RIP Vibe Coding. Feb 2025-Oct 2025.

Welcome back to the AI daily brief. This

week, as I am out traveling for my

anniversary, we are going to have a

combination of regular shows as well as

some different formats that we don't

normally get to do. And one of those is

an interview with the man, the myth, the

legend, Shawn Wang, better known as

Swix. Now, you might have heard me talk

about Swix on here, or maybe you've

heard his podcast, Late in Space, or his

events, the AI engineer Summit and AI

Engineer Worlds Fair. And even though

many of us who are creators or listeners

of this show aren't technical or aren't

developers ourselves outside of vibe

coding, I think it's a really valuable

thing to spend our time understanding

what developers are talking about. As I

discuss with Shawn in this show, it's a

little bit like previewing the future.

And so what we do in this conversation

is look at the big themes that he is

thinking about and the big conversations

shaping that sector of the industry and

also how he's turning those into key

themes for the AI engineer code summit

which is coming up in New York. Now, for

those of you who will be at the AI

Engineer Code Summit, I will be speaking

there and I'm very excited. But without

any further ado, let's get into this

conversation and bring Swix once again

back to the AI Daily Brief. All right,

Sean/Swix, better known as Swix, how are

you doing, man? Welcome back to the

show.

>> I'm doing great. Thank you for having me

again.

>> Yeah, it's uh always great to check in

with you. Um, as I was just saying, I

think the reason that I'm always

pointing people to to you and the set of

content that you're around is I think

especially for folks who are outside of

the kind of AI engineering conversation,

understanding what the builders are

talking about is kind of like living in

the future a little bit. So, what I

wanted to do today is is dig into maybe

some of those conversations that are

driving the AI engineering community.

And the specific context that I think is

interesting is you have a big event

coming up in just about a month, a

little less than a month now where

obviously you have to think about and

crystallize those things into content.

So maybe let's kick off by just if you

want to tell us a little bit about the

code summit and how you think about this

event relative to the others that you

do.

>> Yeah. And I should also flag that you're

speaking uh which I'm very excited

about.

>> Yes, I know. I I can't wait to be back

with you. Uh so I've been organizing AI

engineer summits for three years and

usually they are kind of generalist.

They focus on just whoever the best

speakers I can get and the general state

of AI. And I think that now the meta is

kind of shifting towards focus and

concentration on a certain topic because

when we have as many sort of applicants

as we have cuz it's like a you know you

have to apply to get into this

conference we we get to pick and like

the best vibes are when everyone you run

into is all all concentrated on the same

theme like you gather for a certain

topic and even like changing the name

and like um focusing on certain theme

changes the entire vibe of the whole

thing. Uh which is very cool, very very

very fun. This is something I I realized

as a meetup organizer. So so we're we're

doing uh this is our first ever summit

entirely focused on AI coding and we're

doing enterprise and individual

contributor days as well. Uh but I think

like the focus is on like why coding has

emerged as something that has particular

product market fit uh and especially

emerged this year. And it seems weird

for me to say this as someone who's had

a whole career in developer tools and

kind of always focused on AI coding, but

we've never done this before. Uh but I

think this is the year like you know

most people don't even remember that

cloud code only emerged in March uh this

year and is now larger than you know

$600 million business. Uh and it it was

like after our last summit in New York

when you were MCE. So like a lot has

changed. Um, Cognition and Cursor have

emerged as like very large startups. I

can't even call them startups anymore.

It's like uh what we've been calling is

agent labs that are starting to rival

the model labs in terms of market pool,

valuation, employees, what have you. Uh,

and I think it's one of the most

interesting stories of the year.

>> Yeah. I mean what's what's fascinating

about this is it is I don't think anyone

would disagree that this is if not the

dominant or most important AI theme of

the year it's certainly got to be among

the top two you know and it was not on

the radar as the thing that was going to

drive all conversations you know when

everyone was doing their end of year

content you know their end of 2024 into

2025 content predictions no one at least

the one that I saw was like this is the

year of coding this is the year of AI

coding AI coding agents it was the AI

the year of AI agents broadly right that

was sort of like the money's on bet for

what happened I mean vibe coding only

carpathy said that tweet in February

right it's it's it it feels like a

million years ago because of the

inevitability but it really you know we

we are kind of just catching up with

ourselves in some ways

>> uh a little bit and uh I actually also

you know have a spicy thing because

generally I agree with Andre in

everything and most people do but I

think the one thing that is uh happening

right now is that the software engineers

are feeling very uncomfortable with vibe

coding and I think you know you talk

about how we're all 6 months ahead of of

of the of main street vibe coding you

know I declared the end of vibe coding

being cool uh this month and I think a

lot of what we're meeting to discuss in

uh at AI code summit is is like what's

after vibe coding like how can we avoid

the slop and like build software ware

that we don't hate. Uh don't get don't

get stuck in rabbit holes that the

agents might go down sometimes and uh

it's going to take work from the model

labs which we which we have represented.

It's going to take work from the agents

and it's going to take work from the uh

the customers which we also you know

want to hear from. So I I think it's uh

it's interesting because like there's

new terms and like people viping super

popular but I think it also might need

to evolve in some way.

>> Yeah. Well, so so le let's actually try

to unpack this a little bit because this

is this is sort of to me this was like

okay declaration like Sean's now in uh

in spicy mode for what what's coming

with this event, right? I think the

tweet was rip vibe coding 2025 to 2025

or something like that like perfectly

constructed tweet. But so let's talk

about what where the where the

discomfort is coming from and maybe sort

of like what the the difference between

what someone who's sort of excited about

this term still is thinking about when

they see it versus what this group of

engineers who are getting more

uncomfortable with when they when they

see that term what they're kind of

perceiving.

>> Yeah. I think the issue comes with like

we every one of us every software

engineer is very happy that people who

are nontechnical can get to somewhere

productive without engineers. engineers

are expensive. They're uh hard to do

work with. They're divas. They, you

know, whatever. Like just just, you

know, um they don't need to help make

your website, your personal website when

lovable and bold exist. And I think

that's nobody's nobody's has any issue

with that. I think it comes to a head

when you start to say like, "Oh, I I

vibe coded this. Like, come on. It only

took me like an hour. Uh now here, here

take it." and I expect the full thing by

Friday and like well like you know you

haven't dealt with any of the hard stuff

you you've only painted uh the the sort

of superficial picture and you you

confuse that for the full working app

that's one issue that is the sort of

non-technical to technical handoff that

is not being discussed negotiated uh in

fact what is happening is the infra

layers are specializing for the

nontechnical people so that the non the

the sort of vibe coders nontechnical

people are building basically building

of a completely different stack than the

technical ones. And so when you hand it

off, you have to completely rebuild

because it's it doesn't use any of the

same tech. I'm somewhat exaggerating. I

think the the the best crossover tech

right now is Superbase, which is why

Superbase is doing so well. They've

basically quadrupled valuation this

year, you know, but like the there's a

lot of like experimentation in in just

that that front. Then there's also the

inter software engineer fights where

software engineers are also vi coding of

course but some of them are being a lot

more sloppy than others and the people

who care about software care about

security care about maintenance care

about uh honestly just like getting

things uh right or understanding your

code so that you don't get into trouble

because LMS just do run into rabbit

holes and sometimes to really get them

out you have to understand the code. You

can't just sort of wash your hands off

it or or just flow based on vibes. So

when when that when that stuff happens

and people are irresponsible, then they

also tend to like leave PRs to other

people have to clean up. So you know, I

think like people just want something

better. Uh a lot of people are talking

about spectrum development as as a way

forward, which is something that Amazon

is is pushing a lot. uh as well as a

number of other people like my top

speaker from World's Fair was Sean Grove

from OpenAI who was basically pitching

spectrum and development and model

alignment specs. So like I think there's

a lot of action around this. No one the

the term that has to to sort of replace

or complement vibe coding hasn't emerged

yet but I can definitely feel it in the

air. It's literally present in every

conversation I have. Everyone's sick and

tired of VIP coding.

>> Yeah. Is so it's super interesting.

What? So, a couple things. One, there's

this classic pattern with change,

technology change, where we forget

temporarily that the paradigm shift

isn't going to be from a set of problems

to an era of no problems. It's trading

one set of problems for another, which

hopefully are a it's a good trade-off.

It's a sufficiently good trade-off that

that new set of problems we'd rather

deal with because of the gains that come

from the switch, right? And I think that

that second part of the conversation

that you were just mentioning sort of

the intraengineer conversation is a lot

about that. It's like okay well now we

have to reconcile with you know all of

the stuff that comes along with if we

can do xyz much faster or automated or

with background agents it creates this

new set of problems and we are still

going to have to deal with those. we're

going to have to rearchitect our systems

and and sort of, you know, the the way

that we work to accommodate that. And I

think that that's a very natural process

of like figuring that out and actually

sort of rationalizing what it looks like

to use these systems well even as the

technology is changing. And and I want

to come back and kind of talk about

maybe the the sync async spectrums and

and a couple other things that you've

talked about as it relates to kind of

where where these things are. The first

one, you know, I I was thinking about

this. We really don't we don't have a

word for the difference between sort of

professional and amateur in the context

of a democratizing technology, right?

Like you know, if you think about like I

was trying to trying to make the proxy

of like content creation with social

media, right? Tik Tok and Cap Cut come

along and everyone can make videos.

There's clearly a difference between

amateur videos and Christopher Nolan and

no one would not acknowledge that. And

then in the middle it gets blurry of

course and there's some people who may

not be as technically good but the

things that they produce people like

more and you know but there's still like

you know the terms that we have are all

are all dumb right creator influencer

like they just kind of they don't

actually convey this gap and I think

it's actually one I think it's

completely unsurprising to me that

coding is sort of figuring this out

first in the context of AI you know AI

becomes this mass democratization

technology but there is still a

difference between to your point like my

sick terminalbased you AI daily brief

website that I use lovable to maintain

and like an actual product that goes out

and you know an enterprise is not going

to freak out on because it's it's got

you know kind of its security setup you

know we just don't we just don't have

good good terminology for that which I

which I think is is a challenge cuz to

your point I don't think anyone is

actually in disagreement that these

things are are different things. Yeah,

>> I think to some extent it is our job to

figure it out. Like this is not an

unsolvable problem. And so I don't I

want to put people at ease here in terms

of like keep you know keep keep doing

what you're doing. Keep keep uh keep up

with the Bolton uh and lovables and vibe

coding in general. I think it is the job

of the engineers to try to figure out

that transition path because we haven't

worked it out yet. You know, I'm gabbing

people and like trying to focus people's

energies on on this because clearly like

when a new technology emerges and it is

somewhat disruptive to the old

technology. People who are tied to the

old technology complain, which is

exactly what they're doing here, by the

way. But also the goal is not to reject

the new technology, is to embrace it and

figure out how to reshape everything

else uh in order to accommodate it. So I

I think like there's there's more

there's more synergy here than like

people fear when when they when they

first hear about this stuff.

>> Yeah. Yeah, I wonder I wonder if there's

I mean you know I don't know if it's an

interim solution or not but it feels

like there's there's a role or at least

a function around sort of translating

you know if you've got all especially if

you think inside an organization or a

startup you've got all these folks who

are now able to speak with code right

instead of talking about features they

want they can just you know mock them up

which is you know what we do what every

company I know at this does you're

you're talking about sort of the

challenge of translation it feels like

that's that's a thing that someone could

get really good at you know both helping

people sort of you know build things in

the right way in the But anyways,

there's there's a lot lots of

developments that I think are going to

going to come on that front.

>> Yeah.

>> Okay. So, the next thing I wanted to

talk about which is sort of you know

builds off of this a little bit is what

this landscape of AI and agentic coding

platforms the full breadth of it now

because part of the the challenge and

and why sort of vibe coding RIP I think

is that like if you go back six months

ago it's like who's going to win bolt or

lovable? It's literally that and then

cla code comes and it's like okay now cl

you know as opposed to now people people

with a passing glance see lovable bolt

claude codec cli uh cognition factory

and and it's sort of this you know this

broad spectrum and and you actually

wrote about this a little bit when when

you sort of shared that you were joining

cognition huge congrats by the way I

think that's by the way for for my money

maybe the most useful uh I I'm making a

career switch blog post that I've ever

seen

Um, usually that's a very very sort of

self-indulgent thing. It's just like

here's my trajectory that was like kind

of packed with interesting observations

and one of them that you talked about is

the this sync async spectrum. I would

love you know without asking you to kind

of boil the ocean share kind of roughly

how you see the topography of these you

know of of these categories of coding

tools emerging right now.

Yeah, you're making me think about uh

other conversations I've had since since

that that uh publication. But yeah, so

so totally. Um I think there are a

number of charts that people have made u

you know basically coding agents are

enormously popular and now we're just

figuring out what the ideal interfaces

for them are right so probably it

initially started with GitHub copilot

which is uh just spicy autocomplete as

they say. Devon launched like two years

ago with sort of the web app um sort of

interface code interpreter is also in in

the mix somewhere in there where uh you

know you can chat and like it starts to

generate code and run and execute that

code I would say then cursor obviously

uh with composer and you know all the

other cursor agent stuff that they're

launching. So I think like now the form

factors are you have the IDE or V or or

VS code extension, you have the web app,

you have Slack or whatever your sort of

team collaboration thing is. You might

also want to put linear in there. Uh and

then finally you have the terminal which

is obviously like the newest that

emerged on the burst on the scene this

year with cloud code. basically you just

need universal handoff for among

everything and uh I think like that is

the goal you know everything I described

all the surface areas all the companies

pretty much have all of them now I think

uh with like cloud code going to the web

and codeex coming to the VS code

extension it's everyone's got everything

and I think that the handoff is like not

worked out yet so cloud code is the

first one to work out like a the

hackiest possible version which is uh

cloud code teleport where you can just

sort of dump the JSON of the chat and

continue with it in locally which

because they're the same uh instance uh

the same cloud code on both sides uh but

I think like there may be some more

evolutions from there because that's not

naturally how we transfer context

between engineers working differently

and so like in my post uh I started

talking about the sacing spectrum and

you kind of need to own that which is

why I was very impressed with cognition

buying windsurf when was up for grabs

because well you know here's like the

number two IDE it's for cheap because

you know like a month ago it's worth 3

billion. Now it's worth like less. Uh

the rumor is a 300. I I I I actually

haven't even confirmed uh that number.

But yeah, I mean like you know at some

point it's worth buying and and actually

you know you're starting to have a leg

up in in that sort of sync side of the

the spectrum while Async is having

extreme product market fit, right? Like

I I talked a little bit about the

numbers in in the Cognition blog post as

well. So like I think that's good. I

think actually sync async might be a bad

framing uh which is really weird for me

because one thing that's happening now

that you're going to see with cursor 2.0

today and also uh what cognition is is

launching is that the async side is

moving faster rather than slower because

I think there's been a perverse

incentive to measure all these coding

agents based on the number of hours

worked and like you know where else do

we do that? Well, lawyers and like

everyone every and like you know

everything that we hate uh because

you're just incentivizing them to take

more time uh which is horrible like no

one no one actually wants that we're

just using that as a poor proxy for like

how you know what difficulty of work and

you actually doing so everyone's working

on faster agents I think which is good

for users ultimately because that's what

we want in practice the async side is

becoming more sync and then the sync

side is changing in terms of like the

the mindset right like why do you want

uh synchronous code. Well, the the

actual answer is because not everything

can be viodated. Uh like the anti- vibe

code is to turn your brain on instead of

off and use AI to augment your skills

and thinking rather than to replace it

and with scrolling Twitter, right? So

the sync mode is for the deepest focused

and hardest problems where you need the

centaur combination of human and AI. Uh

and so that's that's what I posted in

the the recent thing we shipped on

Swiggraph where we have the uh sort of

async value of productivity, right? Like

either you're super productive because

you're you're in flow and you're focused

and you're working on hard problems. uh

if agents take longer then you start to

switch away and change context and lose

context and then later on when you you

start to get more productive again

because you are able to employ parallel

async background agents on stuff that is

like really commodity and like you can

trust them to nail it.

>> Yeah. So what this reflects to me is the

richness of the just the topic of AI

coding this why why you can do an entire

summit about the variety of

conversations going on here what are

some of the other conversations that

you're trying to bring in you know that

have been maybe part of part of previous

summits that you've done you know eval

memory context like sort of what you

know rag like what what are what are

some of the other big kind of big

hitters that are going to be you think

key parts of the conversation heading

into this year's event.

>> Yeah, I think memory and planning are

always going to be huge. Context

engineering is obviously a huge theme

this year and we have the the guy who

one of the three people that coined

context engineering

uh speaking. Dex Dex is a fantastic

speaker, one of the top speakers at

World's Fair. And I think like then the

other part is honestly just like

organizational transitions which

actually uniquely as a podcast you you

will cover which is rare uh which is

more of a leadership topic, right? like

uh sure like the AI exists but like how

do you like move an existing large

organization to take advantage of it to

upskill your team and maybe potentially

reorg in order to uh capture the the

opportunities right like I think like

this is one of those things where like

for the first time I'm able to feature

people from like Goldman Sachs and

Mckenzie and like you know like the some

of the top enterprises in the world uh

Northwestern Mutual uh you know and like

Bloomberg's coming back this year.

There's just a lot of like very

interesting especially east coast

stories that I wanted to feature because

a lot of tech is like very west coast

ccentric but uh there's a lot of good

stuff happening in enterprises too.

>> Yeah. on the organizational change

piece. Um, one of the things that I

think is is really interesting about and

and I think to me was reflective of just

how dominant the AI coding theme has

been this year is when we started, you

know, when we were we were kind of first

doing some of these agent audits around

the beginning of the year, it was very

often the case that the engineering

departments were surprisingly some of

the holdouts. They were the sort of most

intrigent around wanting to to adopt new

systems. And while I don't while my

perception is not that that's gone away

entirely, it does feel like there has

been a major shift over the course of

the year. Perhaps as the tools have

gotten better, as the models have gotten

better, as you know, maybe our

understanding of, you know, how to

integrate these systems has gotten

better, certainly not universal, but we

see less and less, you know, just, you

know, over my dead body kind of, uh,

engineering departments when it comes to

some of these transitions.

>> Yeah, totally. Um I I think like there's

a lot of knowledge sharing in this kind

of stuff, but it's also like not fully

uh well mapped out. And honestly, I'm

waiting to hear from you and you know

the rest of the speakers on the on the

leadership day to map out like the state

of affairs and like what is working,

what is not among the the enterprises

that you talk to. So speaking of that

one term, you know, basically going back

to what you were saying about vibe

coding, it almost feels like part of the

challenge is that it this this same word

or same phrase means different things to

different people, right? I think that

context engineering is going to be a

term that has a similar bifurcation or

potential bifurcation in in the year

because context engineering is is a very

sort of like technical set of of

questions for engineers who are thinking

about how to design systems that better

interact with context. But it is also

now a uh a leadership or sort of a

change mindset as people like basically

sort of akin to prompt engineering for

individuals where organizations are

thinking about context engineering as

how do we sort of organize our data

broadly speaking to be ready to be used

by these systems. How do I think as I am

prompting individually as a as a sort of

you know a frontline worker in a

company. How am I making sure that I'm

giving it enough work context? And it's

not that that's obviously a totally

separate thing, but you know, the the

one is not thinking about different ways

for kind of technical methods for the

LLM to access different information.

It's more of a mindset shift getting

away from just strictly prompt

engineering to making sure that your

your cloud skills are updated with all

all the things that they need. And uh I

wouldn't be surprised if we see again

there's sort of like the enterprise

non-technical conversation around

context engineering which is going to be

sort of like a very broad use of the

term context and a very broad use of the

term engineering as opposed to maybe the

more technical conversation.

>> Cool. I don't have a view on that yet.

That's something that you're picking up

better than me. So I'm curious to learn

more as

>> Yeah. Flesh out.

>> It's a prediction, not a not a not a

fatal complaint. So the the last couple

of things I I wanted to ask you about

move back to the blog post that we were

that I was mentioning the Devons in the

details. Um the two things that I think

really stood out to me. One was your

kind of very simple 8020 sort of notion

of of code AGI. Uh I I'd love to just

sort of like hear about that a little

bit. So the the quote is I'll quote

yourself you so you don't have to quote

yourself but the line was the central

realization I had was this code AGI will

be achieved in 20% of the time of full

AGI and capture 80% of the value of AGI

so talk talk I would love to hear just a

little bit about kind of h how you think

about that I think it will resonate even

with my non-technical audience just

based on how much coding has shaped what

we've all done with AI this year despite

not being coders.

>> Yeah. Um, well, I mean, so I would say

that there's a little bit of self cringe

when I when I really boiled it down

because obviously the world is never

that simple, but you have to think about

the highest order bit and you have to

think about concentrating your bets

>> uh instead of spreading them out when it

comes to power laws. And so 8020 paro

principle framing is is the way that I

do it. Um, okay. So, and then the other

the other irony is code AGI is a I don't

know what's what's what's the word for

like self-contradiction because if it's

general, it should be general. It

shouldn't it shouldn't be.

>> Right. Right. Right.

>> But like um you know all all that all

that aside, I think that the general

sentiment is what I was trying to

reflect which is literally value capture

versus timelines. And I think those are

the right two axes to really think about

in terms of where to spend your time and

uh where to invest maybe and which is

which are the same thing. You're

investing your time or investing your

money. And so I I think like one I think

the the obvious statements are all all

listed in there which is how like code

is like a verifiable domain. It's much

faster. The people working on the code

are also the people like you know

consuming the models. So like there's

there's just like a general virtuous

cycle that is obvious in there and like

basically doesn't need any more

elaboration. Uh I think the interesting

thing comparatively here is also the

value side uh instead of just the

timelines which is obviously happening

now a little bit but like you have to

really and for me to join a company

that's that's worth 10 billion you know

like what's the upside like 20 like no

like it has to be 100 and so I think

like you have to really think through

like is that even on the cards and I

think uh yeah probably like and and

that's mostly because of the flexibility

of code I think that the best way to

commute ate this is with like how many

people and how many times people have

observed that you can use cloud code to

do non-coding tasks right because it

does generalize it it has the sandbox of

tools uh we used to you know in the

chatbot era only do uh you know

embeddings retrieval right but now we

have like agentic search and that

basically requires a document library

and all the things that people talk

about in the you know the modernized

LMOS stack uh for people who are

interested in this uh check out Jerry

Lou's talk from the uh 2025 holds fair

and he talks a little bit about the the

emerging stack here and so like I I

think like that is probably the case

where like there's the things that we

learn in coding agents basically

generalize and actually the the people

who work on coding agents first will

will find it faster because they already

have like I it's like super obvious to

me that they that they already seen it

in some ways like cloud code is a is a

new foundation for cloud itself like

when people talk about like the cloud

platform or They people talk about like

cloud for finance which or Excel which

was launched this week. It's all based

on a foundation that was built with

cloud code. Uh so it's like it's funny

because it's I'm not even really really

putting my neck out on on this thesis.

It's just I'm just calling it out as

something that's already happening.

>> Yeah. No, it's it's super interesting.

Like I said, I think I think it's a fa

fascinating way to look at things. And

the la last thing that I wanted to ask

you about is so I' I've said a number of

times on the show probably enough to

start to annoy people that I think two

dominant themes heading into next year

at least for sort of like the business

the AI at work side of things. Um one is

I I actually think is context

engineering and just thinking broadly

about what's the set of information that

we need to provide you know whatever AI

we're using for it to do better than

just whatever it sort of can do out of

the box. I think that's going to be a

massive theme and I think that part of

why it's going to be a big theme is that

by making it a theme it gives

organizations the license to do unfun

very difficult things like you know big

data uh projects that were you know less

sexy than like coming like coming into

this year was like what what

demonstration agent can I build I think

going into next year it's going to be

more like how do I get this entire house

in order and there's going to be you

know sort of wind and wind at people's

backs for that. So that's one. I think

the other very obvious one is ROI and

performance. I think it's easier said

than done, but I think there's going to

be a lot a lot a lot a lot of discussion

around, you know, how these AI uh and

and agentic systems are actually sort of

impacting the world of work. You know,

be it time savings, cost savings, new

capabilities unlocked. Uh I think that's

going to be a major exploration. The

third which I'm just starting to sniff

and uh and so I'm not ready to sort of

call it on that same level is I think

that I see this conversation starting

around the product era of AI and the

emphasis on products in which AI is

situated being the things that people

are are releasing and focusing on as

opposed to the conversation just purely

being you know how does this model

compare to the one that was 0.5 before

it and you had uh it was not this this

wasn't the conversation But one of the

things that you talked about was the

sort of difference between agent labs

and model labs. And uh I'd love to just

that if you if you want to share that

framework because I think it might might

have a stake in that that larger

conversation as well.

>> Yeah. Okay. There's there's a lot in

there. So uh first of all product era is

a broader thesis than agent lab. Um I

think product era is basically in VC

terminology is the application layer

winning right like and definitely 2

years ago application layer was very

unsexy people made fun of it you're just

writing GPC rappers now they're like $30

billion companies and like you know 50x

sales and uh Harvey and and cursor and

all these guys are are doing super well

a bridge uh you know open evidence what

what have you um so so I think like yeah

the the the product era has definitely

happened but the specific type of

products that is doing super well is

agents. So like uh that's how I make

that transition. I think like as a

product person sometimes you can

overthink it and if you really just look

at like what the heck people are

actually having PMF with it's just

agents like Reb spent like two years

like working on AI products and like got

nowhere and then they build an agent and

then suddenly they're like get $300

million revenue. Uh so it's like um kind

of obvious um if you just take it

literally anywhere like uh you know like

uh notion like getting serious of agents

is is is very good for notion uh all

that stuff. Okay. Uh the agent lab is a

thesis that isn't quite fully worked out

yet. Uh but it's really just the case

for building AI companies in a different

way than has been in the past. Uh I you

know obviously I love two word uh

coining things that are two words and I

I love uh like I love the way that like

people start to organically adopt it

which is why I know this this

terminology is working because like now

people are saying it without even me

being present in the room. Uh the the

Asian lab thesis I'm going to I'm going

to pull up this uh this this guy's

coverage of my clothes which is uh which

is really helpful. uh it's basically

like people shipping products first into

the model first, right? Like a lot of AI

companies in the past, they would just

basically say they they'll raise a bunch

of money, announce they have a bunch of

money, announce they have a bunch of

cracked researchers, they buy a bunch of

GPUs, and then you don't hear from them

for 6 months uh or or a year, and then

they they come out with like, oh, here's

our model, you can't try it, but like we

here's here's some interesting updates

from our model. That's exactly by the

way I I mean I'll come right out and say

it like when when we launched street

grip in in cognition like it I was like

oh this is why like magic with their

their 100 million token model never

launched because their model lab uh and

cognition is agent lab build the agent

first and then build the model and I

think that was like a a backto-front

theme that I think really starts to play

well. It it remains to be seen obviously

because I think the bitter lesson

applies and scale in infrastructure and

GPUs is king. Uh how much of the

relative value agent labs can capture

with model labs? But I think that's

really bifrocating and like it's so

weird. Yesterday OpenAI like kind of

proved my point like did you watch the

live stream from yesterday?

>> Mhm.

>> Basically Sam was like we're giving up

on products. We're we're building you

know infra we have like chip we have

Sora but that's about it like everything

else is third party we're going to be a

platform you should make more more money

than us on our models right he said all

this and like I think to to me who has

been watching openi as long as you have

uh that's never been that clear like

they always wanted

>> Yeah totally I think I think it's

probably been not clear to them I think

they've been debating it back and forth

constantly

>> they hired a CEO of applications that's

curious because now they only have too.

Um uh but like you know there's there's

going to be applications built on uh

CHBT but like that's that's a different

thing anyway. So so I think like now the

lane the swim lanes are very clear right

you want to build AGI go join a model

lab. You want to build uh products that

serve users and and vertical domains

build an agent lab. And I think like

that's really what I'm seeing with the

agent lab thesis. I I think there's

probably like more to flesh out here on

like what a good agent lab looks like

versus a bad one.

But like I I I'm pretty curious and I

think like that explains the entire

differences between the vibes that you

get from agent labs versus model labs. I

think one of the interesting

implications maybe we'll explore this in

the in the talk in a couple weeks is it

might force enterprise buyers to think a

little bit differently. I think that it

has felt for a while like you could

effectively avoid pretty much all that's

happening in the longtail and just deal

with you know the you know the the the

foundation model companies or maybe the

one sort of like leading vertical player

in your industry like if you're legal

like maybe you deal with Harvey or you

know if you're in medical you deal but

like but not you know one of the reasons

that I don't have a ton of space on the

show to cover as many of the cool new

products as I'd like is so much of the

audience is like well I can I just I

mean if I use it in my personal life

great but there's no universe in which

that's coming in and if it really is the

case that the model companies decide

that they really are going to be

platforms and let uh let the sort of you

know the agent labs build the next set I

think you will have to see an expansion

in just the procurement process which is

very very discreet part of the

conversation but an interesting one. H

um yeah no no no difference uh take on

that. I I think maybe the one hole in

this thesis is maybe anthropic because

they are really building out cloud code

to be an agent lab within the model lab

and every model lab can easily build an

agent lab for sure. Uh it it is just a

matter of resources and a matter of

honestly social pecking order to be an

applied AI engineer inside a model lab

is like low status. You're paid half

what the researchers get paid probably

less if you're working in meta. Um I I

think like I think that it's interesting

um how seriously the the labs taken and

obviously there's a very very wide

variance but typically like typically

and I speak to plenty of people you know

in those in those roles they are more

like the four deployed engineers but

they're not involved in research and the

company clearly values research more and

it just that's just how it is.

>> Well Sean awesome conversation could

talk to you for hours but uh excited for

the event coming up in a few weeks. Uh,

thank you for hanging out and um keep

keep telling us where the future is.

>> Yeah, I'm excited for your talk. Do you

want to preview your what you what you

gonna talk about?

>> The I I don't know yet, but what I do

know is that I'd like it to be

substantive as possible. So, I don't

know if you've seen, but I've got this

thing live right now, ROI survey.

>> Um, like I said, I think I think that

next year there's going to be so much

conversation of ROI. And this is like

the kindergarten version of ROI. It's

literally like tell us your top use

case, which of these eight areas is sort

of like the biggest area of benefit,

time saved, cost saved, whatever, and

then give us your subjective rating, you

know, of it, like how many hours per

week or what it's it is so generic, but

I still, you know, it's been live at the

time of recording for like 36 hours and

we have, you know, 250 plus use cases

that people have logged in and said,

"Here's how it's benefiting me." And

already that's such a different amount

of information that that we don't really

have access to. So I'm I'm hoping that

there's something that's interesting

there maybe combined with some of the

other uh other readouts and and and

learnings that we've had from super

intelligent. So it's not just me

rambling. It's uh it's a little bit more

data driven, but we'll see. We'll see

what's ready by by uh November 20th.

>> Good. Uh yeah, the ROI of AI is a

perennial topic. Uh just like just like

every other leadership uh thing. It's

it's it's weird because I can just have

the same schedule every year and it like

Totally different.

>> Yeah.

>> I mean, I hope we solved some things.

Uh we'll see. But human human problems

will always make new ones, you know, to

replace the old ones.

>> Uh but yeah, thank you. I know trying to

wrap up. Yeah.

>> Thanks, Sean. I'll see you soon. Season.