to help me break down all things uh

around this incredible abundance. I'm

gonna bring up Anisha Charia.

>> Hi.

>> Awesome. Awesome. Awesome. Hey, Anish.

Uh Anish and I were at the at GP offsite

uh earlier this week and and he shared

with me that he's already running

Grockbot uh and has purchased a bunch of

jeans for him. So, Anish, do you want to

do you want to drop what you purchased?

>> True story. True story. Yes. So, I'm

going to reveal an important secret um

protected IP, which is that I mostly

wear frame jeans. Frame is a great

brand. Um,

>> and Grock Bots is an awesome product.

Actually, I'd say the kind of defining

characteristic of Grockbots is sort of

resourcefulness. You know, I went to bed

a few nights ago and said, "Hey, uh, buy

me a pair of jeans that are inspired by

these." I took a photo of my current

jeans. Um, I said, "Don't spend more

than $500 and get it done." I woke up in

the morning and it had researched, found

a pair, same fit, different wash, used

my credit card, purchased them and

they're on the way. So, I think that is

going to be something that we see more

and more of. We already have the

capabilities and now a lot of the kind

of unlock will come from resourcefulness

um and also the kind of product

architecture delivered in a way that

most consumers um can understand.

>> Awesome. Awesome. Awesome. Yeah, I told

my team that I'm going to set my bot to

finally take care of the pile of things

I've been promising my husband that I'm

going to sell [laughter] for the last

two years. That is the project for for

this weekend. Uh so Anish, we asked the

question earlier, which one of today's

AI leaders will be the clear winner in

three years from now? What's what's your

take?

>> I'm a many winners guy and I see I'm in

a you I'm in good company with many of

you. I mean, if you look at what's

happened in the last two weeks, um, you

know, XAI went from not even being a

real contender on the model side to

being, you know, one of three. So, we

extraordinarily went from a two- horse

race to a three-h horsese race. And, you

know, even more broadly over the course

of the year, we went from anthropic

feeling like they were so dominant, they

could do no wrong to OpenAI who's just

had an excellent three months. You know,

the new models are exceptional. The new

codeex harness and chatgpt desktop app

is very well done and we're seeing the

sort of specialization in different

directions of these labs. They're both

growing like crazy, you know, despite

each other's continued successes. Um,

XAI and as openweight does well. Um, so

I'm definitely in the many winners camp.

>> Yeah, it's interesting to see the

sentiment also on X, which is not always

a perfect, you know, uh, weather vein

for the future, but but oftentimes a

early indicator of at least where

developer sentiment is. And there's been

a lot of push back from Claude it seems

like recently on people uh in terms of

token usage etc. And so you know

developers tend to be fair weather fans

on these things they will go where the

latest and greatest and very best model

isn't and particularly the last six to

eight weeks I think we're going to see

some very interesting um traction in

terms of the flow of activity but

obviously anthropics going public uh

later this year um and also you know

there's a lot of lot of keen interest on

this. So with that uh that actually

brings us straight into the topic of

discussion today. So where where and

what is next in the next frontier of

intelligence.

>> Amazing. Thank you Jen. So let me tee

this up for everybody. Um and please hop

in if you've got questions. So let's

first cover the kind of macro and what's

happening um at a market level. Then

we're going to hop into the application

layer broadly and sort of talk through

why applications are the productization

of the intelligence primitive. And then

finally let's talk about consumer. you

know, with the launch of Grockbots and a

few other products, it's actually been a

very fun um couple of weeks in consumer.

Okay. Uh hopefully our our dear friend

Leopold doesn't mind me poking a little

fun at him here with situational

awareness, please. Next. Um all right.

Look, I think that the kind of case for

this being a bubble um is over sort of

discussed or at least fully discussed. I

think actually the out of distribution

topic that's less discussed is what if

we're insufficiently optimistic? And if

you look at some of the underlying

indicators, what they point to is

essentially infinite demand and highly

constrained supply. You know, things

like B200, which is a non sort of

cutting edge GPU prices going up on a

per hour basis. That is very uh strange.

Normally, we see these things be highly

deflationary and it sort of points to

very constricted supply and essentially

infinite demand. So we're thinking and

talking a lot about what's the kind of

informed case for optimism here given

some of these second order indicators.

The SAS bubble was a very or the SAS

sort of uh you know whipssaw was an an

interesting peak into market psychology.

You know back in February when we saw

this you know 30 to 40% draw down on a

bunch of SAS names. We said that the

market has oversold software. Lo and

behold here we are many of those names

are back up 40%. So I'm not quite sure

what we collectively accomplished, but

I'll tell you what we said then, which

is still true today, which is for the

enterprise, software spend is 8 to 12%.

It's just not a huge proportion of

spend. So the upside to vibe code your

own payroll or CRM is not particularly

high. The downside is essentially

unlimited. You know, obviously there's

all kinds of sort of compliance um

implications of not getting things like

payroll right. So most enterprise

software today demands a level of

precision that just isn't afforded by

coding agents. Um the the one thing that

has happened though is the sort of tide

has receded. So for a lot of SAS

companies had a ton of SBC and you know

things that distorted their economic

performance. I think that's very much um

visible now and they're going to have to

sort of accelerate or die. So, so less

bleak for the SAS uh sort of market than

perhaps we all collectively thought for

a few months there. But still some sort

of existential questions to address. You

know, there's been a huge sort of

discussion of moes. Are there any moes?

There's no more moes. And it's it's very

funny because if you actually study u

moes, which I think are most famously

codified in the book seven powers that's

one of my favorites. The vast majority

of moes actually are not affected by

abundant lowcost intelligence. You know,

when you think about network effects,

scale effects, which shows up in

distribution, brand effects, which we

tend to discount in Silicon Valley,

these things are as good as they've ever

been. You know, no amount of coding

agents is going to make Nike not Nike.

And Instagram, um, the power of

Instagram was never the complexity of

building the Instagram app. Of course,

it was the kind of network behind it.

So, you actually think the majority of

modes are as good as they've ever been

and and of course are still crit

critical to building compounding value.

There are a couple of modes that are

exposed. For me, the integration mode is

the most obvious one. You know, SAP is

is so famously complex to integrate into

and out of that it's a sort of

existential risk to even migrate from

one version of SAP to the next. Coding

agents makes this dramatically better. I

think there's a bit of an existential

question actually for SIS and gsis as to

what will their value be when they've

historically been this sort of point of

integration. So, I do think this moat is

a little bit at risk, but for the other

traditional modes, they persist and

they're as important as they've ever

been. Yeah, I think this is a really

important concept. You know, as you

start to think about what are the job

functions in the enterprise that are

alpha creating, it's typically product,

sales, engineering, research, and

conversely, what are the job functions

in the enterprise that are sort of maybe

administrative is is uh too uh too

bleak, but they are supporting other

functions, legal, um HR, finance, etc.

We really think that the kind of

rational architecture and the one that

is emerging is that for jobs that um

have unlimited upside like sales or

product you always want to use frontier

tokens. And the reason for that is you

just don't know what the value of the

new product feature or closing a

customer account is. It's effectively

unbounded and therefore it's

economically rational to pay almost any

price for a model that's even one IQ

point smarter. You know, your Fable 5 or

your Gro uh or your um GPT56.

Conversely, when you talk about

something like finance, you know, the

best way to close the books is

accurately. You can't close it, you

know, 10x better than accurately. So, as

a result, you kind of have this bounded

upside problem where it makes sense to

use openw weight models with

reinforcement learning for the kind of

paroefficient um cost curve. Maybe

before we we go off this one because

this is a big debate and and again when

Kimmy dropped a few weeks ago there was

a lot of consternation uh about this

this topic just given the relative cost

which was the focus of of the topic of

discussion. But you know um our our

founder Jesse Zang from Decagon dropped

this great post around the fact that in

some respects and and for a lot of

companies like Decagadon open source is

actually the only option. It's not just

cost. It's it's that they can actually

localize it, train, fine-tune it. And so

maybe unpack a little bit of that

configuration. Talk through the the

nuances there and why folks shouldn't be

concerned even though that is the case

for startups that there's a lot in the

way of abundance around this topic.

>> Yeah, I mean one of the big topics that

we're seeing or one of the big trends is

that there are just one there are sort

of comparative advantages of different

models. So and the models often have

sort of areas of focus that are almost

at um tension with each other. So you

see a certain set of models that have a

high degree of neuroticism. Like there

sort of autistic models. GLM52 and GLM53

are great examples of this where they're

very literal and they'll only do exactly

what you told them to do and nothing

more. Then we're seeing models like a K3

um that are just much more sort of open

and they're very presumptuous and

they're creative and there are roles for

both types of models in the organization

and and often the sort of shapes of

those minds if you will are at odds with

each other. So that is like one reason

you actually want to have multiple

models. The reinforcement learning is a

really important point. Um you know if

you actually have a problem that you can

specialize the model around with your

reasoning traces, you can start to

create this compounding advantage in

your domain for your customer base where

you're able to kind of shape the

intelligence to be better than any

general intelligence for your problem. I

know Harvey's had some great results

with this as well. Now the trade-off of

that kind of reinforcement learning is

you lose generality. So if you have the

best sort of model that's fine-tuned for

solving legal problems, it may not be

great at solving sort of theoretical

math problems and that's okay for

Harvey's uses or in the case of decagon

customer support. So this sort of openw

weight specialization property is

something that's very unique and one of

the reasons our startups are selecting

them. This is also a big topic. We've

learned so much since January. We should

really do this monthly gener. Yeah. I

mean honestly we there's just so much

changing. So in January February there

was a lot of discussion and it's it's

very idiosyncratic and interesting. You

know anthropic quad released what is

called a legal plugin. You know plugins

are just collections of skill files. You

can think of it as a zip of skill files.

Skill files are just prompts. They're

just long prompts. And there was this

huge panic and all of a sudden Thompson

Reuters and a bunch of other sort of uh

you know big legal names traded down

dramatically. But those were really just

prompts. And there's a lot of discussion

about if labs were going to integrate

vertically integrate up into the

application layer. Instead, we've seen

the very opposite, which is yes, they

are vertically integrating, but they're

vertically integrating down into

inference and compute. It's actually

logical now um in hindsight because the

workloads for inference are very

homogeneous. So, you can build enormous

scale in one part of the value chain.

Whereas when you think about the

application layer, you know, you've got

so many idiosyncrasies and unique needs

in terms of pricing, packaging, um, sort

of productization, how the market wants

to buy. So, it's actually a much more

challenging and opex heavy proposition

to move into the application layer

versus moving down into the inference

layer. And this is the point I alluded

to earlier, which is sort of this

discussion of model commoditization. And

you know if you use the models every day

which I do I sort of hold myself to a

standard of making something either

small or big with every model that comes

out you you start to appreciate the fact

that these things are are not

commodities that they have comparative

advantage at a domain level. So a great

example is open AI with their new um GPT

models are just so so good at knowledge

work. The harness is also very well set

up for knowledge work. You know if

you've used the chat GPT desktop app you

know what I mean. If you haven't please

install it. It's very very cool and

interesting and it's the perfect sort of

when I say harness I kind of mean kind

of product container like a browser. Um

it's the perfect product container to do

spreadsheets and slide presentations and

written documents and all of that type

of work. If you look at cloud code which

many of you I'm sure have used it's just

so oriented towards software engineering

you know it's in a terminal UI.

Everything from the small design

decisions to the areas in which it

specializes like code planning and code

testing is oriented towards the software

engineer and there are many trade-offs

both products are making for that sort

of respective specialization. So one

you've kind of got this domain level

specialization that's already occurring

and then two as I mentioned earlier

you've got this sort of I think of it as

the big five sort of personality traits

if if folks have studied that you know

you can't be both highly open and highly

neurotic. Um, and you know, sometimes

when you have an intelligence you're

applying to an accounting problem, you

want neuroticism. When you're applying

it to a design problem, you want

openness. So you actually have a need

for both types of minds in the

organization, which is why you would

select something like a GLM53 versus a

Kimmy K3. So definitely not commodities

in our view. This is an important point.

You know, there are many product

categories in which model aggregation

delivers a greater than sum of parts

outcome. And you know, a good metaphor

for this is Expedia. You know, it's so

much more useful to use Expedia than it

is to go to United, then to go to Delta,

then to go to Southwest. You just want a

single place where you can benefit from

seeing every airline's inventory.

Similarly, you know, in coding, we're

actually seeing this with cursor a ton

where you want to do a very frontier

model for planning, for example, but

then you can use a lesser model for

execution and you really need to have

one product harness or sort of product

architecture that lets you use multiple

models. Creative Tools is another great

example where you've got, you know,

models that specialize in different

modalities. So you've got something like

an 11 Labs which of course is incredible

at voice music as well and then you've

got something like Black Forest which is

doing such an excellent job in kind of

video and and creative direction and the

correct product is to bring all of these

together into one shell. And then

finally research and decisions. We see

this all the time where you know the

models are trained with sort of non-over

overlapping data sets often. So you're

able to just get more information by

running the same query through many

models adversarially and then having a

separate model sort of help you

converge. This is a place where the

application layer really shines because

labs of course are both incentivized and

structurally only able to provide their

own in-house models. You as an

application sort of aggregator can

provide the best of breed. Okay, let's

jump into the apps layer. Now the key

point about the application layer is

that you know intelligence is a

primitive just like buying cloud is a

primitive and what does Salesforce do?

It sort of takes the you know AWS cloud

primitive and turns it into CRM software

that delivers an economic outcome for

all of their customer segments. The same

thing is true of the AI application

layer. You know it's great to have the

raw intelligence primitive but you

really need Harvey to turn that into an

economic outcome for the legal industry.

Similar for somebody like credit unions

is a really interesting market segment

where they're so idiosyncratic in how

they want to buy products, how they want

the product sort of productized and and

the shape of the ambition for their

market. You know, most credit unions

don't want to uh decrease their

headcount by half. They want to double

it, right? And they want to double it

while having a an economically

performant business. So, it's just a

very um specific way that they see the

intelligence primitive playing out in

their market segment. and the

application layer's opportunity is to be

the one that kind of delivers that. This

is a bit of an advanced concept, but I

think an important one. If you look at

the kind of way that the evolution of AI

use um has gone, it's gone from

prompting models to putting models in

loops. You know, the the term agent is

overused, but agent is just a model in a

loop with sort of tools and memory and a

few other things. A great example of

this is coding. You know, we've all seen

this from um software companies, which

is a bug gets reported, it gets

reproduced, a fix gets generated, it

gets verified. If it's a low-risk fix,

it gets integrated and shipped and maybe

the customer gets an email saying your

bug was fixed. If it's a high-risisk

change, perhaps a human reviews it. But

that way, every bug that actually gets

reported to the enterprise now gets

autonomously fixed through this coding

loop. As you start to take that idea and

apply it to other parts of the business,

things like price optimization, things

like procurement, these are very natural

sort of business loops that occur that

can be fully automated by these models.

And then perhaps the most ambitious type

of loop is the business loop, which is

hey, you make a change that's very

crosscutting to the business and the

model comes back and says, hey, I think

we need to open a branch in Tijana. Now

the model can't do that autonomously but

it can make a change at the sort of

surface level of the entire business

which is extraordinary. This is how

enterprise automation is going to occur

through AI and I think for me coding has

just been over and over again an

illustration legal is another great area

of industries not markets. This is

something that Mark says and he's so

right which is if you look at

intelligence as a primitive let's think

now about coding intelligence as a

primitive. All of these products are

working in their sort of respective

areas of the stack. You know, Quad Code

does such an excellent job of kind of

exposing the raw hardware, so to say, to

the developer all the way up to replet,

which is a great abstraction layer for

the average small business owner that's

unfamiliar with code. These are

variations of sort of pricing,

producting, pack, productization,

packaging for the coding primitive and

intelligence and all of them are working

as a result. So, I think a big mental

model shift for us is ensuring that

we're assessing these as industries, not

necessarily simple markets. Okay. and

consumer consumers had a really cool

couple of weeks. You know, we've been

saying for, you know, for three years

that this is going to be consumer's

quarter, but I I think that this might

be consumer's quarter. Let's go into it.

The things that have actually held back

consumer um so far have been a couple of

things. You know, the first is consumers

don't love paying for software. We've

learned this lesson um over and over

again. And unfortunately, unlike the

sort of magic of software in the past,

um AI software has marginal costs of

distribution and engagement. And the

marginal cost can sometimes be very

high. you know, I built a an app I use

to help me browse my X timeline and it

costs $250 to onboard a new user. So, if

I'm a startup founder looking at that,

looking at a kind of $250, even with a

$0 TC onboarding cost, it's very hard to

make a mass market free product work.

That is changing now because of openw

weightight models, dramatically cheaper

and more performant. You know, the

second is we've never had an AI native

distribution channel. There's no app

store for AI. So, this actual product

cycle for consumer looks more like web

2.0 know where you have to kind of build

the channel alongside the product and

less like mobile where you actually have

the central point of distribution for

the entire ecosystem. Then the final

point I think is an important one. You

know command line is we're sort of in

the the DOS era of AI and for this

technology and its capabilities to sort

of fully be embraced by consumers. We're

going to need the windows so to say. So

I think there's just a ton of work to be

done around product and design craft to

ensure that consumers know how to

consume um all this magical new

capabilities.

two things are working. Um, so coding

agents is are extraordinary. I know have

been discussed. I think it's it's

interesting to think about how they work

for consumers. You know, if you think of

this concept of the digitally native

entrepreneur, if you're not a

programmer, the way that's historically

shown up is you're a YouTube creator.

And there's a whole moral panic that we

had, you know, 10 years ago about the

kids want to be YouTube creators, not

astronauts. But I would interpret that

instead as the kids actually who grew up

on the internet want to build businesses

on the internet and the only way to do

it again is being a creator. Now with

coding agents you can build a software

product that generates $100,000 of

revenue a year, a million dollars of

revenue a year. Now these are not

venturebackable businesses but it's a

sort of mom and pop SAS opportunity

which is emerging and I think very very

cool for the country. personal agents.

We had this collective moment of

excitement around openclaw um in January

and it was an extraordinary sort of

composition of primitives but it never

really crossed over into consumer. You

know it was sort of a developer oriented

thing more of the homebrew computing

club kind of energy. Um we're starting

to see with the emergence of Grockbot

and chat GBT work personal agents being

turned into software that consumers can

use. Anisha actually um do you mind just

pausing on this before we go to the town

demo because you know you you were a

founder um building in the last era of

the the consumer app experience and when

I even think about I was like gosh how

do you even define consumer today

because you know the plumber that

utilizes now Grockbot to completely turn

around their business end to end like is

that consumer or is that enterprise

because like it's very like it's almost

like a like PLG

>> movement but but it's coming from as a

consumer consumer that then cross over

into enterprise and and particularly

like the last era of consumer

application is more towards

entertainment as a way to to monetize

and so maybe unpack some of that and and

particularly where you've been spending

time as a part of that.

>> I mean our simple rule is if you cannot

justify acquiring the customer through

sales which usually means a 15k ACV you

have to acquire them through marketing

we think of them as a consumer which is

most small business owners. So I think

that the plumber is definitely the

consumer in our sort of investing mind.

Entertainment is huge and there's going

to be a bunch of AI native entertainment

companies. You know I would argue

character was kind of an entertainment

company. There's been a huge trend

around short form drama mostly in Asia

and that's starting to come over here.

Many of those are generative or sort of

generative assisted. So I look I think

entertainment is going to be massive.

Most people want to spend time not save

time and consumer is not that interested

in productivity. So that's definitely

going to happen. Um and probably worth a

separate deep dive. Okay. And I think

town for folks who have used it, it's

it's just such a magical experience. And

you know this is like the the number one

sort of um piece of advice I give to

everybody, friends, family, uh folks in

the industry is like please just use the

products because it's so easy to build

intuition when you see how they change

day-to-day. And town is an investment um

our partner Alex Rampel made. It's

really extraordinary uh productivity

product and you sort of see how the

compounding um improvement of the

product through memory advantages it

over time. So the first day you use a

product it doesn't know you that well.

It's sort of like an employee a new hire

who's just getting up to speed. By day

30 it's able to make excellent

assumptions on your behalf because it

just has soaked in 30 days of sort of

context, memory and skills. And this is

a pattern that we're seeing more and

more. The sort of compounding value

being delivered to the end customer

showing up as retention in the business

and sort of showing up as pricing power

on a per customer basis.

>> Yeah, this is a great one because uh

folks can utilize town for their

personal use case. Uh and it's a free,

you know, trial. They give you I think

something like 40 uh credits to start or

something around there. Um and so you

can kind of see it once you plug into

your personal email how productive it

actually is. Um, on the professional

front, I'm always inbox zero. On the

personal front,

>> my inbox is like 20,000. Uh, David

George is is probably cringing on the

inside here just because it's

unacceptable. However, uh, you know,

personal life things are common. So, if

you email me in my personal, I will

never respond to you. However, I plug

into it and like I don't even check

anymore. If there's something important,

town will surface it to me. And also, it

does all the scrubbing of like

subscriptions and all the things that it

can optimize and it's starting to now

self-improve upon itself. So like it'll

send you emails where it says like hey

this routine is costing this much like

here's how you could actually save your

credit swe. So it's sort of this this

unlock into what starts on the

productivity side and to your point

maybe people won't pay for that

personally but once it starts to get

locked in and then expand in terms of

the remit you're like okay I'll pay the

whatever x bucks you know just because

it helps to manage my life and I can put

it on autopilot.

Yeah, it's such a great point, Shannon.

Like my mental model for this is just an

experienced employee, a tenure employee

versus a new hire. You know, the new

hireer may be brilliant and may even

cost less than tenure employee, but we

all know the value of a tenure employee.

They're just able to make great

assumptions on behalf of the

organization and you. And you know, may

this is a little philosophical, but I

think this is where it all goes. Just as

we talked about kind of coding loops and

business loops for the enterprise, we

think there's a set of loops that are

informally defined that really um sort

of lay out a consumer's life. think of

um family, friendships, money, health.

These are all areas where you have sort

of changing information, decisions,

agency, execution, and then the loop

continues. So, we're starting to see

some of these sort of loops emerge

around self-improvement, kind of health

and finance are the two areas that

OpenAI is focused on. We've seen a bunch

of startups working on shopping, but we

think that like the kind of way that

this ends up playing out is a dramatic

quality of life improvement for the

consumer and um and that really follows

the shape of past product cycles where

80% of the surplus is delivered to the

mass market.

>> Do you think each that all of these uh

sorry maybe just going back to the to

the last slide there's a question here.

you know, when you think about these

personal agent examples, whether it be

town or ethos, etc., all point to, you

know, kind of one assistant having

context, but it seems like there's many

different options. Do you think it'll

end up being, you know, sort of one

dominant platform for this personal

aspect of your life as as time

management? Um, or will it be like an

operating system where you have many

kind of talking to each other and kind

of configuring on the back end?

>> I the comparative vantage point kind of

comes to mind. you know, I think the the

characteristics you want from your CFA

are different from the one that you want

from your sort of party planner. Um, and

that just the surface area is so broad

that I think that yes, there's

overlapping bits of context. And I think

Grock Bots has done a nice job of kind

of illustrating this in product or you

have many bots that are pointed in

slightly different directions that all

coordinate to deliver a globally optimum

uh optimal outcome.

>> There's a few questions. I'm going to go

back to topics you've covered earlier.

So if the application layer captures

economic outcomes, how do you think

about the competition from the model

companies um and what will they allow

value accretion to happen downstream and

and you know are companies at the app

layer able to compete with the frontier

labs going after that particular market?

>> I mean I I think so again I think that

we're we're underestimating the kind of

complexity of product pricing packaging

and how the end customer wants to buy.

you know, the way that um you know, a

teenager wants to consume the

intelligence primitive is different than

the way an marketing executive at credit

union wants to actually consume it. Um

and it's very heterogeneous. So to me,

it just makes less sense for um the labs

to move up to the apps layer than to

move down to inference. So, you know,

and that kind of permission point's an

interesting one. I think if we lived in

a world of 2023 when it was one model to

rule them all, it wouldn't even matter

if you had permission because the labs

would just take 100% of your gross

margin over time. But now because you've

got many options at all points in the

paro frontier, you know, the labs have a

harder time actually doing things like

that.

>> Awesome. There was a question just on

traction. So, do you fund anything where

there's um there there's no revenue at

this point just given how quickly people

have been making progress or is it

extremely difficult? um we try not to I

I certainly have spent less time um on

that strategy. Look, I I think that the

basket is majority investments that are

showing some signs of working. Certainly

from a product velocity perspective,

that used to be something we measure

pretty carefully. Like it's

disqualifying to not be showing a live

product in a pitch at any stage these

days because it's so trivial to build

stuff. So almost everything we we're

seeing are showing signs of you know

sort of some sort of breakout. I mean my

model is somewhat simplistic where I

just sort of look at once you have stats

sig sales and product if we extrapolate

from there um do we kind of like the

price that we have to pay to be a part

of it and the risks that we're taking

implicitly and that's I'd say the

majority of the work that we do look for

very talented experienced folks we do

kind of take a small call option which

looks like a pre- everything round um

but that's not the majority of what we

do.

>> Yeah. Yeah. Well, when you think about

the the um kind of competitive landscape

on this uh consumer has been unloved for

so long um are you seeing now this

reversion just given it's clear that

apps is sort of this next layer of value

creation like the model sort of layer

has been somewhat set and I say that

with a huge aster because there might be

new algorithmic breakthroughs you know

kind of kind of folks coming out from

left field as as we have in the

portfolio as well um but do you feel

like the shift from the competitive

dynamic shifting more towards

application

>> 100% I It's sort of a renaissance for

being a consumer builder because you've

got this extraordinary primitive that

you can work with. By the way, we now

have a primitive that can kind of

operate in the, you know, emotional

interpersonal domain. You know, you can

like have a conversation with claude or

openai or K3 and and feel feelings. And

we've had 40 years of technology that

really boosted our intellect and

productivity, but nothing that kind of

spoke to our humanity. So, it's a whole

different technology surface. It's very

wide. I think there are a set of

products that labs are just culturally

not set up and big tech not set up to go

after. You think about launching, you

know, a companion product at Google that

may disagree with you that may um have

sexual innuendo in it. Like these are

things that there's a thousand

committees at Google um are designed to

prevent. So startups have areas where

they're kind of uniquely capable and

then look finally the consumer sort of

excited to download new software,

excited to pay for it. It's like

Christmas 2009 with the iPhone. People

want to try try new apps, but unlike the

99 cents days, they're willing to pay

200 a month. So, it's sort of a

renaissance for consumer builders and

and yeah, I think that things have

changed.

>> Trying to come up with a joke that the

autist in San Francisco are keenly

keenly waiting for this moment for a

very a very long time. Uh

there's a there's a good um a question

from Michelle here. You know, how should

we think about the new economics of AI

apps companies because there's a great

there's a debate around unit economics

of of um apps companies, right? like for

example, they may may have lower gross

margins. They're just getting more

pressure just because they don't have as

much compute access. Um capital is such

a moat in this environment. Um it's it's

hard to be competitive. So how do you

think about the economics of of

underwriting returns in in um in

companies today?

>> I mean David wrote a great post on this.

I think that the kind of the margin

topic is a lot more nuanced than it once

was. I think it's actually rational in

many cases to trade away margin to have

wider product surface. I think the very

positive part of what's happening in

this product cycle is the willingness to

pay is extraordinary. And that's why the

exercise that we often do with founders

is like on the consumer side, for

example, is if $20 was the historic

ceiling, what's the $200 a month skew of

your product? And in fact, what's the

$2,000 a month skew? Like what's the

Birkin bag of software? I think we're

going to have this luxury software.

We're already seeing willingness to pay

for it. So the margin topic is more

nuanced, but the willingness to pay and

buy is higher than ever. So, you know,

it's a little bit of fog of war, but

we're we're thinking about all those

topics.

>> Anish dropping Birkin bag framed jeans.

Like, I had no idea you were such a

fashion. This is like your your butt is

helping you get up to to seat here, my

friend. Uh for a guy secret [laughter]

is a good steward of capital. Okay,

that's all that I am.

>> For for a guy I only see in quarter zip

ups. I'm just saying. Uh

>> um Okay, maybe one question for you on

just on the on the founders because I I

don't know if you remember this

conversation. This is probably 5 years

ago or so. Um where most of the founders

you saw saw more uh diversity in their

background in part because the software

and technology was way more

sophisticated. So you had a lot of

program managers spinning out of Google

for example and starting a company etc.

What are the type of founders you see

building an apps today? Are they do they

tend to lean you know more technical

more researcher derivatives? Are they

product managers? like what what kind of

archetype are you seeing at least the

early innings of apps come out from the

woodwork on?

>> Yeah. Yeah. Less MBAs, more researchers.

Um and they both have their kind of

strengths and weaknesses. I think the

business sophistication of the founders

are seeing today is lower, but the kind

of technical sophistication is

dramatically higher and the technical

sophistication is kind of upstream of

all the good things that happened. You

know, business sophistication can be

kind of taught and observed, but

technical sophistication typically not.

So, we're definitely seeing a much more

technical kind of earlier career

founder, but the things they're doing

are extraordinary because they don't

have any sort of preconceived notions

about what's possible. And so much of

what holds back senior founders that

don't quite get to the other side of

this product cycle is, you know, they're

not close enough to the technology and

they've got an idea that's rooted in the

past of what the ceiling is. And I think

the best thing about these young

founders is they assume everything is

possible. You know, we are at an offsite

where Ben was saying that the the

biggest risk in the past with the ideas

were too big and now the biggest risk is

that the ideas are too small. But I

think that's sort of illustrative of the

different founder archetypes.

>> Yep. Yep. And maybe on that similar

thread, it used to be that that if you

gave a founder too much money, it would

wreck the company because the founder

almost always has way too many ideas and

is a visionary and doesn't have the

talent to actually commensurately

land with all those ideas. Um and we're

seeing a whole new paradigm on that.

Maybe unpack that idea a little bit more

just uh because it was such a huge theme

of the offsite.

>> Yeah, I mean for sure this was a

historic wisdom. You know, why didn't we

give every seed company 20 or 50 or

hundred million dollars? You know, it

wasn't just the kind of riskreward, but

rather typically the constraining factor

was they just didn't have enough

talented people to work across $20

million of product surface at the same

time. They really had to focus on one

idea at a time and the capital was a

great way to enforce that focus. What

we're now seeing is you could make

different sort of product and model

trade-offs through more or less capital.

And there is a case for a company that

raises a hund00 million, uses it

productively and in a focused way and is

able to deliver a different value

proposition um than the very same team

would be able to do with 20. So I think

that that again like the sort of just as

we talked about sort of fog of war

around margins, I think this question of

what is the optimal seated around size

and how much capital can you put to work

effectively is a much more nuanced

topic. I mean, it's sort of this

embarrassment of riches, but I'd rather

have this problem than the problem we

had five years ago, which is, hey, my

fintech company is indirectly

subsidizing their customers through weak

underwriting, and we don't know the path

home.

>> Yeah. Yep. Yeah. The Chris Chris Dixon

model, which is you always want the

problem of supply, not of demand. Right.

Right now, we have to fix the supply

part, right? Uh but the demand is like

uh so so abundantly there that that uh

undoubtedly that will um the supply part

will will get fixed. Um maybe I'll close

on this one last uh question for Mosfa.

So um double clicking on theme sector

adoption of AI. So unlike large

enterprise the friction of adoption is

much less because they require less

change management. I agree with many of

that but uh not not all uh small small

uh medium businesses sometimes have uh

more habit change that you got to work

through. But the question is how do you

see the gotom market playbook for

startups targetingmemes and has that

changed in the age of AI? I mean a lot

of it for existing thememes I think it's

the same channels with which you

historically reach them. I actually

think that one of the interesting things

about marketing in the age of AI is that

all of the sort of existing networks

have been so trained on the methodology

of building new networks that they're

very careful to ensure no one does it on

their network. So Instagram, Tik Tok X,

it's very hard to build a new sort of

distribution channel off the backs of an

existing one. So what founders have to

do is actually build a product that has

the original network effect which is

word of mouth. So we're definitely

seeing more of a focus on word of mouth.

Yes, the kind of old channels for

reaching are still there. Actually think

the most interesting segment of the

market though is sort of new business

formation which is by the way at an

all-time high. I think it's the highest

it's been outside of a peak sort of

moment during COVID. These are people

who would have never otherwise been.

It's not the sort of 55year-old plumber.

It's a 25year-old who previously would

have been a YouTube creator and now is

building SAS for their, you know,

neighborhood or their city or their high

school or whatever else it is.

>> Yep. Yep. Awesome.

Well, thank you so much for listen. It's

always great to have you on. Uh now, I

know you're a fashionista and we're

going to be clipping that endlessly uh

on the socials. Um but uh but thank you

for that. And if folks have any

questions, you know where to find Anish.

Um and uh we'll follow up here for some

of the questions we weren't able to get

to as well.