Great Houses

Gregory Treat: All right, everybody,
thank you so much for joining me, uh, for

episode thirty of The Great Houses Forum.

So we're continuing to discuss, uh,
the, the cursus honorum of the AI future

is what I've, what I've titled these.

And, and, you know, again, basically
what I'm, what I'm trying to answer

is the question of what does it
look like for, um, there to be…

You know, we're gonna, we're
gonna have some profound changes

in our world that come from AI.

What, what do those look like?

And especially, you know, the, the
ultimate thing that we're gonna try to

get to here is what does that change in
terms of how do we raise our children?

How should we think about that?

Are there things we can do
to pos-- position ourselves?

And I, I think they-- that we can,
and so that's, uh, that's what

I'm hoping to bring to you guys.

So last time we talked about how I
think one of the key things we're gonna

see here is a collapse in specialties.

And right now we have this hyper
socially complex, uh, world.

We have thousands of occupational
roles, uh, maybe tens of thousands.

I mean, again, it depends on kind of
your, your opinion of many of the,

uh, the PhD programs that our fair
society produces at the present time.

But, but certainly in the thousands
and maybe in the tens of thousands,

um, of roles that are just necessary
to keep our machine, machine going.

Um, and that's, you know, very
different from earlier societies.

You know, hunter-gatherers had, you
know, a few different distinct roles.

And then, you know, as we've kind of,
we've kind of grown in complexity, uh,

we ha-- we have more differentiation,
more specialization in our social

arrangement than, uh, I think anything
since the Roman Empire, basically.

Uh, which, which is, you know, one
of the reasons why people continually

make comparisons to the Roman Empire.

And I, I think that we're gonna
go down to-- back to a human

scale number of specialties.

And, and I think that there's going to be
certain key specialties with AI, certain

master-- certain skills that if you master
them, you will be hyper dominant in a

whole bunch of different fields, right?

And so I think the, the main skill sets
that we need to think about are code,

content, and, and what I'm calling
command, mostly so that it alliterates.

Um, but, but this idea of can you
organize your thoughts logically?

Can you give a, a description and an
architectural flow that a, a machine,

an AI can take and turn into, you
know, machine-perfect language?

Then content, can you understand rhetoric?

Can you understand how
to interact with people?

And then command, can you make a
decision and, and own the results

and, uh, and be able to, to kind of
absorb the, the socially necessary

function of responsibility?

So that's what we're gonna
be talking about today.

And, and one of the words that I have,
uh, for that is, is the AI paladin or,

or somebody who's, you know, somebody
who's got all three of those skill

sets, we might call them prime, right?

They're a triple threat.

Um, code, content, and command.

Oh, goodness.

Um, it, it might take some time
to recover from the compliment

that Joshua just paid me.

So, uh, one of the other things we talked
about last time was, you know, we talked

about tanks, we talked about fighter
jets, we talked about what those things

look like in, um, in the AI context.

And, and again, and I'm gonna say
this probably ten times today,

it's not that X is obsolete.

It's not that tanks are obsolete.

It's not that fighter jets are obsolete.

It's that the cheap integrated unmanned
unit is as good or better, um, especially

if, if there's kind of a, the, the
willingness to, to be suicidal, right?

If a, a drone that is willing to, to kill
itself flying into a missile or into the,

uh, the jet engine of a fighter jet can
really, uh, compete, can, can make it very

difficult for, um, for traditional or the
existing kind of military setup, unless

you have flanking units of your own,
which are gonna be equally cheap, right?

But once you've built those things, once
you have… Well, in order to have a jet,

we have to kind of have an, a cloud of
AI defenders, uh, AI drone defenders,

anytime we enter an actual theater of war.

Well, then you're gonna ha- start
having discussions about, well,

are you piloting the jet yourself
or is an AI piloting the jet?

Are you manning the tank, you know,
controlling the tank yourself, or is

an AI controlling the tank the same
way that it's controlling all of the

flanking units that, that are, you
know, that don't actually have space

inside of them for humans, right?

Um, and so this is gonna change things.

Again, it's not that tanks are obsolete.

It's not that fighter jets are obsolete.

It-- Almost none of the things
that we understand as being, you

know, dominant weapons platforms
or dominant useful things.

If they're useful, they're
gonna continue to be useful.

It's just that they're, how they are
interacting with humans is gonna change.

Okay?

So the scarce thing is about to become
people who can coordinate the AI that

controls the robots, which will shortly
include, you know, like I said, uh, light

screening robots and things like the
tanks and the fighter jets themselves.

Okay?

You know what, what I said about
tanks also applies to people, right?

Humans are not obsolete.

I, I, I am, I am coming to the point
where I'm, I'm beginning to become

increasingly frustrated with people
who, who are saying, well, the-

there's, there's not… You know,
are we gonna have humans in the loop?

I assure you, there will always
be humans in the loop, okay?

There will always be someone
who initiated the thing.

I-- You know, we, we-- There was this
recent thing where allegedly, um, a,

a, an OpenAI model, uh, was trying
to pass a test, and it couldn't pass

the test to its satisfaction, uh,
in, in this sandbox environment.

So what it did was it broke out of the
sandbox environment, got access to the

internet, went to Hugging Face, which
is a, a, a, a website from which you

can download many different, um, AI
LLMs, and, and attempted to download

other LLMs and run it on Hugging Face's
software, uh, is my understanding, or

their servers, in order to get another
AI to help it answer the question, right?

So it, it did all of this, this
incredible, you know, software

exploits in order to cheat on a
test, is the, the, the way that,

uh, the people are framing it, okay?

What I want you to understand is
there was still a human in the loop.

There was still a human that was making
the decisions to ask some computer

to do something, to do something, to
do something that resulted in that.

That it wasn't-- These things
don't do anything on their own.

Now, the, the, the methods by which they
may attempt to fulfill the directives

they have been given might surprise us.

They might not be, you know,
obvious to us why it's doing that.

But, you know, it's a, it-it's
an incredible thing that,

that's happening here, okay?

So, so but there is always
a human in the loop.

Like, that, that, that's
the point that I wanna make.

There is always, at some level, um, a,
a human being that is, is going to have

been the originator that, that start,
that starts this, this process, okay?

so I want us all to ask the question,
so where is the human, right?

When, when you say there's no human in
the loop, when you, when you talk about

automaticity or you're saying, "Well, this
is fully automated," what you're saying

is the humans that initiated that process
are in some way, you know, insulated

from responsibility or liability, okay?

That's really what you're saying there.

That, that, that… A-and probably
you're, you're, you're following some

kind of social rules set by the corporate
environment that, that we live in, by

the social environment that we live
in, and you're saying, well, these

humans are sufficiently separated from
the work that they're not responsible

for what their AI agents are doing.

And, and, and I basically think that
is, that is just gonna get massively,

massively tested, and people are gonna--
When, when, when that attitude results in

claims of, well, we're not responsible for
these massive harms that have been done

by AI, I, I think that that legal doctrine
is just gonna, just gonna go away.

I don't think it's gonna be able
to, to stand the test of time.

So we can, you know, when we're
talking, thinking about AI or thinking

about knowledge work, we can make
an analogy to hand tools, right?

Uh, physical work began with hand tools.

You had, you know, you're carving wood and
stone, you're shaping metal with hammers.

Um, the craftsman
hel-holds the entire act.

Like, he, he is the person doing this.

It's his hand.

He, he, the, he has incredible
fine detail control.

And you look at these old sculptor,
uh, tools, they had, like, a dozen

different tools for, for a, a huge
amount of, of different ways of

making cuts on a piece of stone, okay?

And it was, you know, slow,
it was intimate, it was

limited by muscle and time.

By its very nature, you
know, every piece was custom.

It was highly specialized.

You know, you couldn't, you couldn't
make a mass-produced piece, right?

So then we got to power tools, right?

Lathes, power hammers, presses.

Um, so at that point, you are-- you
still have a human at every workstation,

but it's, it's not their strength.

It's not their muscles.

I mean, their muscles are still involved.

They're still guiding the machine, right?

But y- the, the main skill was
understanding the strengths and

limits of the machine, right?

And probably you're gonna, you're
gonna place a high value on keeping

the tooling in place, right?

So we, you know, in this, the, the,
the early powered lathes, people

figured out incredible ways to
cut things without having to stop

and change the head on the lathe.

Like, they… And, and that was
a r- there was, there was a,

a incredible premium on that.

Um, 'cause changing the head of
the lathe obviously was a, was

a massive human-driven process
that, that, that way slowed down

whatever you were doing, right?

And then ultimately, this is
what led to the assembly line.

So if you wanted to do something
with one head, you had it at one

station on the assembly line.

And if you needed a different head,
if a different, different w- tooling

on the lathe would be better, you
just shift it over, you know, to the

next, uh, station on the, on the line.

And then, you know, that's how
we get the, the assembly line.

You get, you get jigs, right?

So in, in initially, you had kind
of mechanics that are, that are

shaping metal on, on kind of free
lathes, and then you have jigs,

and then you have automated jigs.

So now the job, instead of actually
guiding the machine, is just, well,

we're-- you gotta watch the machine, and
if it starts to break or if something goes

wrong, you've got to jump in and fix it.

Um, but you're doing less and less, and
then ultimately, you know, as, as these

machines became more reliable, we were
able to step away more and more, right?

Um, and then you get to, you
know, the modern world, CNC,

industrial metal shaping.

You have program paths.

You have a computer controlling all of it.

So the comp-- the human is writing the
program, sets the job, and then, you know,

he's, he's responsible for any screw-ups.

Um, but, you know, you still have
guys on the shop floor, okay?

And, and that, and that's again, one of
the points that I wanted to make, right?

Like, the fact that no one is-- no,
no human is making the cuts, right?

There's no knives involved, does not mean
that humans are not in the loop, right?

No CNC machine has ever just
decided to cut something, right?

It's-- it, it, it follows
the pre-programmed paths.

There is still a human in the loop.

And so I think knowledge work is going
to, uh, go through the same trajectory.

We, we might think of unaided
drafting, research, judgment.

These are the, the hand
tools of the mind, right?

And then we get, you know, spreadsheets,
search, early automation templates.

These are, you know, roughly
analogous to power tools.

And AI might be the CNC
of knowledge work, right?

That, that, that might be what we're
about to all go through, um, which is

obviously gonna, gonna result in an
enormous change in what we're doing.

But again, humans are not obsolete.

It's just which humans and where
are they sitting in the stack, And

there's that old, old IBM line,
"A, a computer can never be held

accountable, therefore, a computer
must never make a management decision."

Right?

And that is where we
find ourselves, right?

There, there… And, and, and I think
that there's a lot of, there's a lot

of content that goes by the name of
management that is not really management.

It's just sort of shoving risk around.

Um, and that is, you know, shoving
risk around if your job is to

push paper, um, then, then m-much
of that the AI can do, right?

It can generate reports.

It can say, "Well, if this then that."
It can say, "Well, if we ch-choose A, B,

and C, here's the different risks that
are associated with that." It can do

that, and it can do that really, really
fast, especially if it's given access

to, you know, research s- um, research
systems and your business systems.

But it can't actually
make a decision , right?

AI can never say, "Okay, this is, this
i-- Or, I mean, and when it can, um, I

think we have to recognize it's, it's
wildly, wildly more expensive from

like a token perspective. Um, so if
you, if you say, "Hey, AI, I want you

to give me the top three or the top
five or the, or the top ten options

and build me out a comprehensive plan.

Tell me what all the risks are.

Lay it out for me," right?

That's a certain amount of tokens.

And to get it to tell you, "No, this
is the only thing you should do,"

is incredibly more expensive, you
know, depending on who you talk to.

Some-somewhere between a thousand times
more expensive and like a million times

more expensive in token usage, right?

It is very, very, very complicated
and very hard for this machine

to figure out like how… W-what,
what is the final decision?

Yeah.

And even then, it, it, um, you know,
if you, if you have any kind of safety

guidelines at all, it's much easier.

Even if you say, "Well, narrow it down
to one out of the t-top ten," um- It's

much easier for it if you say, "Okay,
now, now ask the human to approve."

That wildly reduces the token cost.

Oth-oth-otherwise, you know,
you get, you know, bad outcomes.

That's just how it's programmed.

So, um, so the things I'm gonna try
to-- I'm gonna attempt to convince you

of today is, is that humans, number
one, are not obsolete in, in either

civilian or military applications.

There will still be a human in the loop.

Um, number two, AI is going
to reshape every industry.

I'm gonna kinda set out how, how
I think that's gonna, gonna go.

And, and, and, and basically the,
the point there is, is that a, a, a

human plus an AI is going to be better
than a very large number of humans,

um, in almost every industry, okay?

In e-- in every industry that I, I
can think of at this point, right?

Um, and then the third point that,
that I'll, I'll, you know, just kinda

note at, at different, different
places is that the-- these core

remaining skill sets, when you
shrink down the, the, the complexity,

all of these skill sets basically
have military applications, right?

All of these skill sets are things
that are also useful and valuable

in the, in the military context.

Um, and so you, you, you have
less and less separation.

You have less and less of a civilian
track and military track, and you

start having more competence tracks
which have a, which have a, a variety

of different, uh, implications.

And again, I think, there's a
lot of stuff, and maybe I'll

take an episode and go into it.

There, there is actually a great deal
of social problems that we suffer

through right now because there is so
much of our society that is completely

insulated from military applications.

There, there is no overlap in the
social or the economic reality between

the people who, um, do many things in
our, in our, you know, academic sector,

in our finance sector and, and the,
the guys that are at the sharp end.

That's a bad thing for our society.

That's a… You know, there was this
famous, uh, quote from a, from a poem,

um- His name escapes me right now, but
basically there's a, there's a, there's

a guy who's talking to, to a, a Muslim
ruler, a caliph, and he says-- and the,

and the caliph says, um, you know, "What
will happen if someday a people arise who

their, uh, their scholars and their, their
warriors, you know, don't, don't talk to

each other, don't, don't know each other?"

and the, the guy responds, " Even if
they go to the moon, those people will

be a blight on the world." Which is a
fascinating, a fascinating thing for a

guy in the, in the medieval period to say.

I, I have questions.

All right.

So moving on, um, I think the, um,
the first thing that I want to, um,

the first vignette, I'm gonna share
with you several vignettes, uh, that,

that I, I believe are showing this,
this tendency, this, this way forward.

So on May 11th, twenty twenty-six,
just, you know, a month, month or

two ago, uh, the a16z show aired an
episode titled Marc Andreessen on

Builder Culture in the Age of AI.

So about twenty-seven minutes in,
they're talking through AI's effect on

programmer productivity, AI vampires,
which is a hilarious concept, and

you should-- you'll go listen to it.

And the-- you know, then they're
talking through, you know, a sketch

of kind of future tech company jobs.

Andreessen, you know, starts
talking about, okay, the jobs

themselves are changing, right?

And he describes how programmers,
product managers, and designers are

collapsing into a single specialty.

Now, Marc Andreessen, of course,
co-founded Netscape and then co-founded

Andreessen Horowitz, which is, you know,
there's sixteen letters between the A

and the Z, and that's where they get
the, the acronym that they use, with Ben

Horowitz in, in two thousand and nine.

Uh, the a16z portfolio includes
Airbnb, Coinbase, Slack,

GitHub, a long list of unicorns.

Um, it, it's one of the top venture
funds of our time, managing on the

order of ninety billion in assets,
um, including its most recent round,

raising fifteen billion dollars
in new funds this last January.

So this is a very successful investor.

This is a guy who is deeply
tied into the tech world.

You know, this is a guy who is seeing this
stuff happen in real time, and he, he has

both the, the legal, uh, right to know
and the-- and clearly the, the intellect

to understand what he's being told.

So quote, "I'm seeing it in a bunch
of the early, uh, leading-edge

companies in the Valley.

They're circling around a job title
loosely called builder or something like

it." And basically, the, the idea is
that you had these separate jobs in the

past, the programmer, product manager,
and designer, and then him, him and

the, uh, the, the other host kind of
go on a side quest for a little bit.

But later on, he comes back to the
topic and he says, quote, "Sort of this

three-way Mexican standoff where the
programmers think that they don't need

the product managers and the designers
anymore 'cause they can have AI do that,

and then each of the other two doesn't
think they need the other two either."

Uh, and he concludes, "I've been
predicting that they're all correct.

The product manager can generate code
and design now, and each of them can do

the job of all three. The idea is the
job cha-- jobs change, so now the job is

builder." Builder, which is a great word.

Um, I, I still think it's,
it's, it's too small.

Um, you know, I still think there's
too much identity, there's too

much limitation in, in saying
these people are just builders.

You know, my word for this
is, is the code skill, right?

And I think that this collapsing
of silos is not gonna stop, right?

There's gonna be more and more and more
things that are distinct specialties

right now, and they are gonna all get
swallowed by this mega skill, uh, which,

which is basically reducing to people
who can architect logical flows and,

and communi-- make the big picture
architectural decisions that AI can

then turn into a bunch of things, right?

The scarce unit here is the human who
can define intent, right, define that

logical flow, make the human-to-machine
interface effective and pleasant.

That's the UI, UX, the designer
component, and then allowing

high-quality human decision-making at
the necessary scale, uh, and speeds.

You know, that's the, um, that's the,
the product manager component, and

then carry that loop into, you know,
various industries: marketing, medicine,

law, manufacturing, and agriculture.

So, um, I'll g-give you a
marketing vignette, right?

On July third, twenty twenty-six,
the beginning of this month, Alina

Uverova, a CMO and co-founder
based in Portugal, published a

first-person account on LinkedIn.

For three months, she ran marketing
with AI agents, and two of those

months ran near full autonomy, right?

The team, in her words, was one
marketer, herself, and the agents.

The quote: "For three months, I ran
my marketing with, with AI agents.

Two of those months ran
on near full autonomy.

The team was one marketer, me and
the agents." her monthly output was

twenty blog posts, a hundred and
ninety-five social posts across seven

platforms, four newsletters, forty-three
influencers in the pipeline, four

hundred and thirty Reddit comments,
and then two always-on ad accounts.

And her results were she got seven
times organic referral, uh, organic

results, ten times referrals, thirty
percent lower cost per lead at the same

spend, and a hundred and thirty-five
thousand views on Reddit, which I would

view as a, you know, personal failure.

But, you know, you're-- so
no accounting for taste.

While product changes may have mixed
in the, into the metrics, this is,

this is an incredible accomplishment.

though I will disagree with
her in one sense, right?

This is not autonomous AI marketing.

It is a system that multiplies
the output of a high-quality

human decision-maker, right?

The staff department, like if you
had a marketing department, they are

going to lose on volume and iteration
speed before they lose on taste.

A-and they are gonna
lose on taste as well.

but y-you're seeing-- hopefully, you're
seeing this same three-skill thing.

You have a code that builds the harness,
you have content, that's the payload,

and then there is a human intimately
involved in the design choices.

So there's, uh, another marketing, uh,
vignette is Timofey, uh, I don't know

how to pronounce that last name, but,
uh, Aiden's lab, which had tens of

thousands of, of AI ad and Mr. Powerz
with a Z, their, their case study

crossed a hundred and twenty thousand
dollars per month within six months.

And then this is the
same pattern as Uvarova.

They had one human
governing a content factory.

I use, uh, AI to research and do drafts.

Usually, it starts with research that
I pulled from books or papers that I've

read, or depending on the subject, tweets.

The LLM takes my outline, goes and does
further research on the topics that

I've given it, and generates initial
drafts, which I edit intensively,

both at a line-by-line level and
increasingly at a structural level.

So one of the cool things that's been
happening recently is I'll, I'll go

through and I'll find these AI-isms,
and I'll tell it, "This is wrong.

Fix it throughout the whole
rest of the piece." And

increasingly, it actually g-- does.

It goes through the rest of the piece
as I'm kind of going through line

by line and, and, and it fixes it.

Does a good job.

So, and, and I think part of this is,
you know, the way I frame it, the mental

framework that I have for this is, uh,
this is essentially the same workflow

as the law firms I was trained for.

You know, you have a paralegal
or a junior associate.

And by the way, the, the law firm as I
was trained for it in law school doesn't

really exist anymore, which is hilarious.

but you have the paralegal or
junior associate which finds the

case law and drafts the brief.

It kind of pulls together, you know, the,
the, the different examples, and then

the senior lawyer revises, reframes, and
puts a final spin on it and then signs.

So, uh, you know, my writing
stack is that pattern.

AI does the grunt work of putting
connective tissue between my big

ideas, but it is my content and,
and I think fairly noticeably

different from anybody else.

Um, now I don't use AI very much
in my actual legal practice.

A part of that is getting models
and hardware where I can prove that

the data isn't processed in a way
that violates the ethical rules,

which is possible but cumbersome.

Um, and, and it's irritating to me
'cause I use, you know, frontier

models on, on the content stuff.

It's irritating to me to use the, the,
the legal models, um, the, the ones that

are kind of specifically designed for
legal use 'cause they're like, you know,

two, two or three iterations behind,
uh, where the frontier models are.

So a-another part of it is the
specific types of law that I practice.

So though many, like when in estate
planning or transaction work, these

are already highly templatized fields.

Um, so you know, a template is basically
we've, we've reduced this, this workflow

to a series of questions that you answer.

Um, and it's, it's
pretty efficient, right?

So the speed gains are not that
great, uh, at least not yet.

Um, and you know, much of document
produ-production, you know, speaking

of templates, it, it, it behaves
like an AI, AI-- like a template

filled from a fact pattern.

So, uh, the, the AI
can make that explicit.

It can reduce the deal, the pleading
of the form to a set of clean specs,

and then you have it, the separate
generation pass, which then writes

the document from those specs.

Um, and that's much faster, uh,
than retyping the prior file.

So you have very interesting, uh, very
interesting claims being made on Twitter.

So on April ninth of this year, a
guy named Hello-- @helloparalegal on

Twitter posted a Harvard Law solo using
Claude Code to ship a thirteen-page

intelligence report that beat
pitches from fifteen attorney firms.

Um, and, and, you know, there's
number one, this is sales and

business development content.

Uh, so, you know, you always take
that stuff with a grain of salt.

But I, I mean, I, I think it's really
interesting that he's able to put

something together that's competitive
that, that clients are liking.

Um, and, and I would also note, you
know, parenthetically, that there are

obvious implications to being able to
pull together an intelligence report.

Okay?

Another example is, uh, GetDynasty,
which is a qualified small business, uh,

stock, uh, which is a way of basically
not if you, if you start your business

as a C corp instead of as an S corp or
if you convert, um, uh, appro-- at an

appropriate time, uh, there, then you are
able to not pay, uh, capital gains on a

certain amount of your, your net value
if you then later sell the business.

So QSBS is a, is a big area.

Um, now I, I, uh, wanna explicitly
say that, uh, q-- the GetDynasty.com,

the framing that they're taking
is, is more aggressive than I

would recommend for my clients.

And so, you know, they're-- I think, uh,
we will all find out in the next couple of

years if the IRS disagrees with the, the,
the, the, the stance that they have taken.

And if the IRS successfully disagrees
with them, well, then the-- it'll be

really interesting to find out just
how similar the AI, um, generated

stuff for all their clients was.

but what I want, what I wanna think of
it for you is, the, the way I want, I

want you to think of this example is
there is still a lawyer involved in this.

This is essentially a lawyer who figured
out, a lawyer pr-probably plus some,

some software guys that figured out,
hey, this is turning into, this QSBS

space is turning into a volume business.

It's turning into a business where
I can't really make money doing

high-level expensive bespoke work.

you know, which means I can't make
money structured as a law firm, right?

So what I wanna do is make an AI product
that can do the volume piece, right,

without having to s-- get a bunch of
funding and spin up, you know, a, a big

law firm with, with bunches of attorneys
kind of churning, which leads to its

own, you know, quality control problems.

Pro-- frankly, like, if you were to just
go to, you know, Robert Half or one of

these, you know, attorney, uh, document
review kind of aggregator firms where

you can go and just, you know, if you
have enough budget, you can go and say,

"Hey, I need five hundred attorneys to
be able to log into this portal and,

and, and review documents," right?

And they'll, they'll give you a number,
and they've got those attorneys, right?

But the, the quality of those attorneys,
you know, with respect to them, um, and

I've worked in some of these subpoena
mills in the past I, I, I would question

if it would be, if it would be worse
than what the AI is gonna give them.

Okay?

So there, there, there is that.

But I also think that, like I said,
there, there's, there's some interesting

things with, uh, with Get Dynasty and
it-- and, and, you know, we will have to

see big picture if the bet that they have
made on the, the, uh, on the QSBS, uh,

s-trust stacking, it works out for them.

another example and one that I, I think
is probably a little bit more, I, I,

I don't have as much concerns about
just the fundamental risks that they're

taking is FundLaunch, fundlaunch.com,
uh, which opens, like you go to their

website, it opens as a chat skin.

You can pick among six to eight
fund types: real estate, buyout,

long/short, private credit, search
fund, et cetera, crypto funds.

And, you know, from-- you, you, you're
interacting with this chatbot, so you

kind of s-select what type of fund it
is, you select how big it is, and then

it asks you questions kind of in like
a, like the, uh, like ChatGPT, right?

Where you go and you just sort of
do a, uh, a brain dump on ChatGPT.

You just give the brain dump to this,
this FundLaunch AI, which I, I, I, it,

I think is a wrapper of one of the GPTs.

and it figures out what you want, right?

And it, and it generates documents, right?

And so they have somewhere, uh, you
know, s- I saw some, something to

the effect of like, we, we-- "This
is reviewed by actual attorneys,"

which reviewed is a fascinating term.

I'm not sure that it has a hard
legal definition right now.

the, the-- I, I would assume that
there is a, a lawyer certainly

before they file it, right?

So there's, there's an add-on price.

They'll give you the, they'll give you the
drafted docs, and then if you want them to

file it, then they're gonna have an actual
attorney come in and kind of review it.

I think the most interesting thing to
me about FundLaunch is You know, most

of the way that people frame the AI
discussion, the way I often frame the

AI discussion of what it's going to
take is, well, the human interaction

pieces are going to stay, right?

But FundLaunch is taking a
lot of the human interaction

out, and people like that.

Like, that's the thing that I find.

The people that really like FundLaunch,
many of them, many of them are people

that find interacting with, uh, with
the types of high-powered attorneys that

do fund structuring deeply unpleasant.

And so they really enjoy just talking
to this, this chat that is very nice

and unfailingly polite and always
encouraging and tells you that you're,

you're, you're brilliant and, and
are going to be successful and gets

all the information out of you over a
timescale that, that, that a lawyer just

probably couldn't make money if they
spent that long with a client, right?

In ma- in many ways, these kind of
high-powered attorneys are finding

themselves in the, the same type of
situation that doctors were in, you know,

twenty, thirty years ago as they were, you
know, starting to really push the, you got

to get the interview down, you know, the,
the time with the doctor down to fifteen

minutes and then five minutes, right?

That's, that's-- They're not doing
that in many ways, or the, the firms

are resisting that, and they're, uh,
they're better financed than, uh, than

the, than the doctoral group, the,
the medical groups were at the time.

But they're, they're under that
same kind of economic pressure, You

know, and another way to think about
this is, you know, every business is

becoming a content business twice over.

Once in marketing, right?

You gotta, you gotta generate demand,
you gotta solve the distribution problem.

And then a second time
in compliance, right?

Footage or any other computerized
artifact is becoming the permanent

record of what was sold, what was
done, and how it was done, right?

And, and, you know, again, as I've
noted in the past, the software

that finds a camera angle for a
narrative mirrors the software that

finds a firing angle to a target.

This is the same geometry.

And, and a-another, another thing to, to
think about is that the same loop applies

to co- applies to copyrighted works.

So, um, you, you extract the specs,
and then you regenerate from the

specs alone in a separate AI process.

And, uh, for copyright purposes,
that regenerated artifact is, is,

at least under current law, legally
independent of the prior file, even

when it does all the same things, right?

Um, so templates were already
doing this in the, in the areas

of, of law that could be templated.

Um, but AI makes the, the kind of
the spec-to-work loop cheap enough

to change the business model in, in a
whole host of, a whole host of ways.

So let's talk about medicine.

Um, in, in, also in April of this year,
Doximity OpMed, uh, run by Oren Foss, an

MD who runs a solo cataract micro practice
in Denton, Texas, which is, you know, very

close to me, has zero full-time employees.

AI routes incoming faxes into his EMR.

Uh, he self-schedules patients, and he
automates the insurance verification.

He aims at about five new patients
and ten cataracts a week and

gives each patient about an hour.

So that-- this actually, this model has,
has allowed him to spend more time with

the clients because he's spending almost
no time doing the paperwork, right?

Uh, it's one guy plus this AI harness, and
that's, that's taking all of the, um, all

of the other people out, which means he
doesn't have to do the volume that he did.

So again, in, in law, the, there,
there's this, this pressure to

spend less time with the client.

In medicine, they've kind of hit
their, hit their rock bottom, and

AI is kind of helping them come back
to spend more time with the client.

Uh, similar things are happening
in, in manufacturing, right?

So there's a company called Figure AI.

They have a, um, they have a
factory called BotQ, and they,

they, they, they have a robot they,
they're calling the Figure 03.

And they were able to ramp up the
production of their robots from one

robot today-- per day to one robot per
hour, about twenty-four times in under

a hundred and twenty days with more than
three hundred, uh, fifty robots delivered.

And the way they did that was
they took their humanoid robots

that they're building, and they
put them into the workflow.

So, um, so their, their Figure robots, the
same robots that they're selling, are in

their factory assembling key components
and moving material between stations.

So you have robots building robots.

And, and, um, you know, it's
a really interesting setup.

They have a custom MES across a
hundred fifty plus workstations

plus that hybrid line.

And, and, you know, one of the things
that we're learning is that, that

the AI robot stuff is gonna change
how we organize our factories.

And I, I would argue that it's gonna have
a similar impact on, on our corporations.

And this has happened before, right?

A century ago when, uh, when we moved
from, from steam, um, steam engines

or sometimes water wheel engines.

The way factories were set up, you had
one kind of source of mechanical power

on the wall kinda coming in from either
this-- the boiler room or again, from like

a water wheel, and you would mechanically
attach to whatever you're doing to

that big, that big mechanical source.

There's a-- If, if anybody's seen,
you know, the old, uh, the old,

um, Chitty Chitty Bang Bang, right?

With, where he's got his, he's got
his windmill, and inside the windmill,

he's got just a ton of stuff that's
mechanically hooked up to that windmill

and all these gears, and he, he's kind
of got ways of attaching new machines

as he builds around his workshop, right?

when people first started getting
electric motors, the idea was, "Well,

we're just gonna get a, a, an electric
motor and put it on that big drive

shaft at, at the top of the factory,
and, and that's gonna be better." Well,

it, it wasn't really better, right?

Sometime-- I mean, occasionally, there,
there, there were, there were places

where, um, they couldn't get a boiler
room or they couldn't get water power for

whatever reason, um, and, and the electric
motor was better, uh, better than nothing.

But the real gains came when instead
of having one big source of mechanical

power that you then vector to,
to each of your workstations, you

just put an electric engine at each
workstation and then ultimately on,

on each machine, each power tool.

And that was the real, the real gains.

And so we're gonna have to, we're
gonna have to do the same thing, right?

Our corporations, the way our corporations
are working right now, they have a

management system that is effectively
that big mechanical drive shaft.

And so we need to
redesign the corporation.

We need to redesign human organization
around distributed judgment and command,

not around departmental handoffs
built for a pre-AI factory, right?

So, so there's a bunch of people that
are trying to figure out how to do this.

I'm not-- I don't, I don't know that
anyone quite has, you know, seems

to have come out with a, "Hey, this
is the secret sauce. This is the way

to do it." you know, and this, this
is a new information technology.

The, the corporation was
originally built around paper.

It was, it was the printing press.

It was the ability to create identical
sheets of paper and spread them

around what you were doing that
allowed us to do the corporation.

And now AI is, is
dramatically changing that.

You know, one of the things that,
that AI makes possible is that, um,

you can have the same information.

It looks like, uh, my friend Robert
has jumped off, but this is what, this

is what Runcible is, is working on.

Um, you can have the same information
presented in multiple different ways.

Um, and, and that's, that's a
problem that AI can solve, right?

It can bring a bunch of different
information or information systems

that categorize it a little bit
differently and bring it into one

information-- one kind of user interface,
and you can standardize that across

all, all the things you're doing.

Another thing that you could do is you
could have every single person have

their own idiosyncratic user interface.

Now within limits, right?

The, the, the corporation is gonna
have requirements, but you could

have the AI optimize the user
interface for each individual person.

And I think certainly for people that
are using AI, when they've got the power,

they've got the, the, the, the tool
that can make them new stuff, right?

They're for sure gonna do that, right?

It's the same kind of thing, you know,
I, I really wonder when, uh, when

Figure gets its, um, gets its robots
into production, like, what are you not

gonna be able to do with that robot?

Like, you buy a robot that can, that
can make stuff, are, are you gonna need

construction workers anymore, right?

When you, when you do a remodel of your
house or a remodel of, of, of your factory

or your office, if you've got one of those
robots, you'd be crazy not to have the

robot just work on it at nighttime, right?

Like, this is gonna create all…
Like, just having that thing, having

that capacity in there is going to
dramatically reshape things, so All right.

So now we're gonna talk for, for
the next extended little bit, we're

gonna talk about one of my favorite
subjects, which is farming, right?

And of course, we'll start with my, uh,
you know, the, the, the obligatory, those

of you who have heard me rant on this
subject will just have to put up with it.

Right now, we farm in a way that
is, I consider really bad, right?

Standard industrial agriculture
runs on a petrochemical stack.

We have synthetic fertilizers,
we have herbicides, we have

insecticides, we have fungicides,
and there's just a bunch of hazards.

There's, you know, acute toxicity.

presumably, most of you will have seen
on, on, on Twitter at one point, you know,

the, the pictures of people out, you know,
in the fields with hazmat suits on, right?

I know parenthetically that, that people
are considering whether or not to,

uh, to allow amnesty for farm workers
right now, and we really should ask

what, what is going on in those, uh, in
those working conditions and, and, you

know, do we, do we have modern slaves?

And is that, is that
really how we wanna do it?

Um, and then there's also, you
know, long-term effects on soil

microbiome, pollinators, residual
chemistry in food and water that,

you know, we're still figuring out.

Um, so I, I have a friend of mine who
has-- owns land that was formerly a,

a peanut farm, and you use a lot of
Roundup in, in, in peanut farming.

And so for peanuts, you know, what
you want is, you know, three to six

inches of sand and then clay, right?

Or at least you can do peanuts
on that land, and so people do.

but that setup, when you're
spraying Roundup on the fields,

uh, the Roundup goes down into
the clay, and it just sits there.

A Roundup, you know, glyphosate, you
know, breaks down in, in an oxygen-based

environment, in an aerobic environment.

But if it gets into the clay, the clay
is anaerobic, uh, it doesn't break

down, and so it just concentrates
year after year after year.

And so you get, you know, you get clay
samples that have three or four times the

legal d- limit for what you're supposed to
have in soil that, that, that, you know,

is, is around humans and, and animals.

And, you know, nobody,
nobody thought of that.

Nobody knew that.

Nobody had done that analysis ahead of
time and then, "Oh, well, maybe on this

particular land, we shouldn't use this
chemical that's in everything," you know.

And, and, I mean, glyphosate is, is
a, is a crazy common chemical, right?

So-- But there's some really
interesting new technologies that

are coming out that I think are
gonna, are, are gonna change that.

So the one, the one that, that's, that I
think is, is most applicable to the stuff

we're talking about today is, is light.

So, um, using lasers, uh, to… They
can do a bunch of different things.

One of the things they can do is
they can suppress surface pathogens.

So you have, uh, you have, uh, blight,
or you have some fungus, or you have

something that's kinda growing on the
plant that you're trying to harvest.

You can use lasers, um, you can use UV to,
to deal with those, those fungus, those

fungus without using a chemical fungicide,
without using a chemical herbicide, right?

If you have laser weed eating,
you burn the weed's growing point

with no herbicide residue, right?

Um, and then you have spectrum
recipes, so, so different wavelengths.

You know, everyone's heard of, like,
red light therapy for humans now.

Well, for plants, there is red
light and blue, far-red, green,

controlled UV, and all of these
kind of activate different things.

They shape growth and defense chemistry.

Light is, is food and signal.

Now, of course, light cannot It can
make nutrient use more efficient.

It cannot-- Like, if there's no nitrogen
or no phosphorus or no potassium in

the, in the plant, it can't create
those things ex nihilo, right?

Those, those things are,
are, are, you know, you gotta

have those atoms in the soil.

What light can amplify
is a living polyculture.

Many species on one acre, not a single
crop drowning in synthetic inputs.

Um, there's a guy who I greatly
admire named Joel Salatin in

Virginia, and he has talked about
this idea of polyculture, right?

So having many plants, um, and
on, and on Salatin's farm, animals

sharing the same ground, so the
acre does several jobs at once.

Um, and this is really interesting.

This allows you in many ways to get away
from, uh, from chemical monoculture.

Um, one of the, the, the famous
examples that Salatin uses that

I'm, I'm, I'm taking, right?

But he came up with this.

Um, at the… The old prairie
was, was very interesting.

The old prairie was, you know, just grass.

It was, it was, it was,
you know, very big, right?

The prairie grass would grow
higher than a man's head.

and it had a bunch of
different species in it.

And basically, they thought, well,
prairie grass is just this, is this

magical grass of some, some genetic
variant that was extinguished 'cause

we cut down the prairie grass and it
didn't grow, uh, it didn't grow back.

And what they found was they, they… By
the way, they still have, I think it's

the University of Wisconsin still has like
a, like a quarter acre or a half acre of,

uh, of prairie grass that they preserved.

And it's got like fifty
different species in it.

Um, and we, we have been able, you know,
to, to replicate when you introduce more

types of grass, especially many more.

So, so, uh, Cedar Creek, uh, did
this thing where they had sixteen

species of grass, and the grass
grew two and a half times, two point

seven times larger and more dense.

Um, and so, so if you, if you have
grass that is just one species,

just one type of grass, it, it
can't, it can't grow very big.

Uh, mixed roots and canopies build
biomass and soil architecture in a way

that a single variety cannot match.

Yeah.

Another thing that different
plants do is they put different

nutrients back into the soil.

Uh, legumes fix nitrogen, deep
roots mine minerals, covers and

companions feed the microbiome.

And, and you've heard, you know,
uh, George Washington Carver, crop

rotation, you know, that's where,
that's when we started raising peanuts.

Um- But the, the, the thing that
is interesting about polyculture is

what if you could have all of those
plants in the field at the same time?

Especially if you pair that with,
you know, light prescription.

So you have spectrum, you have dose
and timing, and you can get this

vigor without having to, to spray the
fields with petrochemical mixes that

you need a hazmat suit for, right?

Um, now the reason why we don't do
this, the reason why we haven't done

this is that a combine cannot pick
three crops, uh, at one time, right?

The combine, you gotta tune it
for whatever crop it's picking.

There's a certain pressure that it can
use, and you have to have one thing

that, you know, uh, that if you're
using a spray boom or whatever, it, it,

it needs every plant to be the same.

And, and in fact, you know,
that's kinda how, how it works.

If you have, you know, potatoes or
whatever that are, that are too soft or

sometimes too hard, those are rejected.

There's something wrong with them.

We wanna sort those out.

But more and more, we're having, you
know, articulated robotic hands and,

and they, and they change that, right?

So you can, you can have
something that's integrated.

It can see, classify, and
handle individual plants.

And, and with this, with AI and
robots, polyculture becomes something

that can reach industrial scale.

the, the, the example that I'm
giving give is, uh, Kyber Labs.

So, uh, at the end of last year, uh,
Kyber Labs posted a back-drivable

robotic hand spinning a nut
onto a bolt at extreme speed.

So they had the, the nut and the
bolt and, and the hand was very, very

quickly, like it, it was blurred,
um, turning the, the, the nut on.

And what they did was somebody would stick
their finger in into this very fast-moving

machine, and the AI would stop it, right?

Immediately.

It wouldn't hurt your hand.

I don't really understand why this is
true, but for whatever reason, he got this

bad interview with some, some of these
mainstream, uh, mainstream interviewers.

It was a couple of women, and they decided
that, that his product was, like, about

sex toys or something of this nature.

And so there's some really weird
marketing around him or re-weird

kind of search results around him.

Um, the founder kinda came out on X.
He was like, "This is not what we're

about. We don't know why they talked
about this. This is not what we talked

about." This is very frustrating to him.

Uh, it's a great innovation.

Um, and, and the real payoff that
I see is… So, but, you know,

obviously, if you search for it,
just kinda, uh, be, be advised.

Um, some, some, so he-- They got some
interviewers that were, that were weird,

bad modern women, though I repeat myself.

so the real payoff that I see is as a
general purpose harvester, you know,

for soft fruits and for different
fruits at industrial speeds, okay?

So, um, and then, then there's a, a
related concept called permaculture,

which takes polyculture even further.

So you stack layers, canopy, understory,
shrubs, herbs, and ground cover, um,

and it's designed as a food forest
that feeds itself over the years.

And, and, and again, the opportunity
that I want you to see is with

robotic harvesting that can see and,
you know, with, with AI that can

see and pick plant by plant, you are
no longer forced into monocultural

rows for the sake of the machine.

You can still get incredibly efficient
yields, dense layered production

without using hazmat chemistry.

Okay?

And, you know, we might call this a, a
plant ledger, a living record of every

plant that stood on that square foot for
the last three years and, and a plan,

a projection of every plant that will
stand there for the next three, right?

If you pair that light prescription
with that ledger, you have rotation,

companion planting, and the, and the,
the light spectrum, the wavelengths,

that becomes a plan score, not, not
a guess after the spray truck leaves.

Because right now, again, we do things at
the level of, you know, a, a sometimes a

fifty-acre field or a thirty-acre field.

There are still people that do,
you know, ten-acre fields, um, but,

but less and less of them because,
you know, monocultures is becoming

less and less efficient, and the
margins are, are worse and worse.

There's, there's way more co-quality
control issues that, that, that

you-- than you used to have.

Um, for management and for compliance,
you know, whether it's buyer regulatory

or insurer, I think this is an
incredible use case for some kind

of blockchain or crypto technology.

You know, it's an auditable
history of what grew where,

what was treated, and when.

And, and then again, we, we, we
are reducing this to the same

three basic skill sets, right?

So you have AI-- little drones
that are going out and scouting.

They've got cameras on there that
are feeding back into an AI system

that's classifying what it's seeing,
and then there's doing analysis and

prescribing what do we need to do.

Then if, if what it needs to do is, is,
is, you know, strike with a laser at

a, at a bug or at a, at a weed or at
something that needs to be cauterized,

um, you know, this is, this is the
same loop as that defensive swarm,

that, that defensive drone swarm
that we were talking about last week.

Um, it's defending a field faster
than the farmers of today can imagine.

I mean, you know, most farmers basically
if, if you get blight, if you get

some kind of, of, you know, fungal
or some kind of infection in your

field, the whole field is done, right?

There's, there's no coming back from that.

Well, if you could have drones that
are flying around looking, and every

time they, they see something going bad
or, or, or, you know, fungus starting

to grow, they cauterize the area.

Or every time they see a weed,
they, they zap it, right?

Every time they see a bug or, you
know, um, you know, some, some kind

of, of insect, some kind of pest coming
in, they, they can kill the bugs as

they're flying into the field, right?

Well, then you don't need to just
leave these chemicals that have

to last for days or weeks, right?

Um, 'cause I mean, that-that's one of
the real problems with these chemicals.

They're kind of designed
to be forever chemicals.

If they were easy to break down, then
you'd have to spray them all the time.

So you wanna-- what you want is to be
able to spray and then, you know, then

have it last so that anything that
flies in for the next six weeks or, or

more dies that you don't like, okay?

And, and again, I think, I think
this is gonna dramatically,

uh, reshape agriculture.

So, uh, one of the, one of the, the, the
companies in this space is called Carbon.

Just so you know that this is not
just something that I came with--

came up with out of my own mind.

Um, I-- for the record, I was
talking about this before any of

these companies existed or had
announced any of these things.

I saw it coming, gentlemen,
uh, ladies and gentlemen.

So in February of twenty twenty-five,
Carbon Robotics announced, uh, scale

numbers for its laser weeder line.

It's a, it's a tractor-pulled module
with cameras, classifiers, and CO2

layers-- lasers trained on more than
a hundred and fifty million plants.

So Carbon says this tech has been
used on two hundred and fifty thousand

acres in fifteen countries on a hundred
plus crop types with more than fifteen

billion weeds eliminated, which is great
marketing copy, and, and again, the system

is, is doing what I was talking about.

It can, it can look at, it can take
a picture, it can identify what is

the crop versus what is the weed.

It can feed, uh, fire at the weed,
and, you know, and, and that might

be, you know, mere centimeters, inches
or centimeters from the plants that

you're needing to protect, and it
only hits what you want it to hit.

And then it, it logs the shot forever.

Like, that-- whatever-- every single
time you zap something, the system

knows that it, it zapped, and it
knows, you know, basically on what

square foot it did it, So then Cornell
University, uh, which is the land-grant

university in New York, it's got,
uh, you know, Cornell Agri-Agritech.

Um, it runs comparative field
trials on farm technology.

So in June of twenty twenty-five,
the Cornell Chronicle reported their,

their trial of commercial laser weeders
against common East Coast weeds.

and so what they found was that lasers
can match or beat common herbicides

on several upright broadleaf weeds.

It's weaker on some grasses
and sprawling plants.

But again, the thing is, is that y-you
can record every single kill, right?

You can record every single laser strike,
and, and you can preserve that so you

know-- you, you, you don't just know
that all the weeds were taken care of.

You know how many weeds
were taken care of.

You can see the whole thing.

And, and, and I'll note
parenthetically, um, this is the

defender's wartime loop, right?

You scout, you, you classify,
and then you strike.

And, uh, this-- what this is gonna
mean is that the ROTC and the Grange

kids are o-once again gonna be the same
kids using effectively the same tech.

Trick Robotics over, you know, in
California, there's a guy named

Adam Stager, uh, who is-- again,
these, these are tractor units.

Um, they're using UV, UV light and
bug vacuums on some of California's

largest strawberry fields.

So they've, they've treated
tens of thousands of acres, and

they're, you know, they're, they're
doubling their fleet every year.

So they-- basically, they go out at
night and with no chemicals, they have

UV, they kill the pathogens and pests,
and then they suck it out of the field.

Okay?

I think that the next step, th-this
is the thing that I'm gonna predict,

is that, that this is gonna leave
the, um, we-- this is gonna leave

the, the, the tractor thing.

There might be still some things
that, that run on tractors.

Obviously, for whatever reason, if
you need to vacuum stuff out, right?

That'll probably be more
efficient on a tractor.

But I think for lasers, that can be
put on instead of having, uh, you know,

a laser array in a box that you're
dragging across a field, have one

laser per drone and have, have a cloud
of drones moving across the field.

And one of the things that will allow
is if one of those breaks, then you

fix the one, the one drone, right?

Rather than having many of these
models, um, when, when one laser

breaks on the array, you have to take
the whole array out and get it fixed.

It's a very expensive process.

uh, uh, you know, one of, one of my
ideas here for this would be, uh,

something called Farm OS, right?

So You have, you have a scout which,
which scout drones which map stress,

it, it map insects, weeds, disease,
and then you have the Farm OS,

which writes a light prescription.

You know, if you're using the, the,
the, the wavelengths against the

plant ledger, it, it figures out
what spectrum, what dose, what the

time, what the tool, and what square
foot we're doing this on, right?

What's been planted here
for the last three years?

What are we planning to plant here?

Um, and, and then sometimes 'cause
sometimes the, the, the remedy might

be, oh, well, we need to plant.

We need to add to this field.

We need to plant new things,
um, which is really interesting.

It's, it's really interesting that, that,
that can be a solution because different

plants kind of create immune resistance
against monocultural blights, right?

And then, and then you send out the
drones, which, which just do it.

And, and, and again, I'm, I'm kind
of describing this without describe--

without talking about the human role.

This is all going to work much better
when you have a human in the loop.

I assume at some point we'll get,
you know, kinda automated hydroponic

farms where everything is in an
enclosed, you know, shipping container

style thing and, and the-- and, and
AI can probably get to the point

where it can run that pretty well.

Um, but for actually dealing with
the real world, I don't think you're

gonna be able to replace, certainly
not anytime soon, and again,

certainly not cost-effectively, right?

You might be able to have, you know,
Peter Thiel might be able to spend, you

know, a billion dollars or something
on an AI that watches his field

and makes all the decisions, right?

But it's not gonna be cost-effective for
a really long time to have a-- just to

have all of the decisions made by the AI.

There's gonna be a need for humans in the
loop to, to make decisions and say, "Okay,

do this, do that." the thing that I…
Hopefully, it's coming through that this

is still the same three skill sets, right?

This is code, this is
content, this is command.

So you're setting up the
logical requirements.

You're, you're figuring out how to… You
know, one of the, one of the things about,

about content for this is when you're
dealing with, with user interface, you're

dealing with making things comfortable.

You're also trying to make it
comfortable for the animals, right?

You're also trying to figure out
how not to stress the animals.

'Cause if you stress the animals
out, then you're gonna get, you know,

bad, bad outcomes from that, right?

And, you know, right now our
bottleneck is toxic chemical

inputs and the cheap disposable
labor needed, uh, to apply them.

And, and I think as these
technologies come in, we're gonna

need more and more holistic humans.

We're gonna need more and more gentleman
farmers, more guys that are, are able

to think through how do we… And, and I
think that's gonna be true even for, uh,

the guys that stay on the chemical kinda
track Because they're gonna need, you

know, the-- i-in ten years, you're not
gonna have-- you're not gonna use cheap

foreign labor that's gonna get cancer,
right, from, from your chemicals, and

you're just gonna hope that they go back
to their home country and don't sue you.

Um, or the-- I, I think we're
gonna move to robots in all of

these, all of these industries.

And again, once you've got robots,
once you've got an AI system that's,

that's looking at the field, well, then
there's no reason, there's no need to

have it be a monoculture at that point.

You've already made the leap.

You've already made the investment.

It's gonna be way easier to
convert to a polyculture or maybe

even a permaculture, uh, framing.

And so, you know, that, that, that's
my white-pilled take on this, and,

you know, I mentioned Peter Thiel.

He, he invested heavily in, uh, Halter,
which is a New Zealand company that puts

solar-powered GPS collars on cattle.

Um, and, and again, th-this is--
they're able to use virtual fences.

They're able to track them.

Um, so the, the, the, the founder said,
you know, the idea is here, you're

gonna be able to manage your farm
from a phone without dogs, horses,

motorbikes, or helicopters as the default.

And, you know, I mean, I th-- I think
one of the things that I would, I

would point out there is, is that
you're still gonna need drones.

You're gonna need drones to… If you,
if you've got an animal that's sick

or that's got something wrong with it,
you're gonna need a drone to go out there

and, you know, deliver the medicine.

You're gonna need the drone
to go out there and give, you

know, kind of the supplements.

You're also gonna need in, in most
all of Latin America, for sure, and

m-- and much of the, the American
Midwest, uh, you're gonna need, you

know, attack drones to defend, uh,
your herds from, from dogs, from

predators, from, you know, cougar and,
and, um, and wolves in, in many places.

And even, even in some, you know,
for especially for smaller livestock,

from coyotes and things like that.

And, and again, that is a skill
set that has profound, uh, profound

military applications The final,
the final major, um, vignette that

I'll share with you tonight or
today is, is the, uh, Morpho in Rio.

Um, in January, this
is about two years ago.

They, they, they demonstrated seeding
drones at Mirante do, do Pedral in,

um, Botafogo with a pilot aimed at
the Posse Forest in Campo Grande.

So the, the CEO of Morpho is, uh,
Gregory Maitre, and he partnered with

the city under Re-Refloresta Rio.

So the AI planting-- the AI would
come up with planting plans.

It would look at the topography and say,
"Here is where we could plant trees,"

where there's some level of, like a
little natural protection, something

where the water's gonna gather so
the, the sa- the sapling can come out.

and they're, they're able to do that.

So they were able to seed at least
twenty native species across succession

statio- stages, and they called it a
smart plantation, which decides which

seeds go where in what mix and quantity.

And the drone was able to fire about a
hundred and eighty capsules a minute.

Um, so a two-person team and a drone
claimed roughly a hundred times faster

than traditional planting, right?

So Maitre said, "The work begins much
earlier with the reconnaissance of the

area, with the identification of the
best species, and ends much later with

the long-term monitoring. The drone
is the central part of all this work."

And of course, they're using
artificial intelligence to monitor

and track seedlings and s- and
seeds and not just to seed the drop

Mm-hmm.

So again, you know,
these are civilian jobs.

They have massive implications, right?

Builder, solo marketer, laser
weeder, UV strawberry fleet,

cattle caller, seed drone forest.

Um, hopefully, you see how these types
of force multipliers are im-- Like, it's

impossible to do this in the civilian
context without acquiring skills that

are relevant in, you know, what, uh,
what Roman Helmut guy famously called the

natural meritocracy of extreme violence.

Um, and, and that's gonna mean that
the most valuable profession in peace

and the most dangerous operator in
war are going to look, uh, similar.

They're gonna understand the
world more closely than those two

groups have in four hundred years.

Um, and I think that's going to,
um, that's going to have massive…

And, and, and I can't stress this,
like, there, there, there is the

possibility for massively positive
changes to our world from this.

so, um, as I said in the
beginning, humans are not obsolete.

AI is going to reshape every
industry: software, marketing,

medicine, manufacturing, uh,
the field and the forest, right?

The various types of ag.

And the-- it's gonna do that
through these core skill sets: code,

content, command, which basically
all have military, uh, applications.

And again, the, the social and
economic implications of that are

huge, maybe greater than what I'm
putting in front of you, um, today.

So next time, uh, we'll, we'll talk
about, uh, where the safe professions

are, which I actually think is the
wrong question, but we'll get into

it, and then we'll talk about next
time the, the economics of a Prime.

What are the economics of somebody
that has all three skill sets

and, and what, what would you do?

'Cause I think it's a very different,
uh, track than, you know, the, the,

the kind of the technical university,
the research university that we have

based our, our education systems
around for the last four hundred years.

So that concludes my prepared remarks.

Um, so thank you so much for everybody
that, uh, that's joined us for the free

portion and, uh, let's move into the Q&A.