---
author: Latent Space
date: '2026-11-07'
guest: ''
layout: post.njk
source: https://www.youtube.com/watch?v=P735f1xlpL0
speaker: Latent Space
tags:
- ipo-analysis
- ai-security
- financial-efficiency
- public-policy
title: 探讨 Anthropic IPO 财务表现、AI 安全漏洞及儿童账户政策
summary: 嘉宾 Jamin Ball 分析 Anthropic 的 S-1 文件，指出其营收增长远超算力支出，暗示推理效率提升；讨论 AI 发现安全漏洞对国家级行为体的影响；并提及一项为新生儿设立投资账户的立法，强调需认领账户。
insight: ''
draft: true
series: ''
category: tech-trends
area: tech-engineering
project: []
people:
- Jamin Ball
companies_orgs:
- Anthropic
- OpenAI
- Altimeter
products_models:
- S-1
media_books:
- Clouded Judgement
status: evergreen
---
### 财务表现与算力效率
好吧？因为事实是……OpenAI，他们开发出了强大得多的模型，只是目前还没有发布。如果对比一年前与今天的算力美元支出，他们用于模型推理服务的算力增长了 2.3 倍。而同一时期他们的营收增长了 14 倍。因此，这其中基本上蕴含着推理服务效率提升了 7 倍——我的意思是，我觉得这是一家几乎是意外实现盈利的企业。对吧？整份 S-1 表格中，我最喜欢的数据或许是迄今为止筹集了 1410 亿美元的资金。这包括股权、可转债以及 SAFE（未来股权简单协议），并且如今拥有 1340 亿美元的现金余额。眼下，你想要去为 GPU 筹集数十亿美元的债务？没有人接盘。毫无买盘。他们感觉债务市场已经关闭了。你知道，那些大型债务交易并没有发生。
Original English
Okay? Because the reality is is open and profit, they have far more powerful models that they've developed that they just haven't released yet. If you compare the dollar spend in compute a year ago versus today, their compute that went towards serving models has gone up by 2.3x. Their revenue over the same period of time has gone up 14x. So embedded in that is basically a 7x [music] efficiency gain in serving inference which I mean this is a business that I think is almost accidentally profitable. Right? Maybe my my favorite stat of the whole S1 was $141 billion of capital raised to date. That's inclusive of equity converts and safes and a $134 billion cash balance today. right now. You want to go raise multi-billion dollar debt for GPUs? No bid. No bid. They're like debt markets are closed. They're, you know, big debt deals that aren't happening.
Original English
Okay. We're here in the studio with Jam Ball from Ultimator. Welcome.
Original English
>> Thanks for having me. Excited to be here.
Original English
>> Uh, Anthropic is IPOing. I was thinking of who do I know would be the perfect guest to cover the S1. You're the guy.
Original English
the appreciate it.
Original English
>> You're the guy. Uh, we met before I think you started your Substack. I maybe was just starting mine. How old is it?
Original English
>> Uh, six and a half years now.
Original English
>> Okay. Actually, no, you're older. Uh, so Cloud of Judgment is like one of the top like what what are the brag statistics about cloud judgments?
Original English
>> Oo. Uh, brag statistics. Um,
Original English
>> probably getting close to 100,000 subscribers, you know, every week. I I think in six and a half years I've I've missed one Friday. uh try to be pretty consistent
Original English
>> and the the the general like my takeaway is like here's like public markets comps for the startup investing that you do but ultimately
Original English
>> yeah the the idea was hey how can I provide some commentary on metrics around software companies and you know the universe of companies that that typically has public metrics or public companies um as a as a way to kind of not guide but uh provide a blueprint to early stage companies and founders of like hey here are some of the metrics required to be public one day Um, and so there's lots of tables and charts and graphs on public metrics and valuation multiples. And maybe three, four years ago, I I kind of started writing a little ramble up front, which is more of a I kind of treat it as a personal journal, like what's on my mind, what what have I been thinking about, what's what what's top of mind. Um, and you know, that'll range from anything from, you know, some macro commentary to market commentary to, you know, trends in AI or what, you know, whatever's trending at the moment. Yeah, I think every generation there's kind of a VC blogger who's like sort of the guy to cover what's going on. It used to be Fred Wilson if back in the day.
Original English
>> Uh he used to write ABC I think
Original English
>> and then uh more recent like Tomas Tongas. I mean he's still active but like uh I feel like you are the king of like charts and public stocks right now.
Original English
>> No, I appreciate I mean Tomas was a huge inspiration. So I was at Redpoint for for quite some time with Tomas and
Original English
>> you know
Original English
>> and now now he's started theory.
Original English
>> Exactly. And you guys
Original English
>> Exactly.
Original English
>> Red Point. Okay, you guys are all leaving.
Original English
>> No, Redpoint's great. They're they're great. I mean, I owe everything I learned to to those guys and I I have a ton of respect for them.
Original English
>> Yeah. Yeah. Amazing. Uh okay, so we're going to get right into Anthropic and uh you have a a sort of some spiel. You've had some public dustups with people that we won't name.
Original English
>> Um people are just generally very confused because they a lot of them have never a lot of our audience are engineers and researchers.
Original English
>> Uh they've never seen an S1.
Original English
>> They they've never been through an IPO process. is they don't know what a road show is. They don't know how this is done. But more importantly than that, just the that's the mechanics. More importantly than that, what does it mean about our general state of AI, right? Like this is the most visibility we've ever gotten into anthropic.
Original English
>> Yes. Yes.
Original English
>> And like well now we know a lot more things than we used to.
Original English
>> Yeah. Totally. Totally. And just just to say it up front um because I've been asked to say this. So these are I work for Altimter. We have a big position in Anthropic. Everything I say is you know these are all my personal views versus versus uh versus my firm views. Uh but yeah, it's a big deal. I think you know the big labs, OpenAI and Enthropic seems like every quarter their numbers leak. Uh but it's a subset of the numbers. It's not it's not everything. And now we kind of get a full open kimono view of the financial profile of you know one of the most important and iconic companies maybe of our lifetime. And just kind of a a quick process uh process refresher. They'll file an S1 uh probably wait about 15 days. There's usually a minimum until they'll start a road show. the road shows and they actually start and they pitch investors, IPO investors that, you know, travel across the country, across the world to pitch the IPO. That'll usually last a week and a half and then it will start trading publicly. So, we're probably looking at
Original English
>> end November.
Original English
>> Yeah, like mid end of November. Um, you know, kind of 15th to 20th, 10th to 20th of November of of when it actually starts trading publicly.
Original English
>> Yeah. Do people IPO past Thanksgiving? That's like a weird dead zone.
Original English
It's, you know, between Thanksgiving and Christmas is is kind of a dead zone. Um, I would say, you know, post Labor Day to Thanksgiving is generally a kind of like a hotbed uh time for companies to go public. People are around, people are engaged. Um, notation and it's anthrop is I think people would people would make time for work for
Original English
>> Yeah. Exactly. Exactly.
Original English
>> Uh, yeah. So, okay, that that's the normal mechanics. I I think um and and that's it, right? So, so off the top of your head, uh, you know, people should understand that an S1 is just a filing that you have to do. Um, opening has done theirs as well confidentially and then in the legal public.
Original English
>> Um, people have filed S1's and not IPOed. Cerebras famously did that.
Original English
>> That's right.
Original English
>> And then they did it again. Um, and uh, it's just part of the routine process and you have investor day and you have whisper rumors and all these things, right? So,
Original English
>> uh, as someone who is unless you're literally a banker or fund manager, that's all you need to know basically, right? there's not there's no this is part of the process. Um and there's a lot of indication to try to make it a successful IPO. Successful is a mixed or loaded word this these days because um typically you you hear things like IPO is so successful the stock popped 50% on the first day. That's actually potentially a failure if you're um Bill Gurley.
Original English
>> Yeah. Exactly. Exactly. [laughter]
Original English
>> Who was like well you could have got 50% more.
Original English
>> Exactly. And you know, and just just for the audience, what what that mechanism is is in an IPO, it's like a fund raise on steroids. You're going to go out, you're going to raise a bunch of money at a certain price.
Original English
>> How much are they raising?
Original English
>> I don't think I don't think that's been
Original English
okay.
Original English
>> I don't think they've decided. I think the rumors are like 75 to 100 billion. Um, it'll be a big raise. I mean, typically,
Original English
>> what you used to see
Original English
>> price I I know what you used to see in normal IPOs was companies would float about 10 to 15% of their company, which means like what you would sell into the IPO would be 10 to 15%. Yes. And there's an employee lock up and
Original English
>> ex Exactly. Uh now it's it's maybe as as the companies get bigger and bigger and bigger, they might float less of the company because hey, you can take less dilution. You know, it's no different than a company raising a series A, B, or C. It's you're going to raise money at a certain dilution. Um and yeah, the Bill gurly thing is well, if the stock pops 50% on day one, why wouldn't you have just raised money at a higher price, right? And taken on taken on less dilution. And
Original English
>> that's a whole separate topic. Yeah. lots of nuance to public markets and IPO out
Original English
>> there's tradition there's conflicting incentives and then there's there's the chance that you
Original English
actually
you
don't
pop
Original English
>> yes
Original English
>> um
so
all
that
let's
put
it
at
a
high
level
why
now
why
IPO
now
right
they
uh
they
could
do
a
Stripe
they
could
do
a
data
bricks
Original English
>> right
they're
not
they're
IPOing
why
Original English
>> yeah
I
I
mean
I
think
there's
a
couple
reasons
well
one
it's
all
speculation
it's
hard
to
know
but
I
think
there's
a
number
of
reasons
why
companies
have
historically
chosen
to
go
public
um
there's
kind
of
the
obvious
let's
provide
liquidity
to
our
employees
and
our
investors
in
like
an
unrestricted
way.
Um
because
of
course,
you
know,
now
big
companies,
data
brick,
stripe,
open
eye,
anthropic
kind
of
like
tenders
are
are
pretty
regular.
Uh
Original English
>> once
a
year
you
you'll
get
like
well
if
you
want
to
cash
out
here's
here.
Original English
>> Yeah.
But
you
know
you'll
have
you'll
be
limited
in
how
much
you
can
sell.
Um
you
know
now
once
the
company's
gone
public
once
they've
gone
through
the
lockup
you
know
which
is
anywhere
from
you
know
it
used
to
be
just
90
180
days
6
months
and
now
it's
kind
of
like
every
company
has
a
bespoke
lockup
that's
like
dribbled
out
some
are
shorter
some
are
longer
but
it's
kind
of
like
unrestricted
access
and
you
can
generate
liquidity
for
your
employees
um
help
morale
whatever
or
um
you
need
a
liquid
currency
for
acquisitions
and
hey
a
company
might
be
more
willing
to
take
an
acquisition
from
a
public
company
because
the
currency
you're
getting
back
is
is
liquid
versus
in
a
private
company.
Maybe
it
isn't.
You
don't
really
know
the
value.
Yeah,
it's
monopoly
money.
Um
I
would
say
these
quasi
public
companies,
you
know,
OpenAI,
Anthropic,
Data
Bricks,
I
think
their
currency
has
almost
started
to
be
viewed
as
cash,
not
monopoly
money,
because
they're
very
high
quality.
They're
very
late
stage.
Um
that
effect
probably
means
less.
I
would
say
a
a
bigger
reason
to
go
public
um
because
and
it
used
to
be
companies
went
public
because
now
you
could
access
much
larger
quantum
of
capital.
You
know,
you
if
you
wanted
to
raise
$100
million,
you
know,
it
used
to
be
the
only
way
to
raise
$100
million
is
to
to
go
public.
You
know,
now
you
you
seize
and
raise
a
series
A
or
a
seed
a
seed
of
of
more
than
that.
Um
and
so
it's
not
necessarily
the
quantum
of
capital.
I
think
whether
it's
US
institutions
getting
bigger
um
maybe
folks
who
used
to
act
as
LPs,
large
pension
funds
and
endowments
acting
as
direct
investors
in
a
company
themselves
or
kind
of
the
emergence
of
big
sovereign
international
funds
investing
direct
[clears throat]
in
companies.
There's
a
huge
capital
base
available
to
private
companies
Original English
and
so
like
I
don't
know if
the
argument
necessarily
holds
hey
you
if
you
want
to
raise
a
lot
of
money
um
go
public.
I
think
Openai
raised
a
round
of
like
120
130
billion
you
know
some
biggest
of
all
time.
Yeah.
Exactly.
Um
I
think
maybe
a
more
important
angle
is
is
more
of
like
the
debt
piece.
Um
and
so
once
you
go
Original English
>> talk
about
it
Original English
once
you
go
public
um
actually
let
me
take
a
step
back.
If
you
want
to
raise
debt,
um,
or
if
you
want
someone
else
to
raise
debt,
not
on
your
behalf,
but
like
as
a
Neocloud,
a
Neoclad
might
want
to
raise
debt
to
build
a
data
center
for
Enthropic
to
to
use.
Debt
providers
are
going
to
look
at
Enthropic
and
they're
going
to
underwrite
Anthropic's
creditworthiness.
And
how
it
works
is
they'll
go
they'll
say,
"Hey,
we're
going to
syndicate
however
many
billions
of
dollars
of
debt
for
you
anthropic
or
via
a
neocloud
for
you,
but
we're
going to
need
we're
going
to
need
like
full
open
kimono
on
your
financials.
We're
going
to
need
to
know
exactly
how
this
business
is
doing
uh
so
that
we
can
underwrite
you.
And
then
everyone
we
syndicate
the
debt
to
on
the
back
end,
they
also
need
to
see
it."
And
I'd
say
historically
companies
have
just
been
hesitant
to
go
full
open
kimono
with
their
financials
to
the
debt
and
credit
markets
because
they
don't
want
them
to
be
fully
public.
Um,
but
if
you
want
to
be
rated
by
the
by
the
rating
agencies
who
basically
they'll
give
you
a
credit
rating,
um,
investment
grade
is
the
highest,
your
credit
rating
will
kind
of
impact
the
rate
with
which
you
can
raise
debt
at.
And
the
higher
your
credit
rating,
the
lower
the
rate
that
you'll
receive.
But
you,
you
know,
have
to
be
open
kimono
with
your
financials.
So
going
public
um
is
a
great
way
for
companies
to
basically
you're
going
to
get
raided.
I
think
SpaceX
very
famously
got
raided
very
quickly
after
going
public.
Now
you
can
tap
into
Yeah.
Original English
arguably
too
I
mean
there
there
was
a
lot
of
people
who
said
it
was
hey
it
was
too
fast
they
need
to
season
more
as
a
public
company.
Um
Original English
>> and
and
added
to
the
S&P
500
something
like
that
new
rules.
Original English
Exactly.
And
so
I
I
think
hey
a
lot
of
these
companies
they
will
be
capital
intensive
whether
it's
for
training
or
building
out
compute
clusters
and
you
want
to
tap
into
the
credit
markets
and
I
think
if
you
want
to
tap
into
the
credit
and
debt
markets
you
can
do
that
much
more
efficiently
as
a
public
company
and
and
so
again
it's
probably
a
confluence
of
all
those
things
added
up
to
hey
why
why
go
public.
I
think
there's
also
just
if
you
ask
if
you
ask
these
executive
teams
and
management
teams,
hey,
they
want
the
everyday
American
to
be
able
to
participate
in
the
equity
upside
of
these
companies.
Original English
>> I
was
going
to
bring
that
up.
Yeah.
Up
to
including
giving
shares
to
everyone.
Original English
>> Totally.
Totally.
And
hey,
the
reality
is
is
private
markets
are
more
of
like
a
cabal.
It's
a
small
subset
of
the
population
um
either
qualified
purchasers
or
accredited
investors
or
or
you
know,
big
funds
and
pensions
and
endowments
get
to
participate
in
the
equity
appreciation
of
these
businesses.
But
you
know
your
doctors
and
dentists
and
your
everyday
American
doesn't
and
there
is
just
something
that
doesn't
doesn't
feel
right
about
that
right
like
Original English
>> straight
up
you'll
get
social
unrest
right
and
straight
up
you'll
get
you
get
voted
regulation
because
like
well
those
guys
have
their
AI
and
Amazon
vote
against
it
I
have
my
right
that's
right
that's
right
Original English
>> and
so
like
that
is
actually
really
an
interesting
governing
mechanism
of
democracy
versus
capitalism
where
like
capitalism
encourages
centralization
and
democracy
encourages
um
decentralization
somewhat
Um
anyway,
so
there
there's
all
that
I
think
it's
very
very
valid.
Uh
I'll
point
out
for
people
who
don't
know
about
capital
structure
that
uh
equity
is
is
junior
to
debt
and
so
it's
nice
to
have
two
trillion
in
junior
Original English
>> Yeah.
Exactly.
Exactly.
Original English
>> in
your
enterprise
value.
Right.
Exactly.
Uh
and
that's
like
well
that's
that's
uh
that's
public.
Um
and
I
think
part
of
that
also
I'll
I'll
throw
in
the
why
now
which
is
a
little
bit
more
advanced
than
than
people
know
is
um
interest
rates
are
spiking
for
for
um
Original English
>> uh
for
data
center
debts.
uh
globally,
right?
Like
is
there
a
source
to
look
that
kind
of
stuff
up?
Is
it
just
Bloomberg
or
is
Original English
>> Yeah,
I
I
I
mean
I
Original English
>> just
charts
that
people
tweet
around,
but
Original English
>> most
of
these
big
debt
deals
will
price
um
and
there'll
be,
you
know,
like
an
interest
rate,
an
o,
you
know,
there'll
be
a
bunch
of
things
associated
like
with
the
price
and
like
often
times
those
will
be
public
once
they've
priced.
Um
but
yeah,
rates
are
going
up.
Um
risk-free
rates
are
going
up.
the
Fed
paused
you
decided
not
to
hike
yesterday
or
earlier
this
week.
Um
I
don't
know
if
that's
necessarily
a
reason
to
go
public.
Um
but
it
Original English
>> it's
part
of
the
why
now
if
if
it
does
help
with
debt
that
if
my
debt
went
from
like
I
don't
know
uh
3%
to
5%
that
is
billions
of
dollars.
Original English
>> Um
and
if
I
can
keep
a
lid
on
that
that's
very
important
to
me.
Original English
>> Yeah.
I
mean
a
lot
of
the
data
center
debt
deals
now
are
are
getting
done
over
10%.
Um
and
so
it's
it's
getting
it's
getting
expensive.
Original English
>> Okay.
Original English
>> Yeah.
And
so
so
and
then
the
other
that's
the
other
part
of
this
discussion
which
is
um
Original English
>> uh
the
ROI
on
a
data
center,
Original English
>> right?
Because
that
there
there
are
a
lot
of
Original English
>> AI
skeptics
Original English
>> who
are
like
this
is
a
the
biggest
buildout
in
human
history
Original English
>> and
we
are
throwing
money
after
this
illusory
dream
of
AI.
Then
on
the
other
side
the
AI
pro
people
are
saying
well
your
payback
period
is
one
year.
Original English
>> Yeah.
Yeah.
Why
is
there
such
a
huge
difference?
Original English
>> This
was
kind
of
the
best
thing
about
the
anthropic
S1,
which
is
a
lot
of
the
metrics
that
were
leaking
were
your
Original English
your ARR, you're this or you're that, but you didn't really get to see a lot of below the line. Um, you didn't get to see the cash, how much are they burning? What's are they profitable? Are they not? And like that I think was one of the biggest surprises of the S1, right? Here's a business that showed 85% gross margins, over 20% free cash flow margins, um 8% gap operating margins, and so even on a gap basis, um highly profitable. And maybe my my favorite stat of the whole S1 was
Original English
>> $141 billion of capital raised to date. That's inclusive of equity converts and safes and a $134 billion cash balance today. And so
Original English
>> cash and cash equivalents, but yes,
Original English
>> exactly. And so you think, okay, well, you've you've raised this much. You have this much in the bank. That means you've burned $7 billion to get to, you know, the Q3 numbers that aren't in the S1, but that were kind of like broadly reported a few weeks ago. $16.8 8 billion of quarterly revenue off of $7 billion of lifetime burn,
Original English
>> which and the burn we we should we should get into cuz like that was a a huge debate on like well have well it looks like they did raise debt or are they really profitable? What is this net loss on the P&L? There's I'd say lots of
Original English
>> lots of things out in the world that aren't fully true.
Original English
>> That is important. I do want to cover it. that's that there's a little bit more in the weeds than than than at the start of this conversation. But like that overall efficiency is by far uh if you've been following the relative fates of let's call it SpaceX uh anthropic openi the the ones that are going public um they are the most efficient lab in the world
Original English
>> by by like order of magnitude.
Original English
>> Yeah totally. Um, and you know, so I I when when I wrote up the S1, I was like, "Okay, well, this is the the the the mystery, like why what's going on?" Like,
Original English
>> how are they so much more efficient?
Original English
>> And uh I they in their S1, they attributed to the loops um the improvement loop, the investment loop, something like that.
Original English
>> Exactly.
Original English
>> Um so people can go read that up. I I already wrote it up. Um but like, you know, maybe they're just smarter.
Original English
>> Yeah. [laughter] Well, I I think here like one of the best numbers to look at is like what is their growth relative to their what is the the change in growth relative to the change in serving compute spend and they do they break out their compute spend into more like training and um internal use
Original English
>> and by the way you have charts to
Original English
>> yeah let's play we do have I do have a chart on that um so here is here is a chart that shows the
Original English
>> let's do full screen on this one
Original English
>> yeah yeah
Original English
>> just just so yeah it's Perfect. It's a little small.
Original English
>> Yeah, exactly. All right. Here's a chart and this is broken down in their S1 that shows the percent of their compute that goes towards basically serving models. So, inference revenue generating or internal R&D kind of training or maybe safety and alignment or you know non serving of uh of tokens. And what's interesting is you can see now I mean they're at 27% only 27% of their compute today is going towards serving models. Um here's why this is so interesting. If you compare the dollar spend in compute a year ago versus today, their compute that went towards serving models has gone up by 2.3x. Their revenue over the same period of time has gone up 14x.
Original English
>> So embedded in that is basically a 7x efficiency gain in serving inference which I mean this is a business that I think is almost accidentally profitable, right? where I mean a 7x increase in efficiency of serving tokens. If you were if you were monetizing, you know, a megawatt at 10 $15 million, now you're monetizing it at $70 to $100 million. And here's this is a little bit of like a a random rant. Um, but software companies have historically told the world and told investors, "Hey, we're an annuity of cash flows. Once we acquire a customer, our net retention is high and they they'll stick with
Original English
>> customer lifetime value."
Original English
>> Exa Exactly. and we're going to be burning a lot of money early on. Um, but we're just in growth mode. We're in customer acquisition mode. Once we exit growth mode, we'll be more in harvest mode. And that's when we'll start to become we'll flip profitable um because we're not spending a lot of money on on um on customer acquisition costs. But, you know, the the general model of a SAS company was unprofitable over time then profitable. What's happened with Anthropic? And let me pull up another chart here. Um, I believe I have their ah here are their free cash flow margins.
Original English
>> Oh my god, it's so negative.
Original English
>> Which [laughter] I had to cut the chart, right? Like like negative 4,000% negative500%, you know, super super negative. If if I actually drew the axis to scale, it it would it wouldn't look real. Um, and so yes, the the axis is broken. Um, but this is the free cash flow margin of anthropic, right? 21% free cash flow margin in the most recent quarter. And so what you're seeing is a business that all of a sudden because they were able to drive such efficiency in their compute, that's kind why I went on that like compute ramble. Um they've been able to monetize at much higher rates and actually deliver positive free cash flow margins pretty early on. And so this is a business that says, "Hey, we've proven that we can operate highly profitably. What we've probably done is underinvested in compute in compute buildout and we need to be more aggressive on it." And so my guess is that you're actually going to see this level off and maybe go negative again as they say, "Hey, we're going to reinvest. Um, and we're going to invest in a lot of compute. We're going to build out a lot of compute. Um, because now they've shown themselves and the worldap. We can we can flip the switch. Um, and and be profitable." Um,
Original English
>> and for listeners, uh, a couple things on the accounting side, just because I we got it like educate people, right? Um, FCF is typically like the most honest version of what profit looks like. Um but uh in this case you can mostly just regard it as what an operating margin would profit margin would look like because basically FCF just focuses on cash and so there's less tricks to play with acred receivables and li and you know offsetting of liabilities and what have you. Um and so like that that is the roughly rough thing and but also this the re the reason people have adjusted revenue and adjusted profit is because of nuances in the running the business like this right like GAP which is the official generally accounted accepted accounting principles that you have to report um doesn't recard how you want to run the business and split out investment uh it does have investment decisions versus like sort of profitability of of core business decision decisions but um it all gets smushed into this decisions right it's like so basically what what I'm trying to sort of guide people towards if you do see a change in this margin profile it is a conscious choice and actually you should probably split it out uh further uh between what is actually the core business and you know is that actually inflecting or is this is this more investment therefore SCF went down
Original English
>> yeah and look I mean at the end of the day cash is king um and like your measure of profitability should be is your cash balance going up or is your cash balance going down. Now, if you wanted to poke holes at this, um the the reality of gap accounting is there's two forms of well, very broadly, there's two forms of of expense. There's the cash and the salary that you pay people and then there's also the equity and then the non-cash that you pay people. So, non-cash uh stockbased compensation, equity compensation that doesn't show up in free cash flow. It's actually added back uh because it's a non-cash expense. So, there are a lot of people who look at the free cash flow metric and say, "Well, it actually overstates your profitability because the reality is is if you didn't grant equity to your hires, they wouldn't join you." And so, like, it is a real expense. Treat it as a real expense. I don't have a chart for this. Um, but if you were to adjust free cash flow for stockbased comp, you're still at an 8% free cash flow excluding stockbased comp. And
Original English
so, you're you're even profitable >> if you include the big by normal rules.
Original English
Yes.
Original English
>> Yeah. Exactly.
Original English
>> And so, like if you've never seen a software IPO, this never happens. Yeah, it's very rare. [laughter] I mean, I think there there are a lot of software companies that are free cash flow positive today, but if you factor in
Original English
>> not at IPOP um but even today, the mature ones, even if you factor in stockbased comp, most of them are free cash flow neutral or negative if you treat stockbased comp as a cash expense.
Original English
>> So, all those companies are dying and everyone's just in buying in topic, right? As a
Original English
>> I think there's Well, you're not quite that. Um but but certainly,
Original English
>> why would you like anything else? Yeah.
Original English
I mean, look, here's a crazy stat. Um, in the S1 is like a Brett versus depth one. Um, they called out 50% of the Fortune 500 is using Anthropic.
Original English
>> Yeah.
Original English
>> However, if you look at what that cohort, the Fortune 500 is spending on Anthropic as a percent of their expenses, it's.1%.
Original English
>> And so, you ask yourself, where's that headed? I think John Collison on he had Sam Alman on his podcast the other day. I think he mentioned 1% of Stripe's expenses are on tokens today, but he expects that to go to 15 to 20% over the next few years. And so obviously Stripe is a very forward-leaning tech company. Your average business in the Fortune 500 isn't going to show that level. Um, but it could show a same order of or same magnitude of growth. So, if Stripe is saying, "We're going to 15 to 20x the spend on tokens relative to other expenses in the org," maybe you see something similar and the Fortune 500 go from 0.1% to 1%. Um, but I do think it's, you know, ultimately where do you think that settles out? How deep can they penetrate their TAM? It's kind of like an important question that, you know, folks should think about. Uh, but to me at least, what it demonstrates is they really haven't, despite, I mean, despite the insane ramp in revenue, you know, growing over a,000% year-over-year in the Q2 quarter, there's probably still a lot of room left um for them to kind of continue to penetrate their TAM.
Original English
>> Yeah. Uh, and, you know, I think the the word is virtual collaborators or, you know, I say agents, but you got to you got to make it a friendly term for people. Um and uh you know I really start to tell people uh you should just start thinking about it in terms of like what your employee spend versus your token spend is
Original English
>> and most people are at let's call it 0.1 or 0 point or like 1 to 5%.
Original English
>> Y
Original English
>> uh the median AI companies at 50%.
Original English
>> And the top AI companies uh you know let's say 90th percentic person is like 10x uh token token to salaries. Yeah.
Original English
>> And so, uh, that is probably the right comp of like, well, what are you spending on humans?
Original English
>> Yeah.
Original English
>> Right.
Original English
>> And like probably 30%. Right.
Original English
>> Like, and well, okay. So,
Original English
>> could be more even. I mean, your humans could be like four, you know, total headcount could even be
Original English
>> depends what kind of business you're running, right? Yeah. Exactly.
Original English
>> And so, like if you're physical business, fine. That's like until we have good robots, then that's not that's not getting happening. But what are you spending on knowledge worker humans? what and knowledge worker humans that includes coders but also just
Original English
>> um admin and ops and what have you and and legal and whatever and all that and some part like that percentage the to ratio of tokens spend to human spend was zero to whatever and now it's going to rise
Original English
>> and how far can it go
Original English
>> totally [laughter] totally can go a lot I mean look I I think there's there's a lot of like hyperbolic statements being made both positive and negative about OpenAI, Anthropic, the big labs. Um, I think it really boils down to three main questions. Can Anthropic stay on the frontier? Is there a business and a durable business to be built at the frontier? And if so, how much is it going to cost? And and then in reality, two and three kind of like relate to each other. Um, can it make enough money to cover all the cost to be at the frontier? And that's that's the one where I think I actually have a couple I think I have one on this. Let me Let me see which one. What did I want to bring up? This one. Yeah, let me pull up. Um, no, let me pull up this one. This is a good one. Um, so this is kind of the second question like how much will it cost to be at the frontier or or or even maybe let's go to like what what can you build a business at the frontier? The other interesting stat that Enthropic had in their S1 was for the 3 months ending September, 80% of their revenue was driven by models that were 6 months old or younger. And so what does that tell you? The vast majority of their revenue is coming from their most recent, most powerful models. And that's something that I kind of expect to continue. I think you know when it comes to frontier tokens down to open weight tokens I think there'll be a massive market for both. Uh I think the trend will continue to be on a as a percent of tokens open weight kind of continues to eat share but as a percent of wallet share in dollars Frontier continues to take the line share. So I expect Frontier tokens to be the most economically valuable to capture the most market share. I expect openw weight and cheap tokens to capture the most market share in terms of total tokens. And I think kind of this stat on 80% which is much bigger than I would have expected just in models six-month old or less kind of demonstrates the value of being on the frontier. Okay, so then let's go next go to the second question which is well what is it going to or the third like what is it going to cost?
Original English
Um and here's a chart I think and there there's I'll have to caveat this chart. Um, but a lot of people look at the Anthropic S1 and say, "Oh my gosh, there's a $856 billion of off the books of like not even off, but like committed deals that they've signed. How are they going to pay for it?" You know, they're at whatever whatever their ARR is, something in like I know 70 75 80, you know, whatever. I think the last number was 65. Um, the Q3 number was 16.8, but whatever 70 plus or minus um how are you going to pay for 855 billion? you know, like have you read the rest of the S19?
Original English
>> Well, what I what I so what I tried to do what I tried to do in this chart is I broke down the the because they broke down their commitments. Um, they didn't do it by year, but
Original English
>> this is normal, by the way, that you would have year by year.
Original English
>> So, I feel like this is unnecessary.
Original English
>> Year yearbyear. This is a little bit of the jam in um best interpretation. They'll give a dollar.
Original English
>> Oh, no. I've seen variations of this.
Original English
>> Yeah. Yeah. Yeah. It's and so I think what you have to ask yourself is in the commitments that they've made and I've tried to annualize the commitments if they said this is a 5-year deal. just made it, you know, by the way, every banker in the world has done this already. Like they did this on on the night of the the S1, right?
Original English
>> So, you have to ask yourselves, can you pay for it? Okay. Well, at a fixed basis and then the the gray bars is maybe kind of like less hard commits, but commits they want to make.
Original English
>> So, the the X SpaceX one is in there, right?
Original English
>> Yeah. Exactly.
Original English
>> That's the one they can walk away from 1990.
Original English
>> Exactly. Um,
Original English
>> will they be able to fund 100 billion a year? And so, it isn't 850 billion a year. It's can they fund a hundred billion a year? Now there's a couple ways they could do that. Well, one, do you expect their growth to continue? Um, which is a question of like will there continue to be a business at the frontier? Um, or you go back to the well only 25% of their compute today is allocated towards um revenue generating or kind of inference serving use cases. if they were to just double that and add zero compute, they would they could in theory double their revenue. And so like you look at the headline number of 855 billion, you're like, "Oh my gosh, that's a huge number. How are you going to do it?" Some of that is very backweighted. Um, but I I I I think when I think about the trajectory of the business, um, the TAM that's been
Original English
penetrated today, I don't think it's far-fetched at all to say, hey, like the business trajectory will more than cover the revenue will more than cover the um, the expense. And I think I have another chart in here. This is another great one. This is the compute margin. So this looks at if you were to take their revenue and subtract all compute re uh uh internal models training
Original English
>> same quarter matched right
Original English
>> exactly exactly so even as what you're seeing is even as they've shifted more of their compute to nonrevenue generating use cases they've still scaled their compute margin to well over 50%. Mhm.
Original English
>> Which what you're So what you're seeing is they're getting hyperefficient at the compute they bring online. The compute they decide to use for revenue generating use cases is becoming it's become 7x more efficient over the last year. I think you have to ask yourself will they continue to ramp compute and capacity? I think the answer is yes. I think if you ask anyone anthropic again this is all speculation they'd probably say we wish we started doing it sooner. We wish we were more.
Original English
>> They're trying to commit to more compute. That's something if you don't talk to any of these guys, you come away with like, oh my god, they're, you know, they um these guys are absolutely crazy and being fiscally irresponsible. Uh but I think if you have any confidence or if you see any
Original English
>> um if you see up close the demand for intelligence that people are snapping up and immediately consuming whatever you give them, then like yes, actually you're trying to get more compute and that that's the game is not to reduce the commitments. It is actually to get more commitments. They would have a trillion if they could.
Original English
>> Yeah. I don't think I've spoken to anyone in the AI industry, certainly not any with the big labs who's who has said, "Yeah, we're, you know, I feel I feel we're good. We got the compute we have." It's it's almost always and and I think a underrated factor on compute demands in the future will be compute that's used for safety, alignment, and verification. Like I think more and more compute is going to go towards those functions. Whether that's on research or just on
Original English
>> verification and alignment that happens at inference time that will be an increased compute demand for all these.
Original English
>> But we're probably not going to break those out, you know, but like it'll it'll be like maybe 5% now and 20% inf I would just tell people like if you think this is bad now, wait till you see the open one.
Original English
>> Uh and then like it's not bad. It's like that's the whole point is like they're actually really trying to get compute from every source they can get and like open buying TPUs like [laughter]
Original English
>> like that was a cold day in hell TPUs right they have their own chip uh initiative Jalapeno which has pretty rave reviews staffed up a compute team to build their own chips obviously the hyperscalers are building their own chips it's a
Original English
>> I mean every
Original English
>> mad dash it's a mad dash
Original English
>> only you know only winners are TSMC and uh Nvidia and ASM ML uh whatever in the supply chain stack.
Original English
>> Totally. And and I think look it's it's getting harder and harder especially in the United States. You have a lot of push back. Um I mean you have all kinds of push back. Data centers are too noisy. Don't want them in my neighborhood. They add a lot of air pollution. Don't want it. It's going to the water. Oh my god. The water.
Original English
>> The water. It's which is it uses too much water which it doesn't. Um it's the electricity. It uses a lot of electricity. It's going to increase my electricity bill if I live in close proximity. or it's just the simple AI is going to kill us and I don't want to enable that which again you have all kinds of push back. It's become hyper hyper political but I think the unfortunate thing is it has become a lot harder. You do see a lot more push back. Um midterms are coming up right who who kind of like takes control in like the House and the Senate like could have a big impact on regulation or lack thereof around data center and intelligence center buildouts. And the good thing is that it's getting harder and harder to build these data centers. Uh behind the meter is harder and harder. And so you do have a natural governor on the buildout and you're we're I think we're a lot less likely to overbuild too quickly. That leads to a supply glut, right? Kind of the the internet bubble analogy of the early 2000s where we built way too much fiber. There wasn't as much demand for the internet. There was a lot of dark fiber, right? Fiber that had no bits and bites traveling over it. We have no dark GPUs today. Um, and so it's it's probably a good thing that there's some rate limiter. It will stop us from entering a huge bubble, but it's also bad because we need, you know, the industry needs more compute. Um, especially as these models get more and more powerful. You know, the security concerns go through the roof. You also need a lot more like the defenders need more compute. The defenders need more capabilities. Oh, yeah. Which, you know, I don't I know, personal belief, I I think we're going to be compute constrained for for far longer than um than the world thinks. Yeah. Um I mean I'd be curious if what the what the markets price the world as thinking and then doing the work on like what do we actually think and have the delta is probably you know could be 3 years could be 30 years.
Original English
>> Yeah.
Original English
>> And and that's a very material thing to figure out.
Original English
>> Yeah. Of course. Of course. I I think one thing to guide people towards is well there's all this political stuff and honestly I would say um Meta does a lot of work and I think increasingly OpenAI does a lot of work in like well here's how we we make sure we're a positive um presence in the communities that we're in right so yes you you do need to
Original English
>> take care of that um and but I think like uh coming back to again research relevance ROI and um what it means about the industry um this profit uh move here. I I I I do wonder sometimes and this is a thing that has come up in our other podcast particularly tying in the research side is how much left is there to improve, right? So, was this kind of like a onetime upgrade of our inference stack which you noted was like something like a 7x improvement in in our inference efficiency because like you can't like
Original English
>> probably like there's no improvements for ever until the end of time in our inference stack like unless we fundamentally changed the chip.
Original English
>> Um we pro like we probably started out pretty unoptimized
Original English
>> and we're now a lot more optimized than we were 2 years ago. Okay. Well, there's a limit to how optimized you can get just like physical like jewels moving around and bits and bites moving around. So, uh that that is my question in terms of like the the how much efficiency there's left to squeeze and maybe
Original English
>> there it might cap out, you know, in terms of like that that year-on-year growth.
Original English
>> Yeah. Well, look, I I think um I I have a chart I'll get to in a second, but I think this is a debate that's existed for quite some time, right? You had, you know, the I don't remember when this was two years ago. uh you know like the Ilia moment of like hey the data and the internet is a fossil fuel. We've we've exhausted it. There's no more data left to train on and like we're hitting a plateau. Yeah, that was years ago. Then we hit inference time reasoning time compute and reasoning models. You hit a new paradigm. Um, but I think maybe embedded in your question is well, is the gap between the frontier and the leading openweight models is that shrinking? Like is is the gap that the frontier
Original English
>> Yeah, I don't mind the gap. It's not something I talked about. It's but I think it's interesting because like what the real way to think about or to answer that question is not the the gap between the best leading openweight models today and the frontier models today because the reality is is open anthropic they have far more powerful models that they've developed that they just haven't released yet and like this is a chart that Mark Chen published uh from open eye actually switching gears from anthropic to open eye
Original English
>> he's been on the pod cooking pod
Original English
>> oh
Original English
nice
we
we
made
some
soup
for
researchersOriginal English
>> amazing
amazing
that's
I
love
that
bod
um
but
you
know
when
they
saw
Navier
Stokes
that
big
math
uh
problem
you
know
Mark
came
out
and
said
hey
here's
a
model
maybe
dark
blue
light
blue
is
harder
to
see
but
like
the
top
bar
is
an
internal
model
we
used
to
solve
that
problem
uh
the
blue
bar
is
Astra
hey
look
at
how
much
more
powerful
the
internal
model
is
to
what's
publicly
out
there
which
on
some
ways
that
like
could
scare
you
a
little
bit
right
like
hey
these
labs
have
even
more
powerful
models
so
if
you
think
about
what's
the
gap
between
openw
weight
and
frontier
today.
I
think
it's
actually
far
wider
than
people
expect
because
there
are
just
far
better
models
at
the
frontier
that
haven't
been
publicly
released
yet.
Either
whether
that's
safety,
testing,
alignment,
um
some
combination
of
everything,
you
know,
unclear,
but
I
do
think
you're
still
seeing
the
frontier
accelerate.
Um
even
if
it
may
appear
that
um
what's
released
is
plate.
I'm
not
even
sure
if
it
it
does
look
like
what's
released
has
been
plateauing.Original English
>> No,
no,
no.
No
one's
saying
it
is
plateauing
today.
It's
it's
it's
we're
all
discussing
could
it
plateau
in
the
future?
Yeah.Original English
>> Right.
Because
that
that
is
a
pretty
important
thing
to
figure
out
for
your
investing.Original English
>> Yeah.Original English
>> U
which
is
uh
part
of
the
the
deal
here.Original English
>> Totally.Original English
>> Um
yeah.
So
so
that
is
a
very
well-rased
point.
I
thought
you
were
going
a
different
area
where
you
were
going
to
argue
about
bitter
lesson
and
you
didn't.
So
kudos
on
that.
Um
and
like
I
think
that's
a
very
very
fair
point
that
I
I
I
think
we're
uh
we've
got
still
every
generation
model
generation
still
shows
quantum
leaves.
This
is
only
in
math
but
open
has
a
pretty
good
track
record
of
generalizing
beyond
just
math
or
or
coding.
Right.
And
so
I
I
I
do
think
that
is
my
point
was
a
little
bit
more
as
the
financial
analyst
hat
on
which
is
you
had
this
one
time
7x
improvement
in
revenue
efficiency
or
whatever.Original English
>> UmOriginal English
>> and
like
it's
not
like
they
raised
prices.
Yeah.
Yeah.Original English
>> Right.
So,
like
where
is
it
coming
from?Original English
>> Oh,
here's
another
good
one.Original English
>> I
mean,
they
didn't
raise
pricesOriginal English
>> here.
Oh,
I
think
I
have.Original English
>> So,
so
you
know
what
I'm
saying,
right?
They
have
100.
They
have
one.Original English
>> Yeah.
It's
actually
opposite.
This
is
an
awesome
chart.
This
shows
the
cost
to
complete.Original English
>> They're
charging
less.Original English
>> It
Yes,
exactly.
The
costOriginal English
>> they're
charging
less
and
making
more
money.Original English
>> The
cost
to
complete
a
task
with
each
model
family.
And
so
what
you
see
is
with
whether
it's
Fable
or
Opus
and
then
you
have
Fable
51
um
and
Opus
55,
you
see
about
a
25%
25
to
40%
reduction
in
cost
to
complete
the
same
task
with
each
successive
model
family.
And
so
it's
kind
of
like
yes,
the
costs
are
going
the
costs
are
going
way
down.
Their
compute
efficiency
is
going
way
up.
And
I
think
a
lot
of
that
just
demonstrates
I
mean
again
it's
going
to
sound
a
little
like
VC
cliche
um
but
like
Jebman's
paradox
like
as
things
get
I
know
I
know
I
knew
I
knew
podcast
on
AI
without
that
coming
up
it
is
this
is
the
appropriate
time
to
bring
it
up.Original English
>> Yeah.
Yes.Original English
>> And
as
as
things
get
you
knowOriginal English
>> you
go
back
to
like
the
early
days
of
the
internet
like
how
many
bytes
were
traveling
across
the
internet
in
2000.
How
many
bytes
are
traveling
across
the
internet
today?
Like
many
many
many many
orders
of
magnitude
more.
And
guess
what?
The
price
to
transfer
bytes
over
the
network
has
like
dropped
like
like
many
I
mean
many
it's
P
and
Q,
right?
Price
has
gone
way
down,
quantity
has
gone
way
up
and
I
think
you're
going
to
see
the
same
thing
uh
with
people
buying
and
serving
models
and
tokens.Original English
>> Yeah.
So
so
I
generally
pro
supported
that.
I
I
do
want
to
remind
people
that
that
is
uh
true
objective
fact.
This
is
not
like
VC
like
this
is
like
actually
how
the
the
businesses
are
running
and
it
is
surprising
to
us.Original English
>> Yeah.Original English
>> That
that
I
get
while
yeah
we
read
about
this
in
theory
but
like
to
actually
see
this
in
financials
is
like
really
ridiculous.Original English
>> You'll
continue
to
see
it.
Think
about
like
Jeb
and
decision
models
you
know
and
like
the
price
point
of
hey
instead
of
you
want
a
a
binary
classifier
uh
you
don't
need
a
language
model
to
do
that.
Just
go
use
a
decision
model.
Super
cheap
efficient.
I
think
you're
going
to
continue
to
see
efficiencies
like
that.
You
know,
I
go
back
to
S3
and
EC2,
AWS's
big
initial
products,
storage
and
compute.
You've
seen,
you
know,
99%
price
reductions
in
both
of
those
services
from
the
day
they
were
launched.
But
like
what's
happened
to
the
EC2
and
S3
business
or
just
the
broader
storage
and
compute,
you
know,
more
CPU
compute,
uh,
it's
it's
gone
up
exponentially.
I
mean,
all
the
hyperscalers
reported
today,
they're
all
over
a
hundred
billion
dollar
run
rate
businesses
today
if
they
were
individual
companies.
Google
Cloud's
grown
over
100%.
It
was
just which
is
crazy.
Azure's
growing
almost
50.
Amazon
reported
today,
we're
recording
on
Thursday,
39
almost
40%.
Like
these
businesses
are
just
accelerating
and
growing
at
it's
crazyOriginal English
>> these
there's
you
should
drop
everything
you're
doing
and
just
go
start
one
of
these
things.Original English
>> Yeah.
Yeah.Original English
>> Uh
yeah,
it's
October
29th
for
those
those
uh
keeping
track.
And
and
yes,
uh
a
fun
personal
anecdote,
I
was
in
my
hedge
fund
when
uh
the
day
they
broke
out
AWS
from
Amazon.
M
no
kidding.Original English
>> And
at
the
time
we
were
not
long
Amazon
because
we
were
worried
about
uh
AWS
margins
and
also
like
oh
is
it
just
startups
and
what
if
like
you
know
we
go
through
uh
a
startup
winter
and
like
all
that.
Yeah.
It
was
so
nonsensical.
So
like
it
it
the
the
funny
thing
about
finance
is
that
you
can
do
all
these
works
and
make
worse
decisions.Original English
>> Yeah.Original English
>> Yeah.
[laughter]Original English
>> Yeah.Original English
>> We
definitely
did
that.
to
totally
totally
I
mean
that
that's
the
thing
like
market
timing
is
impossible
likeOriginal English
>> and
also
fixating
on
the
wrong
thing.
Yeah.
Which
a
lot
of
people
are
having
to
encounter
for
the
first
time.
That's
right.Original English
>> You
haveOriginal English
>> 2,000
data
points
that
were
released
in
this
thing
and
you'll
fixate
on
one
thing
and
they
be
like
that
is
that's
the
everything
you
need
to
know
and
like
well
no
like
you
you
lost
sight
of
the
bigger
picture.Original English
>> Yeah.
I
mean
that's
that's
for
me.
I
mean
again
there's
a
lot
of
things
that
I
believe
that
others
would
disagree
with
me
on
but
I
believe
there's
one
question
that
matters
and
that's
canthropic
stay
at
the
frontier.
Yeah.Original English
>> And
if
I
believe
the
answer
to
that
question
is
everything
they've
done
historically
has
demonstrated
that
I
think
they'll
build
a
greatOriginal English
>> but
then
like
there's
a
right
price
for
that.Original English
>> Of
course.
Of
course.
And
this
is
this
is
not
like
will
it
be
priced
right
or
this
or
that.
That's
more
of
hey
if
you're
able
to
take
a
longer
term
view
on
will
the
be
will
the
business
be
successful
over
time.
I
think
to
me
that's
the
most
important
question.
Now
whether
you
think
the
IPO
price
is
cheap,
expensive,
just
right.
I
mean,
we
don't
even
know
what
that's
going to
be
yet.
Um,
prices
can
change
of
public
companies
for
idiosyncratic
reasons,
reasons
based
on
individual
company
performance,
or
they
could
change
for
macrocorrelated
risk.
Like
the
reality
is
is
if
anthropic
went
public
um
a
month
ago,
you
know,
the
the
big
route
that
you've
seen
in
public
markets
recently,
especially
in
semis
and
the
AI
trade,
anthropic
would
also
be
down
even
if
the
business
was
doing
well.
So,
stocks
could
go
up
and
down
based
on
individual
company
performance
as
well
as
just
market
beta.
Exa.
Exactly.
Exactly.
you
know,
uh,
you
should
be
prepared
for
those
kind
of
discussions
if
you're
getting
into
this.
I
think,
um,
actually,
just
as
a
a
side
question,
there's
a
lot
of
these
like,
um,
non-public
market
trackers
of
the
stock,
like
like
here's
my
proxy
of
OpenAI
stock,
here's
my
proxy
of
Enopic
stock.
Do
you
guys
put
any
weight
into
that
or
like
zero?
YouOriginal English
>> I
don't
Yeah,
I
don't.Original English
>> Yeah,
I'm
just
I
I'm
wondering
if
I
should
pay
attention
to
those
things
more
closely
because
they
they
do
give
some
indication
of
like,
well,
here's
the
stock
price
reaction
when
the
model
just
re
I
mean,
it
might
it
might
show
some
market
sentiment,
right?
But
I
think
Ramp
had
some
data.Original English
>> I
think
I
copied
a
RAMP
chart
um
the
other
day.
IOriginal English
mean, this was anthropic spend um on some of their most recent models. The dark blue is Opus 55 and like, hey, what what the ramp data shows is
Original English
>> people switch.
Original English
>> Yeah, exactly. And and with with Opus 55, you saw some reaceleration. And so, would it surprise me if you saw a reaceleration of revenue and run rate in October? And October won't be in Q3. That's the first month of Q4. No, it wouldn't surprise me because I do think a lot of the third party data is showing and suggesting that. Yeah.
Original English
>> Um
Original English
>> well, so okay, let me this is this chart is super interesting, right? Because you you just said like when you when you say stay at the frontier, there's different levels of that.
Original English
>> Stay at the frontier, meaning we're one of the top two, one of the top six, one of the top 20, whatever.
Original English
>> Um, usually people mean top two.
Original English
>> Yeah.
Original English
>> Usually, but sometimes you can expand and say top six. Yeah, Google now has a has a claim to be a number one in some metrics totally.
Original English
>> Uh Meta doing super super well. Uh SpaceX doing super super well. Not at not number one, right?
Original English
>> But number three, number four, you can say that.
Original English
>> So
Original English
>> look at that like summer slump in anthropic revenue.
Original English
>> Um was that because OpenAI was briefly ahead?
Original English
>> Yeah, I mean I I think Astra was a great model.
Original English
>> Yeah.
Original English
>> And I think a lot of people started using Astra. Um and it wasn't just Astra, right? It was what was Google's models? Um what was the one they just released? Carbon or argon?
Original English
>> Carbon. Yeah, carbon argon. Yeah. Yes. Yes. Next yesterday. Uh they haven't released it yet. This is my gripe at Google. They never released anything. They just announced a weight list and then like god damn it. They just
Original English
>> I I think it was also you saw a massive explosion of you know Kimmy K3, the GLM series, Deepseek models. You saw uh openweight models get really powerful um and and get good quickly. So I think you kind of saw all of that but it's it's crazy like the far right bar like what was nonopus 55 or sonnet 55 like that has gone that's h haveved or more maybe more you know in what is that
Original English
>> 2 3 months and so like you do
Original English
>> you imagine if they didn't have opus 55 or sonnet 55 right like who knows what the revenue growth and trajectory could have been so like that's kind of why I do think it's important to be
Original English
>> have a frontier class Yeah.
Original English
>> style model.
Original English
>> And so I I will say from watching these labs for the past 3 four years, they used to have more of a reserve like I will have internal models that I can just release whenever I feel like it.
Original English
>> And um and then I think they towards the GPT56 era, everyone just released whatever they had when they had it.
Original English
>> Right. Right.
Original English
>> Now I feel like the reserves are building up again. I don't know why, but like I it definitely seems like like as you saw with the internal model, uh it it seems like they they have more wood behind the shred for stuff like this. Like I can I I sitting on Fable 55. I can release it the day before I IPO and like
Original English
>> like that'll that'll make my IPO go well.
Original English
>> Yeah.
Original English
>> You know, [laughter]
Original English
>> I mean, look, I I I think there's I think there's good reasons, you know, that the models aren't released right away. You know, on one hand, there is just a lot of like safety and alignment and all that. But as a PM, as an as a as a executive like playing this game,
Original English
>> yeah,
Original English
>> I can play it pretty well if I have a background.
Original English
>> Oh, really? I mean, and and it's also think about um you know, open eyes patch the planet initiative, which is basically, hey, let's use these super powerful models to go find vulnerabilities.
Original English
>> Not even it was 56. It was 56 soul. They found the PHP vulnerability. It wasn't frontier.
Original English
>> Yeah. Which is crazy.
Original English
>> It was just a spam a bunch of like like our last gen model.
Original English
>> Exactly. Exactly. and then let's let's find all these vulnerabilities like the PHP one. Let's go create the patches so that we can instantaneously release the patch while announcing the vulnerability. The challenge is attackers are using these models are using really powerful openweight models and they're finding these vulnerabilities themselves.
Original English
>> Yeah, it's a race
Original English
>> and it's it's a race and it used to be uh you know you had weeks months from like vulnerability discovered to like exploit. I mean now it's collapsed and it's basically zero you know it's zero days like vulnerability announced or discovered exploit available like it's it's almost negative. Yeah. Um and I think in you know OpenAI's announcement of the PHP thing I think hidden in there was I think they basically said like oh and we we there are more serious vulnerabilities that we know about that we're working on patching right now.
Original English
>> Yeah.
Original English
>> Which I mean more more more critical than you know something that touches 70% of the internet. um is is is pretty serious.
Original English
>> Like the 70% of the internet is just a proxy for old.
Original English
>> Yeah.
Original English
>> And like of course it's older and it's going to be more vulnerable. Yeah. Yeah. Of course. Yeah.
Original English
>> That's um I think uh there's a couple of takes. I'm not that interested in like dwelling on on that stuff only because there's going to be smarter people than me on that.
Original English
>> Of course,
Original English
>> the there's one angle of this which is kind of funny which is like well
Original English
>> we're spending a billion tokens so finding all the vulnerability and we're going to spend a billion more to fix the vulnerabilities. What if we just didn't do either and it would look like net the same position as we were at?
Original English
>> Yeah. Yeah. Yeah. Yeah. Well, I think cats out of the bag on that. You can't put the toothpaste back in the tube.
Original English
>> So that but that is one argument people make like that blame the AI instead of blame the they like kill the messenger. Right.
Original English
>> The the other part which is interesting is like I and this might actually uh be an issue for basically nation state actors
Original English
>> uh who actually had all these exploits already. Yeah.
Original English
>> And now like well you know civilians are finding them. God damn it. Like now I have to work harder.
Original English
>> Yeah. Exactly. like all my zero days are gone. Like I had them for 10 years like whatever.
Original English
>> Yes.
Original English
>> But anyway, so so these are fun anecdotes or consequences of this.
Original English
>> If you want to really do superst and makavelian if you're a nation state actor, what do you do
Original English
>> to to preserve your edge
Original English
>> because like you cannot have the a channels that you're relying on lock up.
Original English
>> Yeah. Uh, so then then then you send the Saudi decoy to put the thing behind your TV so that people so that so that they get
Original English
>> distracted by that so that your real spy
Original English
>> Yeah.
Original English
>> gets in.
[laughter]
Original English
>> That's just some war games.
Original English
>> I I I watch too much TV to to to think that what we are told is the full story.
Original English
>> Yeah. Yeah. I think that's fair, right?
Original English
>> I think that's fair. Too much is at stake.
Original English
>> Yes. Too much is at stake. Um, okay. So coming back to the actual again finance like I really want to be a primer for everyone who are new to the economics of models and model labs and what what have you. Um one thing that have that people have said is that the R&D per model let's say like what does what does Fable 5 s 5 opus 5 cost
Original English
>> let's call it a billion dollars
Original English
>> that lasted me 6 months right because you just said the dep the depreciation it lasted that six months. Yeah.
Original English
>> And so isn't that worrying, right? Like about like well these things are the fastest depreciating assets in history. This is something that me and Sargo have talked about before.
Original English
>> Yeah. Yeah. Yeah. Yeah.
Original English
>> Um and I think the the net is this no but like it's a qualified no
Original English
>> of well as long as my next model run
Original English
>> is better than the than the than the current state of the art we're good.
Original English
>> Yeah. Y
Original English
>> it's a little bit of a house of cars if if the next one doesn't work out.
Original English
>> Yeah. which um briefly affected OpenAI. So, a lot of people know by now that, you know, like GPT45, the the big the big run wasn't wasn't that successful.
Original English
>> Um, and they, you know, they they they since fixed it, but like that a slump in a in a run or a failed run,
Original English
>> uh, can really really screw with the the ROI on the thing. And look, I think there's a really good analogy to the trip industry. And I
Original English
remember speaking to someone, you know, much older and much smarter than myself who did a lot of semiconductor investing and, you know, in the n 80s and 90s, um, about all these chip companies.
Original English
>> They've seen it all
Original English
>> and, you know, they've seen it all and they were highly skeptical. Um, and, you know, sometimes you have industry vets that are highly skeptical and, you know, they miss the next big opportunity because they never think the the incumbent can be disrupted. But the point of view they had was it's very different to tape out a chip that is competitive with the leading chips at the time from building a machine that can continuously tape out chips that compete with, you know, the the chip factory. And so he was like this this individual's point of view was
Original English
>> maybe a chip startup can build one chip, but guess what? they're not going to have the machine to constantly pump out a new chip every year and stay at the frontier um from the chip standpoint. And that's why Nvidia is king. It's not because they have the best chip now. It's they have the best factory
Original English
>> process for chips.
Original English
>> Exact. Exactly. It's the machine that builds the machine that
Original English
>> and so it's kind of one of these I mean this is the bare case on all these neolabs which is you know and and you talk to many smart folks and what they might tell you is hey if you want to really compete and be have like a soda model at the frontier you can't you can't lo you can't uh go more than one order of magnitude of compute less than the labs you just can't compete if you don't have you know one order of magnitude of compute of the big labs um and so as a Neil lab you know the question isn't can you build one model it's Can you attract compute and talent required to continuously pump out new generations of models every year to ultimately compete in your domain? And so I think for the labs the reality is is I think it's very hard for anyone unknown players to catch up to kind of like the generalized frontier big models. I think you'll continue to see domain specific models um or models or like new types of models, decision models like Jev. Um but I think the I think the big labs they are on the treadmill of continuously pumping out new models because guess what? If if anthropic stops, OpenAI and Gemini will continue and then you'll just kind of see re you'll see revenue fall off which is why I do think it's so important to stay at the frontier. Now what do I expect and like what have I seen from anthropic now in the data it's as their model families get better and better they get more efficient but they also capture more revenue that could be because they're doing more economically valuable work or they're doing the work faster. Um but what you're seeing from now kind of black and white is hey as Enthropic has 2xed their compute spend uh to build and train these models they've 14xed their revenue and like I'm not really sure what would cause that trend to stop. Um, I think models are only going to take on longer running tasks, um, more economically valuable tasks. And, you know, if you can't tell, I'm a hyper optimistic person. And so, maybe I'm just drinking the Kool-Aid too much. Uh, but I I do think we're nowhere close to hitting a limit of the dollar kind of like economic value that leading models can capture. I think what's maybe more interesting is you know OpenAI you know Luna class models kind of also trying to capture and compete at the low end of the market with much smaller uh much cheaper models you know and kind of span everything from um frontier to you know distilled versions of their own models that are smaller in and you know
Original English
>> cents per million input output tokens not dollars.
Original English
>> Yeah. um uh cosign all of that. I think uh that is a good way to think about it. Um I I I think uh you know funny I I'll just comment this because it's in my head. Uh 61 Terra has been pretty pretty successful. 61 Luna like that that whole series is very interesting. The approaches of the different labs is also super interesting.
Original English
>> Um Anthropic to this day has no decision model
Original English
>> and they're like well we're good like use type safe whatever. Um, and like well they don't have a standalone voice API.
Original English
>> They they have a voice model. They just don't don't market it. They don't they've never done images. You know, it's a lot of enterprise focus and coding focus and OpenAI has been a little bit more of like we'll do everything and we'll try Sora, we shut down Sora.
Original English
>> Remember when Sora was like the number one app in
Original English
>> Yeah, exactly.
Original English
>> in the US.
Original English
>> Yeah. Exactly.
Original English
>> Um, and then Gemini has been the most like we will explore everything. Um, and so like we appreciate them because if they didn't try it, no one else would, right? like they have the funds for it.
Original English
>> Um, but like I think often about anthropic in terms of like well okay enterprise and coding and they they're first to it. They nailed it. They still continue to nail it right and everyone's chasing after them.
Original English
>> Um is that the story going forward? Is is is it just more of the same or like how what should we expect?
Original English
>> I think it's I think a lot of this boils down to like a new type of innovator's dilemma, you know, which I almost call like the compute innovators dilemma. Like let's say you're open and you have Sora, you have many different model families, you have image models, you have everything. Well, the reality is is how do you choose what to serve? Maybe Sora demand explodes. Are you going to allocate a lot of your codeex compute
Original English
>> to slot videos? Yeah.
Original English
>> Um because you probably will have to like if you actually want to make I mean that's exactly why they shut down experience good you need to. And at the end of the day, what a lot of people have decided is, well, guess what? A lot of the the highest ROI, most economically valuable work has been in coding. So, it's hard to prioritize anything else other than that. It's why there's probably opportunities for other companies and startups to build models and build products in those domains because guess what? What is open ad anthropic chasing? They're not chasing the incremental 10 billion in market cap. They're chasing the next trillion. And so, the reality is is for a startup or a Neolab, great. You could go build a build a company, build a product, build a model where you exit for $10 billion and it's a home run, grand slam. Um, that's awesome. And simultaneously, it's too small for OpenAI and Anthropic to matter. And so, like, what is Anthropic done just a bangup job at? They said coding is our thing. We're going to be great at coding. We're going to build the best coding harness in cloud code, and we're going to continue to nail it. I think everyone is kind of starting to ask themselves well when do we maybe you asked a version of this earlier when do we tap out the coding tam um I think one point of view is I have asked a ve very many times this question
Original English
>> but I think one point of view is hey coding is actually the best generalizing model and framework and like that can be applied you know you think of it as coding but
Original English
>> when I use cloud code like I'm not coding but I'm using cloud code when you use codec are you are you coding no these are they're generalizing and coding is a good framework for generalizing
Original English
a big push by both openthropic into life sciences and drug discovery. I think they've both been quite public um about their interests um to kind of like capture that domain as as a new field and I do think those are just different.
Original English
>> Um
Original English
>> it would seem like Enthropic is investing more than OPI on specifically the life stuff.
Original English
>> Yeah, I' I'd agree. I'd agree. Um but now I can't remember the question you asked. Um but I but I do think you're starting to see these companies evolve the scope of what they want to go after and it has been very targeted on coding given it's been the best generalizing. You know you want to generalize knowledge work. These models have generalized the best as they've been stronger in coding. Um but it also can give it some a soft underbelly to other areas. Maybe it's visual thinking and reasoning. You're seeing great models there like Allorean. Maybe it's decision models like Jev and Types
Original English
safe,
right?
Like
there's
there's
a
lot
of
different
uh
soft
underbellies
they
may
have,
which
is
opportunity,
which
is
great.
It's
opportunities.
Like
you'd
love
to
see
better,
cheaper
models
in
other
domains.
You
know,
you
don't
need
to
drive
the
Ferrari
engine
to
the
grocery
store.
Hey,
maybe
you
a
Toyota
will
do
just
fine.
[laughter]
Uh
and
I
think
that
will
continue.
Original English
>> Um
and
good
shout
out.
We
just
interviewed
Andrew
on
his
benchmark
and
and
glimpse
I
think
glimpse
is
the
the
new
are
you
guys
invested
or
we
are
promotion.
Wow.
Okay.
All right.
Original English
>> Yes.
Original English
>> Uh
it's
it's
it's
weird
to
like
this
is
a
whole
different
discussion
but
ultimately
I
don't
get
as
like
well
you
will
lead
the
giant
rounds
in
like
you
know
the
anthropics
of
the
world
but
you
also
care
about
Laurian.
It's
like
way
smaller
and
like
you
know
probably
worth
like
a
minute
of
your
time.
Original English
>> Well
no
like
like
we're
worth
a
ton
of
my
time.
I
mean
I
would
say
like
we
barbell.
I
mean,
we're
life
cycle
investors.
We
invest
early.
We
invest
late.
We
invest
in
the
public
markets.
And
I
think
there's
a
lot
of
value.
Like
the
way
we
think
about
it
is
some
of
the
best
founders
today.
They
need
latestage
capabilities
early
on.
And
latest
stage
capabilities
isn't
just
capital.
It's
relationships
with
execs.
It's
relationships
with
public
companies.
I
mean,
it's
companies
are
built
on
on
hyper
mode
now,
right?
It's
not
let
me
go
raise
a
round
and
let
me,
you
know,
add
10
million
of
AR
in
18
months
and
I'll
go
raise
my
next
round
and
then
two
years
later.
It's
like
you're
you're
growing
to
100
million
super
fast.
You're
you're
raising
nine
figures
of
of
money
super
quickly
and
it's
and
it's
hard.
I
mean
in
many
ways
you
like
you
have
to
like
break
the
laws
of
physics
to
how
like
cultures
can
be
maintained.
Lawyers
lawyers
are
not
set
up
for
this.
They're
like
what
do
you
mean
when
we
come
on
a
weekend
to
because
like
Monday
might
be
different.
Original English
>> Yeah.
Exact
exactly.
Exactly.
[laughter]
I
mean,
it's,
you
know,
you
used
to
think
about
as
a
board
member,
an
investor,
hey,
let
me
think
ahead.
Let
me
make
sure
we
hire
the
VP
of
sales
and
the
CFO,
you
know,
6
months
before
uh
before
we
need
to,
so
we're
not
behind.
Now,
it's
like,
hey,
6
months
before
you
need
to,
may
have
been
before
the
company
was
even
incorporated,
you
know,
and
so
how
do
you
how
do
you
do
that?
Like,
when
do
you
hire
the
right
execs
so
you're
not
doing
it
too
late?
How
do
you
hire
ahead
but
not
too
ahead?
I
mean,
it's
I
feel
like
the
pressure
of
building
companies
has
never
been
greater.
Now,
the
size
of
the
prize
has
never
been
greater.
Um,
but
it
is
it's
as
you
know
it
might
appear
as
easy
as
ever
to
build
a
company
but
I
actually
think
it's
the
opposite.
It's
almost
as
hard
as
ever
to
build
a
good
company
and
maybe
more
importantly
to
build
a
durable
company
which
is
I
think
what
what
everyone
aspires
to
do.
Original English
>> Yeah.
Uh
I'll
also
shout
out
the
you
know
on
the
model
factory
thing.
Um
we
also
interview
poolside
which
acquired
by
Nvidia
they
were
pitching
this
model
factory
really
hard
and
and
I
do
think
like
uh
the
business
that
builds
a
business
is
kind
of
like
the
the
general
uh
takeaway
that
I
want
people
to
know.
Um,
one
of
the
things
I
wanted
to
I
was
diving
towards
I
didn't
I
didn't
quite
start
it
yet
but
I'll
start
now
which
is
typically
when
you
have
an
S1
you
have
a
clear
sense
of
the
business
uh
let's
call
it
market
segments
product
segments
the
org
chart
the
whatever
Original English
>> um
and
we
got
cloud
code
Original English
>> cloud
code
work
Original English
>> miscellaneous
Original English
>> or
platform
whatever
Original English
>> u
and
I'm
like
okay
what
about
legal
what
about
design
like
all
the
stuff
that
you've
cloudai
the
consumer
business
Original English
>> for
a
while
that
was
a
thing
Original English
>> Mhm.
Mh.
Original English
>> Um,
do
we
care?
Do
it's
just
all
like
one
revenue
bucket,
>> you
know?
Original English
>> I
I
think
we
do
care
like
what
the
products,
right?
Like
this
claw
code
Original English
>> and
what
else?
Original English
>> I
I
and
this
is
where
I
do
think
and
there
was
there
was
a
section
of
the
S1
um
where
they
broke
out
some
of these
like
vertical
applications
they
want
to
go
after.
I
mean,
it's
kind
of
funny
you
bring
this
up.
Like
I
had
a
hypothesis,
you
know,
years
ago
that
like,
hey,
maybe
the
labs
will
stop
releasing
models
and
instead
they'll
just
build
applications
and
products
powered
by
their
models.
And
if
they're
powered
by
the
best
models
that
are
never
released
publicly,
hey,
maybe
they
have
the
right
to
build
all
the
best
applications
Original English
>> that
you
kill
Harvey
by
only
using
Claude
Fable
6
legal,
Original English
>> right?
Or
it's,
hey,
you
know what,
cursor,
guess
what?
We
have
well,
you
know,
years
ago,
we
got
the
best
model
and
we're
not
releasing
it.
So
guess
what?
But
we're
going
to
have
the
best
coding
agent
because
our
model
that's
the
most
powerful
isn't
released
publicly.
And
I
do
think
it's
why
some
of
the
leading
application
companies
today
are
moving
towards
in-housing
their
models.
They
want
to
control
their
own
destiny.
You
need
to
I
think
it's
going
to
be
really
hard
as
an
application
company
over
the
long
haul
to
survive
without
a
research
team
internally.
Um
it's
one
of
the
like
an
investment
thesis
that
I
have
that
if
you
know
investing
in
applications
like
I
want
to
believe
there's
two
things
really
strong
product
org
but
also
a
really
strong
research
org
and
I
also
want
to
believe
that
they're
aligned
and
as
the
research
gets
better
the
product
gets
better
which
sounds
counterintuitive
but
there
are
a
lot
of
research
orgs
that
are
just
doing
research
for
the
sake
of
research
versus
closing
the
loop
and
actually
like
having
a
a
positive
cycle
of
of
product
improvements
with
research
improvements.
I'm
I'm
not
a
you
know
I'm
not
in
a
topic
where
I
have
the
benefit
of
being
friends
with
fair
fair
amount
of
people
in
the
topic.
Uh
and
I
would
say
research
is
king
topic.
Original English
>> Yeah
totally
totally
Original English
>> by
far.
And
so
part
of
my
whole
thesis
around
the
model
labs
versus
agent
labs
is
uh
model
labs
will
you
know
respect
the
model
and
ag
and
try
to
build
AGI
and
products
will
be
a
final
last
mile.
So
the
delivery
mechanism
whereas
the
agent
labs
will
will
respect
the
customer
the
most
and
build
whatever
they
need
and
then
and
they'll
they'll
backwards
integrate
to
models
as
and
when
but
they'll
never
be
probably
never
be
at
a
frontier
for
for
those
things
and
both
can
coexist
both
have
a
marketally
um
maybe
to
your
question
I
do
expect
to
see
a
much
bigger
push
by
both
labs
anthropic
and
open
AI
into
vertical
applications
and
kind
of
like
diversifying
out
of
just
delivering
models.
I
I
think
I
I
think
that
the
actions
of
Enthropic
show
so
far
have
shown
no
serious
intent
at
it
Original English
>> because
cloud
design
is
just
a
tab
inside
of
cloud.
Original English
>> It's
not
like
a
whole
Adobe
you
know
replacement
cloud
legal
is
a
markdown
file
you
know
markdown
repos
Original English
>> uh
which
like
that
is
the
AGI
pill
is
if
if
research
is
king
and
like
you
know
one
model
does
everything
that
is
what
you
do.
Original English
>> Yeah.
This
is
classic
enterprise
and
org
management.
You
have
a
cash
king
product
that
think
your
sales
reps,
what
do
they
want
to
sell?
The
thing
they
know
how
to
sell
that
they've
been
selling
that
is
crushing
it.
What's
harder
to
sell?
Like
new
things,
new
SKUs.
And
so
it's
inertia.
It's
momentum.
And
this
is
just
classic
like
big
company
boring
stuff.
Original English
>> But
it's
just
like,
you
know,
I
I
think
for
someone
like
I've
been
through
maybe
15
years
of
IPOs.
And
for
an
IPO
stage
company,
much
less
a
$2
trillion
company,
Original English
>> you've
never
seen
like,
well,
we
have
three
SKs.
Original English
>> Yeah.
Yeah.
Exactly.
Exactly.
But
but
I
think
to
anthropics
credit,
they've
been
really
good
at
focus
and
like
they
haven't
been,
you
know,
enamored
with
the
next
thing
or
Original English
>> no
robots.
Original English
>> Yeah.
Original English
>> No
images,
Original English
>> you
know?
It's
And
again,
you
look
at
Open
Eye,
it's
tough.
Like
who
are
they
competing
with?
They're
competing
with
Nvidia.
They're
competing
with
Google.
They're
competing
with
Meta.
They're
competing
with
Anthropic,
you
know,
like
they're
they
have
different
lines
of
business
that
are
all
competing
with
giants,
like
the
biggest
tech
company
in
the
world.
And
it's
like
it's
it's
a
tall
task
to
take
on
one
at
a
time,
let
alone
all
of
them
at
a
time.
But
hey,
that's
that's
the
game
you
take
on
if
you
know
when
you're
a
trillion
dollar
company,
you
know,
the
the
Original English
stakes are just higher.Original English
>> Yeah. Um, okay. I want to round out the entropic discussion and then we can go to general commentary on on other stuff. We've typed the main debates that you've you've been engaged in.Original English
>> There's um there's two others. So um this is going to be very like financially in the weeds. Um there if you lookOriginal English
>> you want to talk about convertibles.Original English
>> Yes. Exactly.Original English
>> Yes. [laughter]Original English
>> Okay. So, um, on the balance sheet, you know, earlier in the episode, we talked about, hey, 140 million of cash raised, uh, capital raised, 134 billion of cash. That's what this the balance sheet shows you. What the balance sheet also shows is a I'm I'm going to I might mess up the exact numbers because I don't have them in front of me, but I think about 108 billion of convertible notes payable as a liability. And so, the question is, well, is that debt? Did they raise debt?Original English
>> Um, and what you also see is a huge portion below the operating income line of like other income and expenses that makes it look like they have a huge net loss. They're both related. And let me explain. Um, so any company and any startup founder listening to this would understand and appreciate this. Like there's different ways to raise capital. You can raise preferred equity, you can raise converts, you can raise safes. There's a whole different host of options to raise money. Enthropic just like every other startup did everything. They raise some preferred equity, they raise some converts, um they raise some safes. And along the way, there's different reasons that certain investors of the company might prefer one or the other. When it comes to strategic investors, there's even more reasons why they might prefer one or the other. And I think it's it's very well known that some of the strategic investors, Amazon and Google in particular, were very large investors and very big owners of Anthropic early on, right?Original English
>> Yeah. Go ahead. Sorry.Original English
>> and and so early on Enthropic and Google 23 24 25 a lot of the dollars that they provided the company were in the form of converts and safes and notes. Um those the value the the value that those converted at at the time was pegged more towards the valuation those companies were raising at at the time. Why might they prefer a convert? Well, they're public companies uh Google and Amazon. If they have an ownership in a third party that exceeds a certain limit, they now have to start reporting more on that look through ownership. Right. If you own 20% of another company, you almost have to.Original English
>> It's consolidated financial statements.Original English
>> Exactly. And that company's losing a lot of money, you now have to report that loss on your books. And so it's they didn't, you know, strategic don't want to hit certain ownership thresholds. So, hey, give us a convertible. Give us a note. We'll give you cash. You haven't given us shares yet. And so we don't have that equity ownership. We don't we haven't gone above those thresholds. Maybe Anthropic didn't want to give them voting shares. I don't know, right? Let's let's have your shares convert into non- voting shares because we don't want the governance of a of a supplier or strategic like I'm I'm totally speculating and hypothesizable reasons to do reasonable reasons why hey they were converts but the conversion price was at a much lower valuation relative to what it is today. Okay.Original English
>> And so when you look at the balance sheet what and balance balance sheet have to balance you have assets equal equity plus liabilities they have it has to balance and so when you raise a convert you get cash your assets go up well if your balance sheet's going to have to balance your liabilities also have to go up and it makes sense a convert is a promise for shares in exchange for cash and so Enthropic took on that liability and at the time the liability ility matched the cash raised. Now, what's happened to Anthropic's valuation since 2023 and 20? It's it's gone way up. And so, now what happens is the dollar value of your liability, because you promised shares at a price, the dollar value of that liability has skyrocketed because the valuation of the business has gone up. What does that mean? Okay, now the liability looks big. It looks like a $ 108 billion debt that Anthropic raised.Original English
>> The reality is is they didn't raise $ 108 billion of debt. There was about 11 billion in those combined convertible notes. About $6 billion has already been no $5 billion has been converted to equity. There's 6 billion outstanding. The market value of that 6 billion is 108 billion because the company's value has appreciated meaningfully. And so there's this debate of like, well, they have 108 billion of debt on the balance sheet. They haven't raised 141. they've raised 141 plus 108. Um that debate is just misguided. Um there is no debt on the balance sheet and the 11 billion of converts is inclusive in the 141 billion of total capital raised. Um and so debate one of the say good job summarizing this.Original English
>> Yeah. But but it it actually goes deeper. Yes.Original English
>> Um that's just the balance sheet. Okay. Now let's look at the income statement. The P&L.Original English
>> They have an operating profit. You could say great gap operating profit, but what comes below operating profit? Net income. Someone could say, well, in the first six months of 2026, they had negative80 billion go of net income. So, you could say, and this is a debate. This is what people are saying. Again, it's very misguided. They're losing $80 billion. Like,Original English
>> it's true.Original English
>> What a terrible business. Technically, by gap accounting, it is. But, okay, let's go back and I'm going to go back to the balance sheet. the liability has gone way up. That liability is now 108 billion. Okay, let's think about the the balance sheet. Assets equals liabilities plus equity. As liabilities have gone up, two things have to happen. Either your assets have to go down or your equity has to go down. Well, your assets haven't gone down. That means your equity the the value of your equity has to go down. This is where it might get a little complex, but how the how the three statements link isOriginal English
>> yeah, people can take accounting 101. It'sOriginal English
>> net income.Original English
>> Yeah.Original English
>> On the P&L flows to retained earnings on the equity value of your balance sheet. And so what has to happen as that liability has gone up, retained earnings has to go down by the same amount, which basically means net income has to go down by the same amount because net income on the P&L flows to retained earnings on the balance sheet. It's all mathematically linked. You can't break it.Original English
>> Yeah.Original English
>> It's all mathematically linked. You can't break it.Original English
>> But what's happened is in order for those statements to link and for those statements to tie, you need a huge decrease in value on your income statement. And what what happens is guess what? Anthropic's value has just skyrocketed recently. Um they've raised big rounds over the last 9 months. Um, and so as those converts have appreciated in value, the value that those notes have appreciated shows up as a loss on the P&L. And of the $81 billion of loss in net income on the first 6 months of this year, 72 billion is from a series of converts for Anthropic uh for AWS and Google and 9 billion is for a separate set of safes and notes. And so 100% of the loss in the first six months is just the value appreciation of their converts. If Anthropic never issued converts to Amazon and Google, just did straight equity, that loss would it wouldn't be there.Original English
>> And so it's kind of one of these like technically from GAP accounting, they do show an $80 billion net loss.Original English
>> Do I view that as an accurate representation of the business? No. Because the reality is is 100% of that loss is just the appreciation of their equity, which everyone would say is is a good thing. And as those notes convert to equity, those go away because you no longer have this dynamic of your liability going up as your stock price goes up, which means your net income has to go down over here. So, you know, all the financial statements tick and tie. And I Yeah, sorry if that got too like financial.Original English
>> That was a really good recap of the discussion. That's should be a definitive answer for anyone uh who has come across that topic. Uh there's another discussion about the circular financing ofOriginal English
Google and Amazon. We don't have to go into that. I mean I think that is small in comparison to the numbers we're talking about today.Original English
>> Um and uh in general like you know book value accounting is not that relevant compared to overall enterprise value. UmOriginal English
>> oh maybe one other thing related to this I think there are a lot of people that say okay wellOriginal English
>> the only reason the liability has gone up is because the value has gone up. Well, is that does that mean that Anthropic issued shares today? And did they give did they give Amazon and Google $108 billion worth of value? But did they give him those shares at the price from four years ago? Did they just give them shares at a huge discount? It's like no, that didn't happen. What happened is they raised that money years ago in normal financings like a normal startup would and over time it's just appreciated in value.Original English
>> So, so you know, I think a lot of people if uh they're just not financially educated to that level because they've never had that exposure. So it is what it is. You're just educating. What was the second uh debate that you were referring to? You said you had two debates.Original English
>> Oh, those related. Yeah. So it was the balance sheet. Oh, wow.Original English
>> Have they raised more debt? And then it was the income statement. Are they actually losing money?Original English
>> As an investor, why do you get involved in these kinds ofOriginal English
>> Oh, this is more of like I mean I'mOriginal English
>> you're just having fun.Original English
>> I think in thro look alttimeter is a firm. We do venture investing, growth investing, and public investing. And I spend a lot of my time on the venture investing the early stage. However, I just I consider myself like a student of finance and Anthropic is such an iconic company.Original English
>> You shouldn't have to educate people on the three statements of accounting. Like that'sOriginal English
>> Yeah. Yeah. But but like hey, I mean I think a lot of people look at the balance sheet uh the income statement, the P&L, but the reality is is there's a lot to learn from the balance sheet and the cash flow statement too. Um and so this was more of a hey as just someone who's intellectually curious. I mean anthropic is is a really important company to understand. I think if the anthropic S1 came out and the financial profile that was pictured was doomy and it didn't look like a good business, I think we have a lot to be worried about. Yeah.Original English
>> Um because if they're not delivering profits and high ROI, what does that mean for everyone downstream of them? Like that's it's I think it's really important to understand the anthropic P&L. But what I understood from the anthropic P&L, we let me just rip through these really quickly. Um here's what you're seeing about their gross margins. Lots of leverage in the gross margins. They're improving rapidly.Original English
>> Which uh one one nuance on this is this is the half that is not through cloud cloud partners.Original English
>> Exact. Yeah. Uh this this No, this is just a gap gross margin,Original English
>> right?Original English
>> Is that okay?Original English
>> I I I don't know. I I read something about like how obviously cloud partner margin would not go into gross margin at all and that's 50% of revenueOriginal English
>> account I believe is a lot of the cost that they pay out the rev share is in sales and marketing which is below the line which is which I think what you're sayingOriginal English
>> which by the way $3 billion of sales and marketingOriginal English
>> yeah I think it's like 40 something% of the revenue comes from cloud partners it's it's not a triply amountOriginal English
>> 48Original English
>> 48 okay yeah I was close in so you see gross margin profile improving and and sure maybe it'd be lower if uh you kind of factor in the third party, you know, that the cloud providers, but like the trend is going up. You see operating margin going up, positive gap operating margin, you see f free cash margin going up. And so to me, what I see is a highly profitable business that is probably going to flip to reinvest in compute and buildout. I think the way compute gets built um is going to look different in the future. It's going to be expensive. There's going to be more upfront payments, right? like I do think you should expect to see the profitability profile of these labs um kind of go the other way in the short term as they shift to investing in growth and investing in compute. Uh but the biggest takeaway for me was it's a highly profitable business that's still operating in growth mode that's seeing some reaceleration probably you know in the third party data from the new models which gave me a lot more confidence for the rest of the industry. So long as the revenues show up, the compute obligations will be hit. Um the supply chains will, you know, continue to be saturated and constrained and companies will continue to perform. And OpenAI and Anthropic, the reality is is they're a huge percentage of the overall AI revenues. And so everyone looks to them as a proxy for the rest of the industry. So if they keep growing, which again I think a lot of the bears would say they're going to stop growing because openweight models are going to eat their lunch like this, this and that. Um but I think those are the important kind of questions.Original English
>> Yeah, very important uh very good questions. I I think like Yeah. Anyway, uh do you do you dabble in like forward projections of numbers like what what like we we what what should we expect from FY27? What is expect Yeah. I I think that's that's too hardOriginal English
>> because the the the delta is very high, right? given the year they just had,Original English
>> it's it's a business that is so hard to project because the future is so like unknown and unknowable. Um, you know, they'll go public. The sellside research analysts at all the banks will will build their models. We'll kind of have a forward a forward number. I don't know where that's going to shake out. Um, but I I I do think you'll, you know, you you'll see um a lot of optimism um about what the future of this business could look like. But again, it's would it surprise me if it was much higher than expected or some you know anything could happen. It's anyone trying to predict the future es especially in the short term it's it's as hard as ever. I think businesses at the frontier are the most predictable because they're the least likely to be disrupted but um I don't know we live we live in a highly uncertain time.Original English
>> Yeah. the the kind of A+ financial analyst as a PM in my hedge fund I would have wanted is to do physical limits uh calculations on like okay you have all these inference optimizations where like how much juice is there left on the current thing um let's let's say the nextG has has this level of inference how much can we charge on that and uh given the competitive profile and exhibited u pricing history of of these things uh and then finally how much GPU supply uh has been locked in and secured so that that translates to this number of tokens and that's what it can sell for because that's like the physical limits of what where these things can go because I think a lot of the financial analysts and I used to do this you you would just kind of had this spreadsheet of like here's the past performance and you couldn't click the right corner and just drag itOriginal English
>> out yeah exactly how it works can't do that I meanOriginal English
>> there's not good an there's not good analysis rightOriginal English
>> totally I mean it's I think there's limitations everywhere everyone knows like hey Nvidia can only produce so many chips you know challengers cerebras TPUs can only produce so many chips, but I think there's other constraints. There's only so much land power shell um that's approved and permitted.Original English
>> Correct. So, you do that build up, right? And then you're like, "Okay, who has what share?"Original English
>> I mean, we're we talked a little bit about like debt and credit markets. Like, right now, you want to go raise multi-billion dollar debt for GPUs. No bid. No bid. They're like debt markets are close. They're, you know, big debt deals that aren't happening, right? Almost the equivalent of like an IPO getting pulled. Big debt deals. Why is there 100 billion in equity but no 100 billion in debt?Original English
>> Well, I thinkOriginal English
>> I'm going to turn around and do those put those money right inOriginal English
>> big credit funds. They're all looking at the look through exposure and any neocloud or a hyperscaler that'sOriginal English
raising debt is the biggest question the credit providers are going to say is well who's the offtake? Because the reality is that's actually who I'm underwriting because that's the customer. That's the one paying that's the end payer of the bill. And if the end payer of the bill does poorly, the bills don't get paid. And right now, the ender of the bills are Enthropic and OpenAI. So many of the data centers and the the the being built by the Neoclouds are for OpenAI and Enthropic. And a lot of the credit providers are saying, I'm feeling kind of full, you know, I just did a ton I just did a ton of debt um for either one of these. And didn't matter if it was Coreweave or Nebius or an Upstart like a Volta or someone else. It's like, hey, I'm feeling full. And I think debt markets just are going to go through a little bit of indigestion. They need to figure out, well, do I feel good? Do I feel great? Hey, some of these projects that were promised, are they being built on time? Is the revenue flowing back to me? And I do think the credit and debt markets will eb and flow. Um, because there are just a thousand reasons why, you know, a project could get delayed, right? A Neocloud could go sign a contract with OpenAI. They could sign with uh Nvidia. They could go have Dell package them up. They could raise the debt. And then you know what? There could be local community uprising
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>> and picketing. I'm lit. I'm not even being kidding. And then it's delayed. Then what?
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>> Well, the debt was raised. Someone's got to pay the interest.
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>> What happens? Yeah. You know, and you have creative delayed draw facilities where you don't, you know, you the debt is committed, but you don't draw it down. But like all of these have limits, you know, like and you get super in the weeds on like SLAs's and and who was responsible for it. Was it a delay in the supply chain? Was it a delay in the construction? You know, the data center developer? Was it a delay by the operator? Was it,
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>> you know, something else like community unrest? And,
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>> you know, that's that's everything that, you know, credit markets are are thinking through. Um, and then there's a lot of other super technical reasons, you know, that we don't need to get into that, you know, make it difficult with credit default swaps and hedges going through the roof. And so now it's for debt providers, it's tough to hedge the exposure they have. And so as as the hedges get more expensive, your willingness to take on more risk goes down because you can't hedge out of it. Like it's
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>> this is all the function this is all symptoms of the debt market running out of money.
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>> Yeah. Well, I don't I don't know if debt markets are super deep. I think it's more of like an appetite. And of course, there's a price for everything. Hey, great. You know what? go take out 5 billion of debt at 20%.
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>> But you don't see this. You see this at the equity layer and like so again, how come there's hundred billion dollars of equity
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>> Yeah.
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>> that's going to go into Entropic and and Entropic cannot raise $100 billion or $50 billion.
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>> I I think um I would I don't know if I would say they can't do it. Um I don't know if well one they need to be rated.
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>> Um do they want to? You know, my my guess is Enthropic and OpenAI are happy that Neoclouds exist to take the balance sheet risk. Maybe they don't want to take that balance sheet risk because again, it could be things out of their control that blow them up. You have a huge debt stack
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>> in your cap in your cap table and things out of your control going like
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>> you don't you're not the one holding the bag. And again, this is like the beauty and the curse of of why Neoclouds can be great businesses or they could be brutal businesses if the contracts aren't airtight, if things aren't structured well, your balance sheet business, you know, and and just like fintech balance sheet businesses can eb and flow, you know, there's a there's a ton of Oops, my computer may have died.
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>> It's fine. We'll edit we'll edit.
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>> Okay. You know, balance sheet businesses, right? Like there were a ton of fintech companies in 2020 that did super well when interest rates were low that got blown up when interest rates were high and balance sheet businesses can be really tricky and very cyclical and you don't want to own a balance sheet business when a market cycle tips over. So hey on on some level I'm sure you know especially for internal chip efforts right like the openi team will might may want to develop their own data centers for their jalapeno chips right and anthropic same thing for their chips um but I'm sure they're also very happy for other people to take on that risk um because it's someone else's core competency and you know a lot of debt could add a lot of blowup risk um and there was no debt on on the anthropic balance sheet now they have facilities in place that they could brought down. And so would it surprise me if you start to see debt on their balance sheet in the future? No.
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Um I mean, but again, it's like you want to build a gigawatt, right? 50 60 70 billion. That's that's not that's not a tripling amount just for and that's just just one gigawatt. Um I think both open and anthropic have said they kind of want to get to five by the end of the year. Who knows if it's if it's that or something more or less than that.
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>> Are they about equal?
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>> I don't know. Okay. Um
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>> that's interesting to because I think
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>> Open I was ahead.
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>> Correct. Right. By by a lot but I think closed the gap a lot. Uh and open at least started like Stargate was a plan the first stage in a plan to get to 30 30 gawatt.
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>> Yeah. By 2030.
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>> Yeah. So and so I was like okay well that's I haven't heard that plan from
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>> but again like let's let's do some fun math really quick on the fly. Let's see if I can do it. Getting tired. Um, OpenAI, let's say, I think when they came out with that, it was maybe a year ago and they were what, like 1 and a half gawatt, something like that. So, whatever, round that down to zero for just the sake of the argument. Let's say Open Anthropic each want to go from 0 to 30 over 5 years. So, you're going to add 60 gawatts of AI capacity, just the two labs. What's a good assumption for their total capacity? I don't know, 40 50%. Um, let's say it's 40%. And I don't know, now we're getting too tough in math in my head, but you know, let's say that gets you to, I don't know, 110 120 gigawatts of total capacity brought online over the next five years. Let's say 100 just so the math is easier. And let's say it costs $50 billion to build out a gigawatt. That's $5 trillion. $5 trillion over the next 5 years, right? And so call it a trillion a year. You're you're talking about a nontrivial percent of GDP, you know, real GDP. That's like five% plus or minus of GDP. Like that is a massive bonkers,
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>> massive buildout, you know, rivals the railroad buildout. You know, I I think I've seen some charts somewhere that kind of compare those expected numbers, but like f you know, when you're measuring things in like whole number percents of GDP, uh you're that's that's huge.
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And so like the credit markets, [clears throat] debt markets need to be super super super deep and super super open for that to happen. You need global buildout. Um, you know, it's kind of funny, you know, you know, semi analysis number for next year is like 50 and, you know, I think Brad at the all-in summit said, "Oh, maybe it's lower." And I think there were some trolling going on back and forth. And I think the end result of that was like, "Hey, maybe the 50 gigawatt number, I don't want to speak for anyone, was more of like what's constructed." You know, Brad was more of what's constructed and live generating tokens because there can be a gap um in what's constructed and ready to go versus what's actually live and generating tokens. But I think it's like a real debate in the world, you know, and then you could see a pretty wide range of outcomes, you know, do we really add 50 gawatts of capacity next year as an industry. Um, or is it 20 or 30, you know, like who knows? I just know everyone you talk to in the supply chain is saying, you know, we we have we
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have orders or indications for orders that are well over 100% of our capacity.
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>> And like I don't have line of sight to that slowing down for the next few years. Yeah.
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>> And
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>> Yeah. Yeah. And and the limit like land shell power all these but also silicon like straight up totally like compute memory every every part of supply chain
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>> totally
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>> that's the work that's why 700 is so powerful right now.
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>> I mean look at these recent Kimmy models Kimmy K3 you know how much memory you need gigabytes of memory just to serve that you're not running that on your laptop you know you need like a rack to serve one model.
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>> Um you know again that's that's those are just the big models right like you're also going to have tons of small models. Um but I don't know the big models are getting bigger which only require more memory um and more compute to run.
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>> I used to say they were capping out. I used to say like the max size is one to two and then like we would just optimize from there. I've been wrong.
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>> What's interesting is it does feel like the open weight models are capping more around like a 100 billion active parameters.
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>> That's right. Yeah. It's mostly like an infant speed thing. Totally.
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>> But like I think it's actually really interestingly tied to the generations of GPUs that they have, right? like you wait for the Ver Rubins to to roll out and like
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>> whatever China has and and well then then they'll get it you know like then so they're really memory constrained at the infra layer and that actually shapes the model architecture which is like kind of cool
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>> totally.
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>> Um oh so that is anthropic uh
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>> powerhouse tour. Congrats. I don't know how you felt coming into this but you you did great.
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>> Oh this is fun. Uh, and like obviously more to do and more to analyze, but I just definitely wanted on the podcast to do this
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>> because I I know you.
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>> Yeah, of course. It's been fun.
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>> And like when else you've been meaning to do this for a while when else we going to get it? Yeah, exactly. Exactly. Um, any other commentary? When you don't spend time on, you know, the labs, what do you what are you spending time on?
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Ooh man, it's um again in a world that's highly unknown and unknowable and uncertain, I kind of fall back to what are my core beliefs that I do have conviction in and that that is the world's going to consume more models. World's going to consume more power. World's going to consume more comput, networking, and storage. And we're going to need to build a lot more data centers to serve the intelligence we're building. Those are kind of like my six core truths. And so like a lot of what I'm looking for are what are businesses that are a direct derivative of one of those one of those core beliefs that I have.
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>> Yeah. And any any other expressions of those core beliefs that you've uh done recently?
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>> Done recently. Um yeah, certainly. Um there's a chip company still in stealth. That's super exciting. Maybe we can talk about later. Um there's, you know, power company American terowatt. They want to build a new grid, an industrial grid to serve industrial offtake. Big huge bold vision. Um you know, we talked about Allorean and Glimpse and kind of the visual thinking.
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>> Anything on the comput side? Uh so effectively like slicing out compute uh in order to increase the efficiency
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>> trading futures trading futures on comput all
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>> I haven't I I haven't spent as much time there. Yeah
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>> I haven't spent as much time there.
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>> Yeah you know um we spend some time with an who's doing national comput and and AMP grid and and SF compute and all those those guys. uh in in New York we launched the comput trading track where we're we're like mostly it's just to increase utilization is the broader goal
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>> but that's how you can magically create a whole bunch more compute which is
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>> better use the ones we already have
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>> totally
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>> uh and so you know if the MFU whatever choice of utilization is at 30 to 40% right now taking up to 60% you've doubled the number of GPUs you have
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>> yeah totally so I mean I think there's going to be so much cool innovation it's why like
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>> this is the most exciting time to be in tech, to be investing, to be around all these startups cuz like you're just you're seeing innovation at the silicon layer, you're seeing innovation at the capital markets layer, you're seeing innovation at the product. I mean, like it's like every creative destruction everywhere.
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>> I notice you're not doing much on the application layer,
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>> you know. Look, I I would say
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>> the agent layer.
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>> No, c certainly are. Again, like I I I mean, I'm speaking more personally than necessarily on the the altimeter behalf. um want to believe there's a strong product research or combo. Um there's certainly a lot of great teams and we think you know early on in market cycles you know we kind of use these like triangle analogies where you have um compute or infra like like hardware infra um software infra software apps and in a mature market the biggest the biggest TAM is the apps and you know the smallest TAM is the the hardware um early on in cycles it's inverted and you know the biggest markets are at the at the at the well both top or bottom of the triangle more of the the physical hardware and then the infrastructure and then the apps and like eventually the triangles invert and so hey at some point in the future I think the apps and the agents will be the biggest part of the TAM we're just early on and there's lots of opportunity in the other parts of
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>> objectively that is correct yeah I I I do feel like and and by the way you can see various versions of this in all of computer history right down to like the uh western micro like the like when hard disks were were getting built out or whatever. Anyway, so I I I don't think those are the best examples. Western digital.
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>> Western digital, right?
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>> Yeah. Yeah.
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>> Um anyway, so um there uh there Yeah, I I I think that is true, but like to me domain specificity is very interesting and to me the app layer, the agent layer is supposed to capture that.
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>> Yeah.
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>> They they're just like the biggest outcome here is cursor right now and that's 60 billion. Yeah,
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>> we're here talking about two trillion. Very very different.
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>> Yeah, totally. Totally. I think I also just like skew a little bit more infra than Yeah, you know, just personal
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>> bias. Yeah. Yeah, we we we're in for guys at heart.
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>> Yeah, exactly.
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>> Uh okay, great. Um my closing question was going to be favorite um Brad story. I mean Brad's a character.
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>> Yeah.
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>> Uh and you've worked with him for like I don't know three, four years now.
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>> Six years.
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>> Six years. Okay. So So cloud judgement ends. Okay. All right.
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>> Brad run around. Yeah. Yeah. I I feel like I'm getting my time either my timeline is wrong or I met you way earlier than I thought I met you.
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>> No, I think we met earlier cuz I I was I think I was still at Redpoint.
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>> Oh my god. Okay. All right. All right.
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>> Yeah. You Where were you? Netifi. No.
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>> Uh Airbyte. Uh
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>> no. No. Before that.
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>> Before that was Temporal.
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>> Before that.
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>> Nellifi.
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>> That's when we met.
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>> Really?
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>> Yeah.
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>> Jesus. That's that's ancient history.
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>> Yeah.
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>> Uh anyway, B's a character. I I just think he's he's interesting and like what's what's a good story you have?
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>> Oh. Oh, man. You put me on the spot. Yeah. Um, honestly, I would say favorite Brad's story just how much he's worked at invest America like and I don't know if people know this initiative. It is the idea of like how can we get people invested in capitalism early on in their life. I think like right now in the world right you mean you survey you know America you know most people have a negative view of capitalism because they haven't participated in it and like a lot of people view capitalism as a great thing and it's the people who have participated in the equity upside of America and have kind of compounded um into capitalism but most people don't have that luxury and so how can we educate people with firsthand experience about the benefits of capitalism by helping them participate early on the whole idea is an investment account funded by the US government at birth. That can be funded by anyone along
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the way. That could be philanthropists like like Michael Dell um who made a $6 billion uh donation. I think Elon Musk has pledged one share to every kid in America which is you I think $10 billion
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>> and has some version of like the public benefit fund or something.
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>> Exactly. And so I I think just seeing him work on that behind the scenes for years um and kind of seeing it come to fruition all very quickly. You know, it's the classic overnight success. And so there's not like one specific story. It's more of just seeing the grind that goes on. Yeah. Exactly.
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>> And is it done by the way? I actually don't know that.
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>> So it's passed. Yes, it's passed in the law.
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>> It's passed. So So what? Like if I have a kid now, they get
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>> you open an account. Yeah. Yeah. You open an account. Um any new kid born gets $1,000 of birth. Um parents can contribute to it. Employers can contribute to it. Um friends can contribute to it.
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>> It's like it's like a baby 401k.
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>> Exactly. [laughter] Exactly. And you know, you have people like Michael Dell who say, "Hey, here's my donation, and I want it to be gifted to I can't remember the exact specifics, but I think it was like, hey, bottom 75% uh income by zip code." And the money just
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>> That's better. Yeah.
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>> So, Exactly. The money just cuz, you know, as Brad would say, look, what's 529 plans, which are kind of education plans, like, hey, I can fund money that can be used to for my kids education one day. Like the reality is is the people using 529 plans are generally like the wealthiest part of the country. Like 529 plans and the tax advantage wasn't made for the wealthy part of the country, but that's who uses it the most. Uh what he wanted to do was, hey, let me create investment accounts for the people who actually need them. Um and I think it's, you know, it's an awesome initiative that's just getting started. I think you're going to see a lot more direct philanthropy. What's a big issue people have with philanthropy is well you're going to donate a bunch of money and then middlemen are going to take half of it.
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>> I have seen literally directly this tons of
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>> loss. It's used for corruption like
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>> Exactly. But what's better direct contributions direct to bank account of people who need it most. No middleman direct accounts that then people can see appreciate in value over time. Can't touch it till you're 18 years old. Um and so you get to see the appreciation happen over time. maybe learn and and participate in in the upside of America along the way. Um, and I think it's it's it's really like it it's caught on and just seeing how passionate he is about it. Um, has been kind of inspiring to see.
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>> Okay. My one push back is like I'm, you know, homeless on the streets.
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>> Yeah.
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>> Food stamps, everything.
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>> Yeah.
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>> I have $1,000 sitting there.
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>> Yeah. Yeah.
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>> You then like Well, give it to me.
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>> Yeah. So, yeah. you. [laughter]
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>> Well, it's made it's made more for kids now and so hopefully you're not well there there are plenty of homeless kids.
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>> Exactly. Right. Like so so like that that's the thing like it's like well you know did you know you have a bank account and you're participating growth for capitalism.
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>> That is the biggest thing.
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>> I don't care you know like
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>> so that that's the biggest thing now which is um people need to claim their accounts. The government opened up accounts for every kid in America
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>> and parents need to claim them. It it is sitting there now. The money is sitting there for newborns. Um, you know, if you're 15 years old, money doesn't show up in your account. I think donations will. And so, you could be a 15-year-old that does have a $250 donation for Michael Dell sitting in your account. Um, but they need to be claimed. And so, like now the I would say the focus has gone from let's get this passed into law. Let's get it up and running. Let's get the infrastructure set up so people can donate.
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>> Now, it's let's go convert those created account to claimed account. And that's just a that's like a grassroots word of mouth game because there are people whose accounts are sitting there with money in it and it just claim Yeah.
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>> Yeah.
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>> Yeah. By the way, most people have money sitting in the government that has not been unclaimed. You can look it up.
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>> Yeah. Unclaimed property. It's like a great use case for like have an agent go look up your name. Exactly.
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>> Uh you probably have a few thousand dollar sitting somewhere that someone returned to you and it's just
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>> you just need to know your address, your prior addresses and then go have instinct or muse. highest ROI on this podcast is that not not the anthropic.
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>> Exa Exactly. Exactly.
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>> Um, cool. Thank you so much for spending your time with us. This is great. Uh, we'll have you back. Uh, we'll see how the IPO goes.
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>> This was fun. Thanks for doing it.
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>> Thank you.
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