Andrej Karpathy: From Vibe Coding to Agentic Engineering

We're so excited for our very first

special guest. He has helped build

modern AI, then explain modern AI, and

then occasionally rename modern AI. He

actually helped co-ound open AAI right

inside of this office. Was the one who

actually got Autopilot working at Tesla

back in the day, and he has a rare gift

of making the most complex technical

shifts feel both accessible and

inevitable.

You all know him for having coined the

term vibe coding last year, but just in

the last few months, he said something

even more startling. That he's never

felt more behind as a programmer. That's

where we're starting today. Thank you,

Andre, for joining us.

>> Yeah. Hello. Excited to be here and to

kick us off.

>> Okay. So, just a couple months ago, you

said that you've never felt more behind

as a programmer. That's startling to

hear from you of all people. Um, can you

help us unpack that? Was that feeling

exhilarating or unsettling?

>> Uh yeah, a mixture of both for sure. Uh

well, first of all, um

I guess like as many of you, I've been

using agentic tools like lot code,

adjacent things, uh for a while, maybe

over the last year as it came out and it

was very good at you know chunks of code

and sometimes it would mess up and you

have to edit them and it was kind of

helpful and then I would say December

was this uh clear point where for me I

was on a break so I had a bit more time.

I think many other people were similar

and uh I just started to notice that

with the latest models uh the chunks

just came out fine and then I kept

asking for more and it just came out

fine and then I can't remember the last

time I corrected it and then I was I

just you know trusted the system more

and more and then I was vibe coding

[laughter]

and uh so it was kind of a I do think

that it was a very stark transition. I

think that a lot of people actually I

tried to I tried to stress this on uh

Twitter and or X because I think a lot

of people experienced AI last year as

ChachiPT adjacent thing. Uh but you

really had to look again and you had to

look as of December uh because things

have changed fundamentally and uh

especially on this like agentic coherent

workflow uh that really started to

actually work. Um, and so I would say

that um, yeah, it was just that

realization that really uh, uh, had me

um, go down their whole rabbit hole of

just, you know, infinity side projects.

Uh, my side projects folder is like

extremely full with lots of random

things and, uh, just, uh, V coding all

the time. Uh, so, uh, yeah, that kind of

happened in December, I would say, and I

was looking at the repercussions of that

since.

>> Um, you've talked a lot about this idea

of LLMs as a new computer. um that it

isn't just better software, it's a whole

new computing paradigm. And um software

1.0 was explicit rules, software 2.0 was

learned weights, software 3.0 is this.

Um if that's actually true, what does a

team build differently the day they

actually believe this,

>> right? So uh yeah, exactly. So software

1.0, I'm writing code, software 2.0, I'm

actually programming by creating data

sets and training uh training neural

networks. So the programming is kind of

like arranging data sets and maybe some

objectives and neural network

architectures. And then what happened is

that basically if you train one of these

GPT models or LLMs on a sufficiently

large set of tasks implicit basically um

implicitly because by training on the

internet you have to multitask all the

things that are in the data set. Uh

these actually become kind of like a

programmable computer in a certain

sense. So software 3.0 know is kind of

about uh you know your programming now

turns to prompting and what's in the

context window is your lever over the

interpreter that is the LLM that is kind

of like interpreting your context and uh

performing computation in the dig

digital information space. So I guess um

yeah that's kind of the transition and I

think there's a few examples of that

really drove it home for me and maybe

that might be instructive. Uh so for

example when you when openclaw came out

when you want to install openclaw you

would expect that normally this is a

bash bash script like a shell script. So

run the shell script to run to install

open claw. Um but the thing is that in

order to target lots of different

platforms and lots of different types of

computers you might run an open claw.

This these shell scripts usually balloon

up and become extremely complex. But the

thing is you're still stuck in a

software 1.0 universe of wanting to

write the code. And actually the open

claw installation is a is a copy paste

of a b bunch of text that you're

supposed to give to your agent. Uh so

basically it's it's a little skill of uh

you know copy paste this and give it to

your agent and it will install open

claw. And the reason this is a lot more

powerful is you're working now in the

software 3.0 paradigm where you don't

have to precisely spell out you know all

the individual details of that setup.

The agent has its own intelligence that

it packages up and then it kind of like

follows the instructions and it looks at

your environment, your computer and it

kind of like performs intelligent

actions to make things work and it

debugs things in the loop and it's just

like so much more powerful, right? So I

think that's a very different kind of

like way of thinking about it is just

like what is the piece of text to copy

paste to your agent? That's the

programming paradigm. Now I think one

more maybe uh example that comes to mind

that is even more extreme than that is

when I was building um menu genen. So,

menu genen is this idea where you um you

come to a restaurant, they give you a

menu. There's no pictures usually. So, I

don't know what any of these things are

uh usually like 30% of the things I have

no idea what they are, 50%. So, I wanted

to take a photo of the restaurant menu

and to get pictures of what those things

might look like in a generic sense. And

so I built I've vcoded this app that

basically lets you upload a photo and it

does all this stuff and it runs on

Verscell and uh it basically rerenders

the menu and it gives you like all the

items and it gives you a picture that it

uses an image um you know generator uh

for to basically OCR all the different

titles uh use the image generator to get

pictures of them and then shows it to

you. And then I saw the software 3.0

version of this which is which blew my

mind which is literally just take your

photo give it to Gemini and say use

Nanobanana to overlay the the things

onto the menu. Uh and Nanabanana

basically returned an image that is

exactly the picture of the menu that I

took but it actually put into the pixels

it rendered the different things in the

menu and this blew my mind because

actually all of my menu gen is spirious.

It's working in the old paradigm that

app shouldn't exist. uh and uh yeah the

software 3.0 paradigm is a lot more kind

of raw. It just um your neural network

is doing more and more of the work and

your prompt or context is just the image

and the output is an image and there's

no need to have any of the app in

between. Um so I think that people have

to kind of like reframe you know not to

work in existing paradigm of what things

existed and just think about it as a

speed up of what exists. It's actually

like new things are available now. And

going back to your programming question,

it's not even I think that's also an

example of working in the in the old

mindset because it's not just about

programming and programming becoming