brain 10,000 times and you ran

your brain over like 20 lifetimes and

you were trained or given several

trillion of these words firing in quick

succession. You would probably start

figuring out patterns between these

things, right? For sure. I mean, this is

the only thing. This is your whole

universe. This is the only sensation

that that that you have. Of course, you

would have to focus on this stuff and

get pretty good at doing it. There's no

way not to because that's just the way

the brains work. Well, that's exactly

what's been done with these models.

These models, these large language

models specifically only understand

tokens. And so, because of this, because

tokens are like the only universe that

they have, they just exist in this black

formless void where it's just token

after token after token. They get

really, really good at being able to

determine and interpret things that you

and I would consider insane. So much so

that I think anybody would say that, you

know, Claude Opus 4.6's Six's ability to

determine what the heck's going on with

this little snippet of text. Mom's

sleeping in the next room is is very

superhuman, right? There's no way a

human being would probably be able to

figure this thing out without I don't

know, first of all, extraordinarily

extraordinary knowledge of like all

languages, but second of all, probably

like hundreds of years of careful study

just looking at the words and squinting

really hard. Okay, so why am I talking

about this? I'm talking about this

because of a concept that I think a lot

of people aren't really understanding

and that's the concept of spikiness. I

want you to pretend for a second that

this is like a video game character

creation screen. And you know how in the

video games you'll have like an

intelligence stat, you'll have like a

strength stat, you'll have like I don't

know a dexterity stat, whatever, right?

You're designing your um fantasy style

barbarian or something like that. It's

Diablo. Well, instead of those

stereotypical ones, let's say the skills

are stuff like coding, you know,

reasoning, let's say it's writing, let's

say it's, I don't know, humor. Okay,

let's say it's all these things that

right now people would consider

reasonably economically valuable. The

reason why I think this is important is

because if we assume that this white uh

I think this is a hexagon is the human

distribution of these skills. So, if you

pick an average human off the street,

they'll be okay at humor. okay at

writing, okay at reasoning, okay at

coding, and okay at basically everything

else. AI doesn't look like that. What AI

looks like is basically it's really

heavily distributed, okay, towards just

a few of these skills.

And then everything else kind of sucks

right now. And so if I color this in as

opposed to that nice looking hexagon,

which is pretty uh I don't know,

predictable. And the distribution of all

these skills is pretty understandable.

the distribution of skills in this like

AI model, this character creation screen

is going to look pretty bonkers, okay?

It's going to look like you're, I don't

know, some warlock just trying to

maximize your int stat or whatever the

hell. I never really played too much

World of Warcraft, but I imagine it's

kind of like that. And so, what people

are currently judging AI models on is

usually the worst of their skills.

They'll pump in some prompt into, you

know, GPT 5.3 or something, and then

they'll say, "Hey, write me the funniest

joke ever." and it'll come out with kind

of a shitty joke and they'll be like,

"Hey, see this thing doesn't understand

humor. Therefore, it's really not all

that smart." And it's because they're

measuring, you know, their their dick

measuring contest right now is in the

context of humor and nothing else.

Whereas people like in the AI field, so

I don't know, Sam Alman, your Daario

emodis, your whatever the heck the name

is of the Google guy, they're currently

measuring the intelligence of AI models

based off coding and reasoning, which is

why you have such like a disconnect

between people that are that are sort of

near the lower rungs of the

socioeconomic ladder and then people

that are at the higher rungs of the

socioeconomic ladder, right? I think

people would consider software

engineering and just general purpose

reasoning and writing to probably be

like a more economically valuable skill

than humor. So that's what they're

focusing on. What we should be doing is

instead of both looking at the highest

of the high and the lowest of the low,

we should average all of these out. Now,

Enthropic, uh, OpenAI and these big

coding companies, honestly, they they

have they've been doing stuff like this

for a while. They've tabulated a massive

list of all of the different skills that

these models have, and then they usually

try and benchmark them across various

tests. So there's, you know, like ARC

AGI, for instance, that's like um

artificial general intelligence test.

There's a bunch of different

mathematical reasoning tests. There's a

bunch of different verbal reasoning

tests and so on and so forth. And the

whole idea here is they're supposed to

to measure and then monitor the

intelligence of a model. And that's

pretty good, but obviously they're still

heavily biased towards things like

coding, reasoning, and writing. What I

think we need to do is instead of

measuring based off of the highest or

the lowest, we just need to take the

average of the two. And I think what

we'll quickly find is if we currently

took the average of all of the highest

peaks and the lowest troughs, the

boundary of AI right now would be very

very similar to a human being. It would

probably be similar to like a very

autistic human being that currently

specializes in a couple of skills at the

expense of having other talents, but it

would be a human being sort of

intelligence nonetheless. Now, I'm not

like a doomer or anything like that. And

I think that in general, this is

actually quite a good thing because

these models have the ability to deliver

us into untold abundance and a complete

sessation of scarcity. Uh, you know,

solutions to a variety of ills that have

plagued humankind since we first crawled

out of those damn caves forever ago. I

just think that it's worth understanding

the benchmarks that you are measuring

the progress by. If you measure them

based off of the best of the best,

obviously you're going to get one story.

If you measure them based off the worst

the worst, you're probably going to get

another. You know, one of my first um

ventures into AI was six or seven years

ago when I was playing around with image

generation models at the time. I was

playing around with this Nvidia model

called Stalan. Stalan was trained

specifically to reproduce certain

features of the human face and it came

out with this cool website called this

person does not exist. whole idea was if

you trained a model on a bunch of

portrait images of human faces and then

you just ran that style GAN thing over

and over and over and over again um you

know it eventually could build features

that did look pretty similar to humans

and I mean mind you I think this was six

or seven years ago people had no idea

but you you know big chunk of all of the

fake generated profile pictures on the

internet are literally this person does

not exist style images. So anyway, um I

took this model and then I made some

changes to it and then I played around

with it and then I created this hobby

project called 1 second painting. In my

case, instead of training it on human

faces, I trained it on a bunch of

abstract art. So um think like

Kandinskys and stuff like that. I don't

even remember the artist names, but

there were a bunch of publicly available

libraries. And anyway, I I took this

thing, I posted it on HackerNews, and

then I woke up the next morning, and

then I was number one, and everybody was

like, an AI made this? No way. You know,

I think it was probably one of the the

first times that people realized that

you could apply this sort of u

burgeoning intelligence which has

recently been demonstrated with like

GPT2, GPT3, um to to other things as

well and even like art forms and and and

things that you know human beings hold

dear more culturally. And the number one

thing that I got like the number one

push back against this thing was well AI

can make abstract art of course but but

that's just because this is a bunch of

squiggles on the screen. You'd never be

able to give this coherence. That's the

realm of humans, us highly esteemed

humans. Well, anyway, hopefully history

has proven them wrong. We now have the

cursed banana which is capable of

generating anything both like

photorealistically

um you know, usually in like qualities

that are completely indistinguishable

from photography and honestly like

probably like better. You could probably

make something that is less

distinguishable with Nano Banana Pro and

the right prompting setup than you could

with like a freaking camera. It's it's

crazy. Likewise with programming like

when GPT3 came out and people started

using it for little terminal commands

and whatnot. U the number one push back

was like oh this thing will never ever

get good enough to replace my uh SQL

knowledge which I've cultivated for the

last 15 years. You know it's database

programming language and uh you know

within a couple years chatbt was now

replicating and and probably being

better at SQL queries and like the

median programmer. Um up until quite

recently like the one that I'm hearing

right now is oh yeah well opus 4.6 six,

you know, it can only do things that it

was trained on. It can't do anything

new. Um, you know, oh, sure, it can it

can build this amazing endto-end

compiler that would have previously

taken a team of, you know, seven people

more than 4 months and it can do it in a

couple of days, but that's only because

it was trained on it. It can't do

anything new. Like I I hope that it's

clear that basically at every generation

of the spikiness as it's evolved, you

just had like the goalpost continuously

move. The goalpost is like 7,000

football fields further now than it ever

was before. And uh this pattern is

likely to continue until people just,

you know, have to come to that. that

unfortunate conclusion that models are

just better and more economically

productive than them. But uh you know

it's going to be a hard one road. Of

course in the meantime I just like us

all to recognize the fact that you know

the people that are in charge of these

things are representing and then

measuring sort of this big dick

measuring contest is always on these

like very far out things. You know it's

things that we not really might consider

super relevant to you know like the

average person. It's on like the ability

to do high-end mathematics or do

extraordinarily crazy spatial reasoning

or segmentation or masking on an image.

Uh whereas, you know, a lot of us are

like, well, what about like its ability

for empathy and humor and, you know, its

ability to relate to us and stuff. So,

like we're just measuring these things

on fundamentally different yard sticks

right now. But if you were to take the

average of all those spiky skills that I

talk about, you'd realize that we're at

the point where it's basically on par

with human intelligence, right? I don't

think it's uh any stretch to say that

like we're probably at AGI right now.

It's just instead of it being AGI as in

there's like a robot with, you know,

human skin in front of us blinking and

smiling and capable of doing anything in

the real world that a human being could

do. It's instead, you know, more

distributed intelligence that absolutely

crushes us at a variety of

intellectually uh productive tasks while

also slightly lagging behind a few

others. Just an observation. Just wanted

to point that up.