Read_935 - AI Will Eat Application Software

Yes, AI is a big deal, but the conclusion that AI is going to kill the vertical and functional

software business model simply makes no sense.

The truth is that AI simply isn't going to kill software companies.

After all this panic has passed, we'll see that AI is the best thing that ever happened

to the software industry.

The best in Bitcoin made audible.

I am Guy Swan and this is Bitcoin Audible.

What is up guys, welcome back to Bitcoin Audible.

I am Guy Swan, the guy who has read more about Bitcoin than anybody else.

You know, we've got a fun AI episode today that I think hits on, there's a lot of doom

and gloom and this idea that software is all about to die and humans are about to be replaced.

I don't know if I've talked about it often on and a bunch of different episodes here,

but this article actually from the a16z.news, but it's, it's their sub stack, this looks

like sub stack.

But there is an article by Alex Emmerman and Santiago Rodriguez and it's a really good

one that without actually talking about economics, hits on a handful of really critical economic

principles that I think are some of the things that are just totally, totally missed on

people and why there's so many people who claim like over about to hit super intelligence

and all humans will be replaced and nobody's gonna have a job anymore and all of this stuff.

I've always thought and I've said that multiple times on this show is that the failure to understand

basic economic principles are why people believe these ridiculous things.

They're simply not true, they're not going to happen and there's fundamental reasons

to understand why.

And I think this article actually hits on a few of them without actually hitting on

a few of them and I think it gives me a good foundation which I think what they get into

is a very sensible and sober look at what the value propositions are and what where the

modes will still be and what the major modes are that will be collapsing or changing dramatically

in the way that we currently see the market economy and how the software economy and the

internet economy work.

All of these various industries that are going to be undermined were changed dramatically

by AI but that this is normal creative destruction and there's a number of things that they can

point to and framings that they can suggest to make it clear.

And then I think taking those things and explaining how they connect to the economic principles,

the very fundamental nature of value and judgment in an economy I think can complete paint

a much more complete picture there.

So I thought this would be a really good one to read on a show and give my thoughts on

a quick shout out to the Human Rights Foundation and don't forget that they have tickets on

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So shout out to those guys and everybody who supports the show.

With that, let's get into today's read and it's titled good news AI will eat application

software by Alex Zimmerman and Santiago Rodriguez.

The software industry is having a panic attack since the start of 2026 ETFs for public software

companies have fallen by 30% erasing all the gains since the launch of chat GPT companies

like sales force Adobe into it service now and Viva bellweathers that have compounded

investors capital for a decade or more are down 25 to 30% in a matter of weeks viral

sub stack posts imagine a world where the customer base for enterprise software is hollowed

out and S&P enters a massive years long drawdown.

They're calling it the sass apocalypse.

It's rapidly become the market consensus AI is going to kill the software industry.

Yes, AI is a big deal, but the conclusion that AI is going to kill the vertical and

functional software business model simply makes no sense.

The truth is that AI simply isn't going to kill software companies after all this panic

has passed we'll see that AI is the best thing that ever happened to the software industry.

Why is that?

The bear case rests on a basic misunderstanding of what software companies actually sell.

The market is treating quote software as though it were a commodity input as if the value

of a software company resided in its code and cheaper code meant more competition and

therefore cheaper companies.

But code is never where the value has lived.

If code is where the value was, these companies would have never gotten so big in the first

place.

They would have been killed years ago by open source software or by competition from cheap

software engineering labor in developing countries.

The bearish arguments today usually fall into one of four categories.

Maybe the foundation model labs will move up the stack and own every function specific

application or maybe enterprises will vibe code replacements for their internal tooling

or at least use the option of doing that to reduce software business's pricing power

or maybe existing players will use AI to massively expand their product breadth rubbing

up against each other or maybe a flood of new entrance the famous single person billion

dollar company will undercut incumbents on price pile on top of this agents won't care

about brand loyalty or familiar names only the cheapest options for any particular task.

AI might increase competition but it will also dramatically expand what software companies

can do how fast they can do it and how large the markets they serve can become.

The end result won't be margin compression to zero software will be a much bigger industry

with durable competitive advantages for the companies that earned them.

The modes that matter aren't going away.

The classic contemporary book on business modes is Hamilton Helmer's seven powers.

He lists seven distinct ways in which companies develop robust competitive advantages scale,

network effects, counter positioning, switching costs, brand, cornered resources and process

power.

Let's go through them.

Switching costs are perhaps the one mode that really is going to change.

It's definitely true that AI is changing the friction and the cost benefit analysis

associated with switching vendors agents can assist with a lot of migration work that

used to be a headache.

So it means legacy companies with hostages not customers to borrow a phrase from our colleague

Alex Rampel will feel a lot more pressure than they're used to.

But that is a good thing for software as a whole.

When companies have to earn their customers loyalty instead of relying just on vendor

lock-in, the result is better products, faster innovation and a healthier competitive ecosystem

that grows faster and delivers more value to its customers.

We expect AI will shift some customers to new winners, but it won't impair industry

profit pools at large.

Companies will just get better.

Network effects are a classic mode and they aren't going away.

We tend to invoke network effects for social media platforms or marketplaces.

The more nodes in the network, the more attractive it is to be on it.

But the same applies to application software offerings that exhibit ecosystem, collaboration

and data network effects.

On the surface, Salesforce is a CRM database.

But anyone who has worked in an enterprise setting knows that Salesforce is also an ecosystem.

When everybody uses one platform, the network becomes self-reinforcing.

You use Salesforce because everyone uses Salesforce.

And the more companies use Salesforce, the more valuable the ecosystem of third-party applications

built on top of Salesforce and platform administrators experts in Salesforce.

In recent years, a similar thread is true for Figma.

Every designer, then every engineer, product manager, marketer buys Figma because everyone

is collaborating there.

Go to the annual config conference and witness the value of the ecosystem firsthand.

And the same dynamic is emerging in the AI native generation.

Harvey and Hevia are building finance and legal collaboration spaces that connect service

providers and clients and soon their agents on a single system.

The more people and agents who use these platforms, the more valuable the platforms become.

Elise AI's maintenance product is a multi-sided network that becomes more valuable with every

unit and vendor added.

As migration gets easier, aggregation gets easier.

But these network effects simply don't go away in a world where software is free.

In fact, insofar as AI makes the network more powerful, you can just do much more with

the network than you could before.

We should expect to see AI make these network effects more powerful than they were before.

Scale was never the defining mode in software.

It's just not as important for Salesforce as it is for a cloud provider or an industrial

company.

But to some extent, it may matter for AI applications where compute spend exceeds labor costs, driving

a unit cost advantage to the larger consumers of tokens.

In addition, there are places where scale will still help, as a straightforward economy

of scale to concentrate that maintenance burden in one place, since productivity gains

from specialization don't go away in an AI world.

Stripe highlights the value of centralized infrastructure benefiting all of its clients.

Its compliance infrastructure absorbs the cost of navigating regulations across dozens

of countries so that individual businesses don't have to.

Its payment optimization algorithms, which route and retry transactions to maximize authorization

rates, improve with every dollar of volume, and they can pass those savings on to their

customers.

Finally, scale will continue to benefit companies at the intersection of bits and atoms.

Anduriel, flock safety, and Waymo will continue to see lower unit costs as they produce higher

volume of their hardware offerings.

Brand Endurers

For better or worse, no one got fired for buying IBM remains a fact of life in most

enterprises.

And if every industry gets more crowded, if there's suddenly an explosion of fly-by-night

solopreneurs selling vibe-coded ERPs, we should expect the power of strong brands to increase.

Brand is how you signal reliability in a world of infinite optionality.

No upstart is going to instantly replicate the trust and recognition that companies

like Stripe or Shopify or Service Titan have built.

The closer you sit to business-critical functions, people really don't want to get creative

when it comes to payment processing.

The more powerful brand effects will be.

If you are a startup and you charge customers, you build on Stripe by default.

We do acknowledge the power of brand might change, as more decisions are delegated to

AI agents that optimize for price without the soft considerations that humans have.

Agent-led growth.

But as long as they report to humans who have to worry about getting fired, the no one

got fired for buying IBM principle still holds.

Cornered resources, like high-quality proprietary data, aren't going to stop mattering either.

If friction goes to zero, simply consolidating publicly available data into a usable interface

becomes less valuable because anyone can do it.

But if AI enables doing much more with high-quality data than you could before, then the stuff

that you can't get easily becomes extremely valuable.

We have observed the power of Bloomberg's live market data, a bridge's millions of

clinical conversations, open evidences, vast medical library, and VLEX's legal database.

And perhaps the strongest mode of all in this new era is process power, or as George

Savolka of Hebija calls it, process engineering.

Application software can be thought of as a stored process.

It encodes opinions about how the function of an organization should operate, and those

opinions calcify over years and decades of use into something that is inseparable from

the organization itself.

Successful app software companies are the ones that co-evolve with their clients around

these workflows.

As those workflows penetrate ever deeper into an organization, process engineering only

becomes more important, and more difficult for challengers to replicate.

Consider Harvey.

If Harvey deeply understands how a particular law firm structures its work, the templates

the review process is the institutional preferences, the way a specific partner likes her

mimos done.

There is simply no way a new entrant can replicate that overnight, even with the cost of coding

being zero.

That kind of embedded workflow knowledge becomes more powerful, not less, as software moves

from a system of record to a system of action, because you can just do much more with that

knowledge.

So as the underlying models improve, Harvey's orchestration layer, the scaffolding that

routes model output through specific professional workflows, compound in value.

Better models don't make the application layer thinner.

They make it more capable, because the hard part was never raw intelligence.

It was knowing what to do with it.

From shifts create new winners and new modes.

But there's one final source of durable competitive advantage that we find particularly exciting

as investors, and that is counter positioning.

Counter positioning is a kind of power that can be summoned and wielded by new entrants

to a market.

It's when the new company has a business model, which, for whatever reason, is unattractive

for the incumbent company to compete against.

The corruption theory from Clay Christensen is a classic type of counter positioning,

but it doesn't always have to be low cost as the differentiated counter position.

In software, a new technology stack could create the opening for a startup to create

new kinds of products and business models that are difficult for incumbents to replicate,

like data bricks and their lake house model.

The agent model of doing work and replacing tasks is certainly going to create some counter

position opportunities for new startups to challenge incumbents.

There's been a lot of ink spilled on the disruption of per seat pricing at the hands of agentic

upstarts with value-based pricing.

Let's take customer service as an example.

Decagon prices its customer support product per conversation handled, not per agent seat,

and will eventually price per resolution achieved.

That's fundamentally a better alignment of incentives between vendor and buyer.

An incumbent like ZenDesk can't easily make that same move without cannibalizing its

own seat-based revenue.

Just as Blockbuster couldn't match Netflix's subscription model without destroying its

existing economics, or PeopleSoft couldn't match Workday's SaaS model without upending

its monetization.

Companies that start with the new business model don't face that dilemma, and it's the

core reason why platform shifts so reliably produce new winners.

Guess what?

The total amount of in-state pricing power in the market didn't necessarily decrease.

It just means customers now have a choice of business models they'd like to subscribe

to, and the better one will win.

That's how competitive markets have always worked.

AI is not the first time that a wave of creative destruction has rearranged markets and shifted

the playing field.

But here's the thing, the business models that result almost always dwarf the old ones

in the scale of the total opportunity.

The great software bifurcation is coming.

So yes, AI will definitely change vertical and functional software, but it won't look

like a massacre.

Maybe Gross margins settle into a different steady state.

The pricing power is diminished because switching costs give procurement teams more leverage

in vendor negotiations.

But AI also supports margin expansion due to a much more efficient use of labor.

But no matter where margins end up, we expect that scale will expand dramatically.

Because as our colleague Anish Akarya likes to say, the world is still short software.

We are nowhere near saturating the world's demand for high quality software.

And as code becomes cheaper, we should just expect to see the market demand more.

On the other side of this AI transition, we'll be looking at a much bigger software industry

that provides much more value to its customers.

Companies will be able to serve more customers, enter adjacent markets and automate workflows

that were previously far too complex or too expensive to touch.

Companies that were previously too low ACV will suddenly have attractive economics, ideas

that would once have gone into the too hard pile suddenly become interesting and feasible.

There will still be modes, and as long as there are modes, there's plenty of reason

to expect hugely successful and highly durable businesses to survive and thrive.

AI isn't going to destroy the software industry.

It's going to split it into two parts.

There really will be some categories of software companies that face genuine pressure.

Front-end tools that serve primarily as thin wrappers around commodity functionality,

and do relatively little beyond presenting data in a slightly more convenient format,

are vulnerable.

Incombined systems of record that still operate on archaic interfaces, but raise prices

every year should be worried.

So should software companies that have an outdated pricing model and value proposition

that's just inferior to what AI native competitors can offer?

The companies that win in this environment will be the ones delivering genuine value,

not the ones that built the highest walls around their customer base.

But that's just creative destruction.

It's great for the industry that these companies are facing pressure that they weren't facing

before.

Some of them will figure things out and get stronger.

Others won't, and will die.

That's good.

The rest of the software ecosystem, the companies that are committed to delivering real value

for their customers, is set to grow massively.

So yes, some individual companies will lose, but the industry will win and win big.

The SaaS apocalypse isn't the death of software.

It's the start of something much bigger.

All right, so that wraps us up.

Shout out to the authors of this one.

I have the link to the article.

This is from, I think it's the A16Z sub-stack.

But there's just a ton of really, really good, honestly, very simple and very good economics

in this article, maybe unintentionally, but just kind of in the broad concepts that they

discuss and how they talk about the evolution of markets and stuff that are deeply aligned

with Austrian theory, probably without intending to be, but probably the best example, or the

best thing to connect this to is Javon's paradox, is that when you make something more available

and cheaper to produce, the demand for it inevitably increases, so so many people will

think that like, oh, it won't be viable as a business anymore or it will just be totally

replaced because it's too easy to create.

When in fact, and there's actually a line in this that I really liked, the kind of demonstrated

the concept behind this is that customers will be able to serve, excuse me, companies

will be able to serve more customers, enter adjacent markets and automate workflows that

were previously far too complex or too expensive to touch.

Customers that were previously too low ACV will suddenly have attractive economics.

So what they're talking about here is like too low ACV is it's annual customer value

or annual contract value, something like that, and what they're basically talking about

is like, how much will each new customer bring in versus the cost of sustaining whatever

it is that you are providing that customer?

So if you build a new area of your business or a new feature that only brings in 10 customers,

well then and you're only expected to or it only makes sense to be able to charge those

customers $5 a month, well then you'd better be able to provide that feature for less

than $600 a year or you're wasting money on it.

This is exactly why certain features or software or customizations or preferences don't

get answered in a market because they it's the it's the niche problem, right?

It's the same reason why broadcast media while you wouldn't see on cable news or cable

TV, you wouldn't see a show that was just tutorials about Bitcoin wallets or unboxing videos

for popular products or even something kind of crazy and high risk like black mirror.

You would only ever see it on very specific, very specific platforms or production companies

that knew exactly why or how it would benefit their audience.

But now YouTube and streaming come along and it changes the nature of how that content

is provided and what the ACV, what the cost per benefit ratio of catching or attracting

certain niche audiences or being able to serve some small subset of a network or some

small group or some cultural niche and suddenly all of these things are available suddenly

Netflix can take the risk on something like black mirror because keeping the 50,000 or

100,000 users that can't find that content somewhere else on that platform having a variety

of content types and ratings and length and seriousness versus stupidity, you know,

all of the various types of content that might serve the huge swath of customers Netflix

is actually benefited because they sell one subscription and they simply need and most

people only need two to three good shows that they might want to binge watch or explore

to justify keeping that subscription.

And then in addition, the delivery of that content has a lower overhead now.

And then think about YouTube, the number of people who could survive or benefit from just

doing reviews, just doing reviews of products and being able to branch out because people

can simply pick and choose me just the whole idea of pay per view, right?

Like that was a huge thing in cable and broadcast media that there was this concept after

after you know, quote unquote advanced and you could get it per customer that you could

pay per view.

That was like a big deal.

Now basically everything is some form of you just watch the content that you specifically

select at the time that you specifically want to and there's no schedule.

You don't have to wait until five o'clock in the afternoon to watch your tutorial videos.

You don't have to decide whether and like wait for the next episode of black mirror.

You literally get to binge watch binge watch the entire three seasons or five seasons whatever

it is in one day if you just feel so inclined to do that.

This wasn't but this wasn't even possible until the economics around providing the service

around paying for the service around the platform delivery and the connection or the connection

and the exposure to the size and breadth of the audience changed dramatically with the

nature of the internet.

And is there less media today or less money going into production or provision and content

creation today than in the 1990s because it's easier to do.

I think the fact that there's now this entire subset of economic activity that is called

content creator myself included this podcast doesn't exist without that platform shift without

the change in those economics you couldn't possibly sustain a show about you know economics

and philosophy and engineering and technological shifts and internet history called Bitcoin

Audible on television on broadcast media back in the 1990s I mean obviously none of that

stuff existed but it's this is a very niche audience and it's kind of a niche within a niche

to this like Bitcoin and crypto is a pretty big environment now and I do not serve the

broader audience there's a lot of stuff that I talk about that would make any damn sense

to them and most of them actually want to trade garbage right they wanted they're they're

in it because they're like how can I make money in which when do I sell this token and buy

this token that is not what my audience is that is not what my show is I hate that that's

total garbage I might as well just have a show about casinos but new technologies and new

platforms that lower the barrier to entry and lower the barrier of service provision fundamentally

change the economics of the ability to actually participate in that market and it's specifically

in the context of Jevon's paradox it specifically opens it up to be able to apply or provide services

for a vastly greater array or variety and again niche audiences smaller networks and smaller

features less important ones that did not make economic sense when the calls were too high

that now suddenly become available and that same thing is going to happen a software that same

thing is kind of already happening to software I think it's just largely and more isolated in kind

of the tinker or the solo prerumer sense is that people are building things for themselves that's

exactly what I've done and in fact everybody that I've talked to or listened to when it comes to like

Stefan Lavera and Marty Bent everybody in the podcast and sessions everybody in kind of the pod

Bitcoin podcasting circles is they've all created their own custom workflows and setups and they're

all using AI to make their implementations faster and and get from zero to episode published with

lower cost and lower turnaround and this is where another thing that they specifically mentioned in

this article that I really really liked and and I love this kind of distinction one of the one of

the biggest things that I think people constantly miss is that what is the value shifts value shifts

to where the new challenge is when you make something automatable or you make something

you know you you get intelligence to to be able to do something it's the idea there's this

drastic over simplification of how economies work or the different layers of it and some of the layers

become much more clear when you solve a layer underneath it when it becomes far simpler to understand

how the next layer up becomes the focus it's a slightly oversimplified but I think it's a good

example just conceptually being a good painter for getting a good piece of art right is we often

conflate the process the act or the skill of doing a thing a particular way or with a particular

tool with the value of the thing itself right is a great piece of art is great because it's very

very difficult or it's valuable because it's very very difficult to a make something this pretty

and you have to be a good painter and it's really hard you got to get all these brushes you got to

figure all this stuff out or guitar and music right is that oh I'm valuable because I have spent all

this time and investment into learning how to pluck the guitar and I can do it very well and I can

do it with memory and with with my emotion memory and I can do it offhand I'm very skilled at

creating creating music with the guitar so my value is in my ability to master the tool itself

but then the tool changes and what happens when the tool changes we recognize that being able to

play the guitar doesn't really matter if there's no good music or you don't understand music

theory or you don't understand you don't have the sense of creating good music because the real

value is the music the real value is the beauty and the painting or the artwork itself and in this

sense there's a really good there's a really strong section in this that I think really

uncovers and I've found this with a few other articles and other people talking about this as to

where is the strongest part of the value proposition and doing this work it's in the process

so let me reread this section so perhaps the strongest mode of all in this new era is process power

or as George George Savalka of Hebija calls it process engineering application software can be

thought of as a stored process it encodes opinions about how the function of an organization should

operate and those opinions calcify over years and decades of use into something that is inseparable

from the organization itself successful app software companies are the ones that co-evolve with

their clients around these workflows as those workflows penetrate ever deeper into an organization

process engineering only becomes more important and more difficult for challengers to replicate so

what what do we mean by this and I believe I read I know I read the article but I think I read it

on the show I'll I have to dig into the the feed it wouldn't have been that many episodes back

because we've talked about AI coding and vibe coding stuff recently but about how it is that

a process only is actually created or a process only actually emerges through the act of doing it is

over time and as you run into reality as you run into the need or the judgment or the value that

thing is actually provided the the tool itself is actually providing and I don't I don't think I

did read it I think it was from Peter Steinberg the I think that's his last name the guy who created

open call well I think this was like a long Twitter post or something that I read I'm having a

hard time placing in my memory but it was about the fact that what he is managing when he's managing

multiple agents running at the exact same time and he's publishing you know he's making 6,000

commits to GitHub every single month and he's got like 15 agents or whatever doing different jobs

is he ends up managing the process and directing this orchestra of where to go and how to think

about what we learned and adapt to those new things which means essentially goals judgment and long

term memory become the value that you can provide because you tweak and guide things as they move

you know memory isn't simply a a task of trying to find the right pieces or having all of the

context around one particular thing and it's like you know you can't just read a billion books

and then you're smarter or you have a better sense of the world you also have to filter

what a good book you can eat just as easily just fill your head with garbage because you've read

a billion of the wrong books it matters just isn't it actually matters far more especially in a

world where there's an infinite amount of content where the the price to create content is essentially

zero it matters to filter and judge what is valuable and what is a good use of your memory and

that's exactly the sort of thing if anybody who's worked with an agent for long enough knows that

memory is one of the biggest problems and process is one of the biggest problems because the number

of times that you can go down the wrong rabbit hole or not properly learn or recognize that a

lesson that supposed to have been learned has not actually been internalized or got out of context

with your model and so you broke a bunch of things and you have to figure out how to reel it back

in and clear your context so that you can put those things at the forefront of the most critical

things to consider well this is exactly how you would what you would have to

keep into account or modify your structure like kind of your gateway or the system that you're

actually working in which you can code with your AI agent to basically insist that those things are

never lost in context that means that you have to do something different than what the agent with

the LLM itself is actually doing because it's not just in the weights in the context it's in the

process it's in how you actually apply those things and then create a custom workflow around what

you value or what you think are the most important judgments because you might have software that

just values just is concerned most most concerned with the features that you want to implement and

having fun gadgets and stuff for your customers or you might have software where your goal is

trust your goal is reliability your goal is having a base that cannot be left with and if a little

bit of features on the end at the end state or on the front end aren't directly or immediately

provided or you know have some sort of a trade off that's worth it if you can know that someone can

always go to your application and do the one thing that you need that application to do those are

different considerations those are different judgments those are things that humans must be involved

to make calls on because judgment is specifically a mortal thing judgment is is a

extension of value and value only comes from living in the real world and having a risk and maybe

that maybe you walk out one day and die and there's so much of this doom and gloom that AI is going

to replace everybody and there aren't going to be any jobs and this time is different and it's

incredible to me how God how blind people still are to this that that's just not how it works and

it's not that oh we just got to a point where suddenly it does work that way it just fundamentally

doesn't work that way and I'll tell you a simple reason to understand why LLMs are a map

they're a map they are they are weights derived from human judgment human assessment and human

connection to the real world and then all that information is aggregated together and patterns

are determined within that set of data but if that data is not generated from real world

experience and basically clashing up against the contradictions of the universe then they aren't

valuable anymore they won't actually create a consistent experience and be of something that

will sustain itself in the real world it has to have some sort of interaction it has to have

some sort of means to judge what a good output is and it can't be circular you can't use that

output you can't judge that output and then put it back into the input and think that you're

going to get a better system there's a reason why we've seen this fundamentally when LLMs try to

train themselves on LLM output data or when image models try to train themselves on the best

output of the image model that it gets retarded this isn't something that we're going to engineer

around it's it's a universal truth about the nature of what these things are they are derivatives

they cannot then be better than what they are derived from it is a map of intelligence and

intelligence only matters if it corresponds to the real world and real value judgments for

something that is alive and can die one day you cannot draw a more accurate map because you've

got a better process from the previous map you can take that process into the real world and you

can draw a better map because you have a better system or a better way to draw it but the map will

never ever ever be as complex or varied or be able to better account for the reality of the

situation on the ground then reality itself reality will always be infinitely more complex and

infinitely deeper than any map of it in fact the map is wrong as soon as it's drawn because

everything changes and there's only so there's only so deep of a degree that a map can go

in actually determining the accuracy or the precision of a pattern and the bigger and more

general the attempt of making that map or that pattern of that pattern waiting system is the

less efficient it is at doing the job and this and this is basic economics if we try to create

a superhuman AI that can do everything and it's one big giant model it's just going to fail it's

going to fail to basic economics it will cost too much to run it and it will cost too much to get

marginal improvements in the waiting of that not of that system itself then it will be to create

thousands of tiny specific specific models and have one general agent that can spin up 100 other

general agents to help manage while it's all conducted by a human who creates a series of processes

and structures that are actually specific to the thing they are trying to do and the goal they are

trying to achieve in these process will get processes will get deeper and more complex and more

specific to everything that we are trying to accomplish to the point that everything the complexity

itself becomes exponential such that the bigger models have even worse time trying to achieve it

or answer it and all of this actually only unfolds in the process of the doing of the thing you can't

just take a picture of it and then map it and then suddenly it works forever and that's how

everything goes it literally requires time and response and interaction for it to be developed

and as it's developed it changes the environment that then forces new development and new change

because all of these things are interacting and nothing is static it's like trying to predict the

weather by having a knowing where every single molecule is in the air and knowing every direction

and velocity and then trying to calculate what's going to happen in the next six weeks by watching

all of these molecules bounce into each other it's not even it's not an intelligence problem it's a

sheer compute problem and every time we do something that can more automate or better provide any

of those things everything gets more complex because your output is now part of the input

this is exactly what we see in economics as soon as the measure itself becomes the goal it ceases

to be a good measure as soon as we have a pattern in the market where we're like oh this is definitely

the way that everybody's going to make money well it breaks down and everybody and everything fails

and the pattern dot the dot pattern dies why because you can actually only isolate and take

advantage of that pattern if you're doing it in a tiny minimal sense and as soon as you're

large enough for there's enough volume or liquidity trying to take advantage of that pattern the

pattern stops working because you're predicting the pattern you are an output as much as you are an

input in the market this is exactly true of our technology as well this is how all of this

shit works it's never different it's just exponentially bigger and faster and whatever it is

wherever the value goes to the new layer it's simply it's less and less obvious until we have

technology that actually solves the layer that we are so die hard focused on right now and what

we are doing is we are moving into a new layer and so everybody thinks this time is different

because it's obvious that oh well Photoshop was clearly just it wasn't you know it wasn't going

to kill or destroy all graphic design and all artwork ever well that's yeah in hindsight it makes

perfect sense but in foresight nobody saw that in foresight photos killed painting it destroyed

art photography was the photography was literally the end of the world and it's like oh now because

now we can see the obviously it didn't and photography is great and we need it for all of these

things and it made accessible billions of things that would never accessible for now I can have

pictures of my family I don't have to go sit in front of someone to paint my picture for three

hours just to get some historical image of what I used to look like when I was 20 years old now

you just take pictures of my family and my friends and every event that I go to it was brilliant

it lowered the you could drastically change the economics of everything around the capturing

of history and the creation of art and beauty this time is not different universal truths don't

just hand wave away because we built a new tool and it urged anyone to go into any of these

Bitcoin and AI groups or the the open claw groups and tell me if there's not work involved here

that you can just tell an agent to do stuff and it will run away and do everything and everything

solved and there's no problem go ahead go ahead all I see are people frustrated all I see are

people trying to do bigger things than they've ever done before and running into roadblocks and

having to clear context and having to figure out far bigger and more advanced memory problems

and how do I get it to associate in context be able to pull context from every single thing on my

hard drive the the problems will just scale exponentially with the exponential scale of the tool

and technologies that we are using to take advantage of them this is how it worked yesterday

this is how it works today and this is how it will always work and to the contrary I really like

this article application software will not be replaced it will not die it will actually become

vastly more available more diverse and it will eat everything and humans will not be replaced

because we will now start doing and engaging in so many different tasks and in so many different

goals that we will need anything and everything we can to manage direct and that provide judgment

for all of the agents that we have the people who will lose their jobs and will become poor are the

people who simply have no agency or no motivation to do anything and they were always going to be

unsuccessful they were always going to be miserable because they have no goals anyway anybody who

actually has some motivation and wants to achieve something and has something of value they want to

obtain will have that thing vastly more affordable and accessible to them will be able to create whatever

they want to create far more easily than ever before because that's exactly what these tools do

and there will be tons of disruption there will be tons of chaos there will be tons of shifting

sands all over the place there will not be a strong foundation except for die hard blue collar age

old work that will maintain value at least in the short to medium term but all of that will be a good

thing because everything will be getting more accessible but I really just think the people who

who think that we're going to just break into some super intelligence and everybody's going to

be replaced and there's not going to be any jobs just just don't don't understand the basics of

economics like it economics is relative economics is relative it is not absolute there's no

source of absolute anything in economics it's all relative which means if you move one thing

then it just changes the relative importance of something else because it's all just relative

it's like velocity it's like everything's going to start moving so fast one day that nothing will

ever move slow again it's like if everything's moving fast together then everything standing still

and whether you're going slower fast is just relative to our current position and velocity

it's like everybody's going to be on a train a speeding bullet train and we're still going to be walking

back and forth we're still going to be doing things it is all relative value works the exact same

in fact it cannot be defined in any other way like it is purely relative which is exactly why a

price is only useful if you know what the price of another thing is you know if if if I give you

something if I say that this microphone sitting in front of me calls to bajillion wing dings you

have absolutely no idea if that's expensive or not but what if I tell you that a cup of coffee

calls to bajillion wing dings well now you have some sense of whether or not that's expensive

but you might be wrong because you don't know where I am and how difficult it is to obtain coffee

in the place that I am only in your experience because you know it is relatively easy to obtain coffee

and it is not scarce where you are you can go to a Starbucks it's probably less than two bucks away

right now and get yourself a cup of coffee with a relatively short amount of exchanged work for it

maybe an hour maybe less than an hour maybe a fourth of an hour's worth of work of your work

will actually obtain that coffee but you had no idea you had no idea whether it was expensive until

the tell you told you how much the coffee was worth that is exactly how money and why money

actually works is that it doesn't change so that it can actually be used to weigh the difference

between those two things however if I buy the mic today and there's only a bajillion wing dings

in existence and then I buy the coffee in five weeks and there's 10 billion times the number

of wing dings in existence well then now that comparison is utterly meaningless it doesn't mean

anything at all that is the shittiest money on earth because it cannot possibly compare something

five weeks into the future from five weeks ago which defeats the very purpose and value and

coordination capability of money itself and it's why every money that inflates itself dies because

it unsolves the one problem it fundamentally is fucking supposed to solve if anybody who listens

to this show learns anything it better be that it better be that the concept and reason for

money's existence and why it it necessarily is the rock that every it's the it's the fundamental

weight that doesn't change because all economics is relative all value is relative and without

a good stable measure a good thing to weigh against not only from one space to another not only

from one time to another not only between one judgment and one life and another judgment and life

experience against another but from the past to the future it sucks if it cannot do those things

and the most reliable way for it to be able to accomplish that is to be as scarce as physically

possible if it cannot be that then all of the other attributes attributes of money the fact that

it moves reliably it's portable and it can be transmitted across the space the time in the

various people and existence experiences that it's durable which is just an extension of the

fact that it's portable across time all of those things don't matter because it can't hold the

value needed to be exchanged that those other attributes are even good for an AI is not going to

change that it's just going to f with our current relative assessments because it's going to

fundamentally change the assessment of or the relative value of a ton of things but relative

value will not go away the nature of value will not change to not being relative anymore that's

the suggestion that all value will be that AI will run everything and there will be no jobs and

nobody will ever do anything and there won't be an economy blah blah blah AI will just run

everything and we'll just live our lives on UBI that is the notion that value will stop being

relative and we'll just achieve all of our ends and honestly that very notion should be seen

as absolutely idiotic to anybody who's ever imagined a sci-fi future ever to the contrary most

of the things we dream about doing aren't possible until you have something like AI agents doing all

the grueling and grunt work of all the other stuff if we want to be a civilization that goes to

different stars you'll never be able to do that without something like AI are you kidding me

this is a necessary step to actually gaining the complexity and the power and the capability to

actually do all of the things that we imagine and when we start to do those things we'll just imagine

vastly more complex and ambitious things that weren't even imaginable until we crossed that Rubicon

whether or not you will succeed or fail or whether or not you will have anything you want in a

world with AI will be entirely up to you because it's going to get easier not harder in the exact

same way but probably to a bigger extent than the internet did to everything before it so if you're

a doomer you should think about that so take that with you and I'll catch you on the next episode

of Bitcoin Audible I hope you enjoyed this one I'm Guy Swan and until next time everybody

that's my two sats

you