I think generative AI is at its heart

con and seeing these ultra rich ultra

powerful people lie through their teeth

turns my stomach. The word con is a

strong word.

>> Well, what do you call something where

from the very beginning they've sold

[music] it in the terms of magic but

it's just a halfass arcery machine. They

are misleading the entire world.

>> You are the first person that I've

spoken to that has that opinion.

>> Well, the fact that this is happening is

insane and the fact it's not a scandal

is insane. And I've been in the tech

industry for 16 years now and I love

technology and I'm enthusiastic about

it, but I don't like being misled. And

this is the largest non-consensual push

of technology in history.

>> So, we're going to play a game, Ed. I

have the things that you consider to be

myths about the AI industry.

>> Let's play it. The AI industry is

creating enormous economic growth. No,

it's not. All of these companies run at

a horrifying loss. Open AI lost $20.9

billion last year. None of these people

can just say, "Yeah, we're on the path

to making this profitable." because they

can't.

>> Next one.

>> AI will replace all human jobs. That

just isn't happening and there's no

economic data to support it. Next, the

United States need to spend trillions to

beat China in the AI race. What's the

race to do for us to constantly piss our

pants worrying about China? But people

keep saying, "What if these models fall

into the wrong hands? They're already in

the wrong hands." Mark Zuckerberg, Sam

Olman, Dario Amade.

>> Mark Zuckerberg says, "We'll continue to

invest aggressively in infrastructure to

meet the demand." God met as a

monstrosity. Makes me think of Shrek

with L fogquad. Some of you may die, but

that's a risk I'm willing to accept. If

only these people gave a about

poverty or actual problems in the world

versus are we buying enough GPUs. If

this continues, [music] what does the

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[music]

>> Ed Zitron,

there are a number of things that you

believe that a lot of other people don't

believe, right? You have, I think, a

couple of controversial opinions and

opinions that are in contrast to the

other guests that I've sat here with.

What exactly are those opinions, Ed? I

think generative AI is at its heart con.

I don't think it is sold as honest

software. I think that they overstate

both what it can do, what it will do,

and the underlying financials to the

point that they are misleading the

entire world. And they're actively

exploiting the weaknesses in journalism,

in our economies, and indeed within the

responsible parties with sellside

analysts, governments, and all over the

shop.

>> The word con is a strong word.

>> Yeah. I mean, what do you call something

where from the very beginning they've

sold it in the terms of magic as this

thing that will replace all jobs, that

will cure cancer, and all of these

things? And when you look at it, it's

boring cloud software that's extremely

expensive and unprofitable and also

unreliable at its core.

>> People will be asking where are you

drawing from in terms of your

references, your personal experiences?

Where were you educate? What you study?

What you write about? What do you do Ed?

>> So that's the funny thing is people say

he's not got a finance experience. He's

not going to take. I've been in the tech

industry 15 16 years now in PR but still

had practical experience and I love

technology and I'm enthusiastic about

it. And this thing just comes along that

everyone is telling me is the best thing

since sliced bread. And it can't even do

the basics. It can't even do search.

Well, whenever you ask an AI person,

well, what's your setup? They describe

this PeeWee's Playhouse thing of like,

well, you got to harness here and you

got to use the right prompt. Well, you

don't want to use that prompt. You want

to use this prompt here with this model,

but don't use this model for the

beginning, but at the end, you're going

to want to use this model. And this is

meant to be artificial intelligence.

It's meant to be smart. It's meant to be

autonomous. It's meant to be something

that you set and forget.

>> We have the sort of six leading AI

companies on the table here. Anthropic

Amazon, Nvidia, Microsoft, OpenAI,

Google. You're saying that their

fundamental business model is a con.

>> Well, their revenues are not really

coming from AI. Up until fairly

recently, none of their revenues were

coming from AI. Like dribbles a bit.

Right now, 70% of all AI revenues across

those three companies are from OpenAI

and Anthropic to unprofitable,

unsustainable companies that literally

cannot afford to exist without these

very same companies giving them money.

Amazon sent $50 billion to OpenAI this

year. They sent $5 billion to Anthropic.

Google sent $10 billion to Anthropic.

And in the next three and a half years,

OpenAI and Anthropic based on actual

sellside analyst evaluations, their

estimates that inform whether stock is

going to go up or down after earnings,

they are expecting 400 or more billion

dollar of revenue, 30 or something% of

cloud growth just from these two

unprofitable companies that will need to

be given the money from somewhere. And

on top of that, these companies have

such low respect for the average

investor, for the analyst, for everyone

really that they don't even disclose

their AI revenues. The few times they

dain us worthy, they use something

called a run rate, an annualized run

rate, which means well, nothing. They

never define it. It can mean months 12.

It can mean month 13. It can mean last 4

weeks time 13. It's different every

time, and they never define it. And then

they sometimes just don't mention it.

So, you've got this big thing that is

meant to be the biggest, most

influential change to software ever. And

whenever you ask them about it, when you

say, "What? How much you making from

this?" They go, "Oh, I couldn't possibly

say. I'm too shy." These are public

companies, or at least the ones that

aren't anthropic and open AI. When they

have good news, they'll tell you. And

when they don't tell you something,

well, that actually speaks volumes.

>> Have you you used these tools, the AI

tools, Gemini, Anthropic, Chat, GBT,

etc., and you found no value in them?

There's some value, but it's not there's

they have spent over a trillion dollars

in capex. What

>> does capex mean for you?

>> Capital expenditures. So, when you are a

business and you have operating expenses

like electricity, for example, those

come right off immediately. Capital

expenditures are long-term investments

that are theoretically one-off. So, a

data center or indeed the GPUs you put

inside an AI data center.

>> Okay? So, you've got a data center

>> and then you have these GPUs which are

like computer chips. So AI GPUs are much

bigger, much more power intensive. They

take a bunch of high bandwidth memory

and they because of how many of them you

need. You need thousands of them, tens

of thousands, hundreds of thousands in

some case. You need a bunch of power. So

an example, OpenAI and Oracle are

building a data center in Texas in

Abalene, Texas. 1.2 GW called Stargate

Abene. Within that, with each one of the

eight buildings, there'll be 50,000

Nvidia GB200 GPUs. So, city of Bristol

takes about 7800 megawatt of power a

year, right? Well, Stargate Abene is

condensing more power than that, 1.2

gawatt into a space around 1,172

times smaller. City of Bristol is about

1.2 billion square ft. Star Evelyn is

about 998,000.

So, you're condensing all of this power,

all of this money, all of this labor

into this one spot. And all of these

data centers cost billions of dollars.

All of these companies other than

Microsoft are now to take out debt. And

the thing is they've spent over a

trillion dollars so far and they want to

spend another trillion dollars next

year. And for what? To make tens of

billions of dollars, most of which comes

from two unprofitable companies,

Anthropic and Open AI. One of the

rebuttals to that would be that the

adoption, the customer adoption of

people using Open AI and Enthropic has

been absolutely insane. These are the

fastest growing products in all of

history, especially as it relates to

sort of technology. If we just focus in

on technology, they are, you know,

hundreds and hundreds of millions of

people, billions of people are using

these tools every single day for things

that they have subjectively decided are

problems they need solving. So, you

know, money is a lagging indicator of

value. So, one would argue that they're

just investing ahead of the monetization

options.

>> The first let's start with this

adoption. Is it honest adoption when you

are forced to use generative AI when you

load Google? When you load Google Docs,

Gemini screams in your ear. When you

load Word, co-pilot's bugging you. When

you use Amazon, whatever rofus AI is

wants has opinions on what socks you're

buying. This is the largest

non-consensual push of technology in

history. Chat GPD for example, every

single media outlet has been screaming

about this for 3 years. They've been

saying, "This will take your job. You

must use this. If you don't use this,

you're going to be falling behind." So

people are using it because they've been

told to use it constantly and they're

using it like search predominantly and

that's partly because Google fell behind

search and also because it's better at

ingesting queries sometimes. Sometimes

if you use a generative search it's like

a trolling vessel. It's not very good at

specifics but if you're like does this

thing exist? Has this person ever said

anything like this? It'll still probably

get it wrong but it'll scour the ocean

for you. Nevertheless, that's not worth

a trillion dollars. None of it is. The

amount of money being sunk into this is

just incomparable to anything. Railways,

it blows everything out of the water

because there is no postbubble story

even for this. AIG GPU is not useful for

other things either. There's it's a

directionless egregor of capitalism.

this headless beast that lumbers around

desperate to seek out growth everywhere

in the hopes that if it harasses people

and scares people and demonizes labor

enough, people will be forced to use it.

>> The the reason I I pause is because I

just I think about my own company.

Obviously, everybody thinks about their

own personal situation. So, you have

people listening now that don't use any

AI tools. Then you'll have people that

are using it for everything from coding

new software tools to everything they

write to, you know, images, whatever.

And when you look at the the stats

around enterprise adoption, it says 88%

of organizations regularly use AI at

least once for one particular business

function. And I'd say in our company,

95% of people use a one of these AI

tools like anthropical chatbt or Gemini

every day,

>> right? And that exists on some kind of

spectrum of like the super users that

are using it probably, you know, every

hour of every day for almost everything

to, you know, someone maybe hiring the

executive team that's using it less

because their job doesn't require of it

as much,

>> right?

>> And when you look out into the world,

you know, at how the world is changing

from a content perspective, if we're

looking at generative AI, it is obvious

that these tools are being widely

adopted. Part of the symptom is the AI

slop you see all over the internet,

>> right?

So, I I don't know this this this idea

that it's not being used. I struggle

with

>> it's being used. Here's the thing with

the slop. Before we had AI slop, we had

SEO slop because Google incentivized

doing the lowest common denominator that

would rank well in search. There's a

whole story about how they pulled back

spam guards thanks to Bravagar Ragavan,

which we can get into,

>> where they made the internet worse by

allowing worse content to rank higher.

It's why we have when you used to

Google, oh, best washing machine,

there's 11 different horrible blogs that

read like somebody got a concussion.

They are built to rank rather than be

read by humans or built to be good made

good. So AI helps weaponize that at

scale. Yeah, you can make a bunch of

generic slop. We've had slop for years.

We've just found a slop machine. But

then also there's the problem of cost.

So when you use AI services, you burn

tokens and it's per million tokens. So

>> what's a token? So it's around 3/4 of a

word. So it's characters.

>> So the AI companies have a currency in

which they charge you. Like a taxi in

New York has a meter.

>> Yeah.

>> And they call it tokens.

>> Yeah.

>> And every word, let's just say for ease

it's a word. You're paying per word.

>> About a word. Yeah. And it's per million

tokens. So you'll be charged per million

input tokens. The stuff you feed into it

like a document or a bunch a code base.

And the output tokens are both the stuff

it spits out at the end but also when it

thinks. So, okay, you've asked me to

give you the best restaurants in this

area of New York. I should find the best

restaurants in New York. All of that's

output tokens as well.

>> However, when you're paying for a

monthly service, you don't see any of

that. Put all that crap to the side.

They just have rate limits. So, you can

use them a certain amount and then when

you run out, but they kind of offiscate

what that was. Now, someone recently

found, semi analysis actually found

this, a big analyst group. They found

that on a $200 a month chat GPD

subscription, you can burn $14,000

worth of tokens and on anthropics you

can burn $8,000 for 200 bucks. That is

how most and even on the 20 buck a month

service you can burn $400.

Now most people don't realize that. Most

people have no idea what AI costs. Most

people just think, "Oh, it's 20 bucks a

month." No. All of these companies run

at a horrifying loss. OpenAI lost $20.9

billion last year because people can

burn as many tokens as they want. And

when they tried to move everybody on the

enterprise side, so companies bigger

than 150 onto actually paying the cost

of AI in around March of 2026, to quote

Sam Orman, they said, uh, people have a

big problem with it. I think it's a huge

issue, which is not really what the air

apparent text history is meant to be

saying, but the point is enterprises

immediately started freaking out. Uber

burned through their entire annual token

budget in three months. So suddenly

after everyone saying AI is the most

productive thing ever. It's amazing.

It's changing everything. The moment

people actually had to pay for it, they

go, [snorts]

I don't know actually. Um maybe it's

obviously we all love it. It's all

great, right? But it's costing too much.

So we need to reduce the cost because

people are just dumping stuff into it

being like what do I do here and getting

whatever the median is out because

that's what these things do. they

provide the median answer.

>> So essentially, someone like me who's a

power user of these tools,

>> I could be costing Anthropic or OpenAI

$1,000, but they're only charging me

$100, let's say. So they are having to

subsidize $900 of my usage because of

the electricity costs and the costs at

their data centers. And so your

assertion here is that that is

unsustainable.

>> Yes. And just to be clear, they're

probably not one for$1. It might be 30

for. We don't we don't know. I think

it's unprofitable. These companies don't

disclose them even in their auditive

financials. They play funny games with

how they categorize things. But

nevertheless, yes. And on top of that,

the way that you stand up inference,

which is the thing that creates the

output within these data centers, you're

not just saying, "Okay, turn the

inference machine on. Let's go." You are

standing up the GPUs necessary to take

in the demand, and if you buy too much,

you've wasted the money. You You have to

pay for the hourly GPU use regardless.

If you buy too few, your customers can't

use it. They get pissed off at you. They

cancel. They go with someone else. But

nevertheless, yeah, they would get

demand selling $20 or $40 for a dollar.

And that's what these services do. And

really, the simplest way to explain it

is they were actually profitable if they

were actually just they believed that

these services were worthwhile and that

they were worthy of the cost, they'd

charge it. Regular people wouldn't be

able to get a monthly subscription.

They'd just be paying what it's worth,

unless, of course, there was an economic

problem. And it's very simple. You pay

when you use an LLM regardless of

whether you get what you want. When

these things hallucinate, say you're

doing something, you're coding something

and they go through a code base and they

up a bunch of stuff, they break a

bunch of stuff, you're paying for that.

You're paying for it whether it works or

not, unless of course you're using one

of these subscriptions. I think the the

really interesting point is are they

spending ahead of the value showing up

which is I imagine what they would argue

or are they spending all of this money

and subsidizing all of their users in a

way that's unsustainable and that will

never be justified like does it you know

because you think back through the

history of technology you often get

people

losing money to grab market share

>> right

>> and they're also focusing on bringing

the costs down and making it more

profitable for them as well. But they

can't afford to underinvest.

>> If they were bringing the cost down,

they would have brought the cost down,

which they have not. It seems to be

getting more expensive. In fact,

everyone inference providers don't seem

to be profitable. Even the companies

renting out GPUs don't seem to be

profitable. I imagine that it wasn't

like they started out and they were

like, "Shit, this is unprofitable at the

beginning. We know it. Screw it. We'll

keep doing it any screw." I don't think

it's some big conspiracy. They probably

thought at some point, yeah, this will

go profitable. The chips will catch up.

Customers will pay for the overwhelming

value because you don't know in 2023

where it's going to be in 2026. You

assume it's going to go up. That's the

nature of venture capital. They should

have stopped in like 2024 when OpenAI

lost over $5 billion. They should have

been like, "Yep, this is not going to

work." But they kept going because it

helped number go up so much. It helped

stock values pump. It helped everyone

pump. It helped Nvidia pump, Microsoft,

everyone. and not from the revenues.

Because here's the funny thing about

Google, Microsoft, and Amazon. People

for years have been saying their AI bets

have paid off. Wow, their AI bets have

paid off. As these companies refused to

say how much they're making from AI, but

because their existing businesses

continued to grow and did so, by the

way, through price increases, changes to

how Google and Meta uh did advertising.

Amazon bumped up prices and changed how

they did actually Amazon started a

remarkable ad business during this whole

time as well. and the selling through

Amazon platform anyway nothing to do

with AI but because number go up because

revenue go up everyone went it's AI

because these companies wouldn't spend a

trillion dollars for for no reason right

except in fiscal year 2026 which just

ended for Microsoft annoying I know they

made total according to Bloomberg about

$34.33 billion $24.1 billion of that was

from OpenAI so that leaves them with

about $10 billion in a year when they

spent 115 billion on capital

expenditures just intend to spend 175

billion next year. The math does not

make sense. I imagine their plan was

okay, this is just going to get

exponentially more valuable and at some

point the costs will be outpaced by the

return. Problem is that large language

models need a bunch of money to train

them. They need constant data flow. They

need customized data. It's just this big

expensive monster. And when you try and

talk to people about it and you try and

say, "Hey, look, this is really bad.

Nvidia has sold it was $215.9 billion in

the last fiscal year worth of GPUs

mostly. And you try and go, yeah, that's

to support like $22 billion of revenue

total in the entire world outside of

these two companies that literally

require money being fed into them

sometimes by Nvidia to keep alive. When

you tell people that, they go, "Well,

companies just lose money, right?

Companies because we have this quote

Edson from Prophy Markets. We have this

cult-like worship of the wealthy where

we think that someone wouldn't spend all

this money for no reason. Right? Because

reconciling with that with this idea

that the ultra wealthy, the ultra

powerful didn't get there through big

brains. They didn't get there through

anything other than luck and opportunism

and getting an MBA perhaps with the

right people. That they just got there

because they're regular people and they

just happen to be in the right place at

the right time. reconciling with that

and realizing that the world is not

controlled by people like a meritocracy

is kind of grim. So it's easy to be like

no they're not making a mistake I must

be missing something and that's what

they want. So you know I think back

through the history of technological

breakthroughs and I think about I mean

you can look at different industries and

one of my favorite books on this subject

is the innovator's dilemma. not read it.

>> And one of the things it talks about is

how the the innovation that ends up

taking out or transforming an industry

often starts worse, doesn't make

economic sense, none of your customers

are asking for it. And this is typically

why we end up ignoring it. So like

you've got horse and carriages in the

1800s.

>> Amazing form of transport according to

the 1800s, you know, people of the

1800s. And then you have this thing

called cars come along. Now the problem

with cars is they broke down all the

time. It's kind of like AI hallucinates

now. um they were more expensive and the

the economics of it didn't make sense.

You might as well walk than buy a car.

There was a law at the time that meant

you had to walk in front of it with a

red flag and wave and someone had you

had to employ someone to walk in front

of it waving a red flag. Obviously, it's

worse. It's like a worse solution.

However, these things that are

disruptive innovations, they have a

higher ceiling of growth and so they

eventually overtake the horse. And I

when I think about that analogy in the

context of all of this, I go, okay, it's

imperfect at the at the moment. the

economic models aren't perfectly ironed

out. They're still figuring out how to

make it cheaper, the infrastructure,

etc. But as if you think about the rate

of improvement versus other you know

let's say coding how much could I train

a human coder to improve and to increase

their output versus an AI agent one

would go if you just imagine any rate of

improvement in these AI tools at some

point if you just imagine a 5% rate of

improvement per month at some point it's

you know and then you imagine a 5%

reduction in cost which is what we did

with the internet what we did with cars

but Mo's law

>> mos law is a mos law is not with GPUs.

So let me let me actually explain. So

Nvidia Nvidia invented I think it was in

the 2000s they put out something called

CUDA which is the underlying software

library and the way to run software on

GPUs. took them solid decade or more to

make it something where they could do

data analytics, one of the early things,

mapper and such. And then when AI came

along, they'd had lots of experience

with it. But nevertheless, this company

has got more money, more attention, more

geniuses behind them, more people

focused on making their things more

efficient than anyone could ever ask

for.

>> And Nvidia, for anyone that doesn't

know, makes the chips.

>> They So, and that CUDA thing I

mentioned, they were the ones with CUDA

and CUDA allowed generative AI to grow.

Okay, so they're chips.

>> Chips and chips are needed. Those are

the things that go into the data

centers.

>> And there specific chips are the ones

where you can run AI software on it. So

the training runs and also the

inference. Now, here's the thing. The

the car example back then you didn't

have pretty much every mathematician and

scientist going into the car industry.

You didn't have the combined world's

governments never shutting up about

this. And by the way, giving them credit

early since 2023, they've been saying

this is inevitable. Even in what you

said, 5% improvement. I don't even know

how you'd measure that because a junior

software engineer can still experience

things and learn things from context,

from how people deal with problems. And

the way that people deal with problems

is not as simple as looking at the code

or reading some emails. It's context

cues from speaking to a person. It's

being in different environments. And

there may there are uses for LLM's

encoding. I don't dispute that. But even

saying 5% uh what does that mean? Is it

better at Rust? Is it better at C++?

>> I'd say productivity just like yeah

shipped. If we did it in the context of

coding, it would be like shipped code.

>> That's the thing that would be like he's

the best writer in the world cuz his

newsletter's really long. That's an

insane way of evaluing it. With coding,

it would be I mean it's even difficult

to evaluate because it's is the software

out there better is actually a great way

of evaluating it. And I would say

uniformly not. I would say the standard

of software across Google, Microsoft,

Amazon, Meta, especially God, Meta is a

monstrosity, is worse. GitHub, GitHub,

someone posted on Twitter earlier today,

we should get a notification when GitHub

is up rather than when it's down because

that would be more reliable. Microsoft's

one of the largest companies in the

world, and they can barely wipe their

own ass when it comes to GitHub. The

quality of software is going down

weirdly enough as more people use LLMs

and more businesses demand and I really

do mean demand that people use these

services. So on this point of if we go

back to this horse and carriage and car

analogy say that we're at whatever point

today if you imagine any rate of

improvement in the technology which we

have seen since tragedy came out

>> I remember when tragy came out and I was

in Asia and I was there showing it to my

fiance I was like look it can do this

and it was hallucinating once in a while

and getting things wrong. I actually

don't have that experience anymore. I

have moments where I believe it's

reasoning is weak, but I don't have

outright hallucinations anymore. See

that? I I disagree. So,

>> give me an example of what you define as

a hallucination.

>> Okay, great one. So, I have a Bloomberg

terminal. Yeah. The very useful thing

they have on there is ask B. So, when

you do a Bloomberg inquiry to like look

up what we think Nvidia's revenue is

going to be next quarter, it runs

something called BQL, which is its own

programming language. Now, instead of

having to learn that, you can just type

into RSB and it will generate it and run

it for you. And so, you get it pulled up

and you know where the data is coming

from. It deals with hallucinations real

well. The other day, I was like, you

know what, get a little spicy. I'm going

to look up the growth rate of stocks of

Microsoft, Google, Meta, and Amazon over

the course of 5 years, I think it was.

>> And I was about to I was copy pasted it

over to something looked at in Excel. I

was about to was writing the newsletter.

I went, Microsoft stocks never been $575

a stock.

You know what? When it's a cute little

thing like, oh, it's a stock price and I

kind of call it was no harm, no foul.

That's fine. But when you're talking

about, I don't know, like a transcribing

tool for a doctor or a financial model

that a hedge fund is dependent on, at

that point it becomes a little more

dangerous. And the thing is a

hallucination with a software package.

For example, you're refactoring a code

base and it leaves a door open

security-wise or it just breaks

something and you I don't know maybe

you've been vibe coding for 6 months.

You haven't really been coding with your

own hands for a while. Maybe you've

forgotten a few things. You had this

slop to look for. I'm not doing

it. And so the problems become

multiplicative. And I don't really know

how you train them out of that. And

they've certainly not succeeded. So on

one hand they have got better but one of

the main ways they evaluate them getting

better are benchmarks that are adjusted

specifically for large language models

because you can't just have them do

tasks. They've got better at that. They

found some tasks they can have them do

on them like meter me they have this

thing where it's like check out this

chart look how much better it's getting

at running tasks. Wow it can go for an

hour and then you look it's like yeah

and successfully completing them 50% of

the time. They they have a hallucination

leaderboard and it really focuses on

basic tasks and it shows that the

four-year trend according to historical

data from the Victaria hallucination

leaderboard shows that hallucination

rates on simple summarization tasks have

plummeted from around 21% 21.8% 4 years

ago down to 0.7%

roughly on today's top frontier models

like Gemini and Chat GPT. Again the

point of nuance here is that these are

on simple tasks which is kind of what

I've experienced. I've experienced that

on day-to-day things that hallucinates

less again rate of improvement thinking.

So if I just imagine the trajectory to

continue there is going to become a time

where hallucinations become rarer than

they are today increasingly and also

what I would say is when I think about

other technologies there's two more

points other technologies at their

inception when they first came to the

world like the internet also had

technical difficulties. I remember

growing up with dialup modems and I

couldn't go on the phone at the same

time as going on the internet. I'd have

to stop Runescape upstairs to go on the

phone. And you thought this is crap.

This is technology crap. All the

>> I I don't know, mate. I loved it.

>> Yeah, I know. You It felt like magic.

And then in hindsight, you go, "Wow, I

now have Starink and 5G internet from my

phone. It's unbelievable." You couldn't

leave the house with internet before.

And that's what I mean by the rate of

improvement thinking. I'd say the last

point is we often compare AI to

perfection,

>> right?

>> Whereas that's not actually the

alternative in the working world. Like

if I wanted to do let's say a simple

writing task, I should compare AI to my

alternative alternative way of doing

that simple writing task which is both

measured in my time right and my ability

to hallucinate as a person who doesn't

know everything

or if I'm hiring someone an intern who

might also be prone to hallucination or

have gaps in their knowledge.

>> So it's not actually like we're

comparing we should compare AI to

perfection. It's AI to the other

alternatives. And if someone

hallucinates 0.7% of the time, but knows

way more and is faster, maybe on a net

basis, that's a good trade. Maybe I

should use AI. So, let's start with an

example. Someone I love dearly, Matt

Hughes, my editor, lives out of

Liverpool. Wonderful guy. I don't pay

Matt Hughes because he knows everything.

I pay him because he has incredible

context and a ton of knowledge and he's

willing to expand it and work with me

and moral sport and he's a great editor,

but he's also someone who gets into the

guts of it and has the experiences of

it. He's a decorated tech journalist and

on top of that a wonderful loving being

with empathy and joy in his heart for

the stuff he loves and absolute

venom for the people he hates. That's I

can't get that from a large language

model. But on top of that, I don't I

push back on just the assumption there.

>> When you say knows everything, what good

is something that knows everything when

it sometimes doesn't know anything when

it's sometimes? And on the thing is, are

you really paying an intern for

something basic? Are you really going to

them and saying, "Yeah, can you look up

what the date is?" No, you're doing that

on Google. Whatever the task is, you are

trying to also train an intern. The

point of an intern is to train them and

turn them in, take them out of Pinocchio

status,

>> but it's also an intern learns. And in

turn gets context and in turn learns

your habits. Learns

>> AI gets context and learns.

>> No, it doesn't. It

>> doesn't learn.

>> I mean, it doesn't. The way it learns is

you create a giant claw. MD file that it

sometimes doesn't read, sometimes does

read. You create a harness. You put it's

like it's Pee-Wee's breakfast machine

from PeeWee's Playhouse. You have to do

all these controversies to mitigate the

hallucinations. And even then at the

end, how much effort have you put in?

>> But so, okay, this is an extreme

simplified example. If I went on my

Claude now and said, "What's my dog? my

dog's name.

>> Uhhuh.

>> It would know my dog's name.

>> Jesus Christ. This this company raised

95 billion.

>> I'm saying I'm I'm using an extreme

simplified example to show that it can

remember things from the past.

Obviously, it knows much more complex

things as well, but I just use that as

an example. So, we we we accept the fact

that it can it does have memory of the

past.

>> It has files it can access that have

stuff on it, but that's not the same as

memory. And it's also just okay. So, it

remembers your dog's name. It might

remember your habits. It might be able

to read things you've said before.

>> Does it know your moods? Does it know

what's going on in the world around it?

Does it have good days and bad days? Is

it there for you? Because it's just a

text machine. And the thing is

the intern example. An intern is

something that can grow. It's something

that you invest in. That's not something

you do through feeding files and text to

it. The way that we store memories

ourselves, the way in which we acrue

experiences is a a milerum of emotion

and feelings and facts

>> completely different. So I think there's

two things here. There's the process in

which something happens and then there's

the output.

>> So the process you're describing the

process of how a human does memory,

>> right?

>> The way that an AI does memory is

different. But the thing that people

care about is there value in the output.

I.e. You know, if I dump all of my files

into Claude, I don't really care how it

processes it as long as when I ask it,

what's my revenue? It has the number.

And one could say the same thing about

training someone. You could say, you

teach them, you put lots of effort into

them. You give them lots of context. You

you educate them and give them

experiences. And then you might come and

say to them, by the way, what's my

revenue? Now, the processes are entirely

different, but the outcome is what I

care about. Do they know the revenue

number when I ask them? And so, I think

that's the part that we sometimes get

lost. we get, you know, cuz I have I've

heard this debate about like can AI be

creative,

>> right?

>> I think like the way to answer that

question is like it's about the output

when I ask it to do a creative thing

does it give me the answer not is the

process the same as a human process cuz

actually no who cares what the people

care about they pay for the outcome the

product.

>> I actually disagree about the process

because Matt Hughes for example

>> your editor

>> Yeah.

>> Yeah. watching him go down a rabbit hole

and being there with him and actually

vice versa him doing the same thing. We

wrote these well I mean we were working

on the research I ended up sitting there

for like the dayong session of writing

11,000 words and he he had given me a

bunch of notes. It was actually just

even describing that process, I feel so

happy cuz it was like us being like I

can't believe how these Jesus

Christ they can't do like just like the

misanthropy of just the horrible cynical

people of asset managers like Blackstone

just learning about them and being like

it can't be this and having a back and

forth with him that is fundamentally

different because we were both learning

together and the learning process was as

much about creating the output as the

output itself. When you learn something,

you're not creating the average, which

really is what these things do, of the

documents it could find. You're not

getting particularly novel outputs. If I

needed a generic slop output, sure, but

I've I've used some of the higherend LLM

harness machines that the hedge funds

use, and they all give the same shite.

It's all the same the same generic

reports, the same, oh, we noticed this

analysis, things that you can find on

any kind of AI slop out there. what you

described to me there, what I heard

anyway is there's two points of value

you're getting from your time with that.

I mean, I mean, there's many more, but

you said you're you're learning and then

you're getting this book edited blog

blog. You're getting a blog edited,

which is the output, and you're getting

learning and you're also really getting

connection and all these other things.

But when I come to when people sort of

think about the value of AI, of course,

they could use it to learn. But in the

example I gave of like repeat my revenue

number back to me or do this number, I I

just care about the output. I could use

it to learn. I could say what if the

revenue number was wrong once you should

have defined deterministic ways of

knowing those numbers you should not

rely on them even with the terminal

running BQL which I trust I will double

triple treble check everything just to

be sure partly because also the process

of learning for me I don't want just a

report I go like that I want something

that I fully understand and also

understand the context around it I don't

think that LLM do that and I just don't

see them getting

in a way that does that because it's

it's just not what they do. And also

there's the other problem of the more

detailed the report, the more likely

there are things to be wrong with it. If

you are with Matt Hughes, for example, I

can trust he's got it right. I can trust

he understood and I can trust that I can

have a back and forth with him that will

inform me if I've missed something. I

can read the stuff that he's read and

actually trust him because there's a big

trust part as well. What is the basis of

your trust in Matt? Could it be his

historical performance?

>> I mean, yes.

>> Okay.

>> And also the fact we've learned half of

this stuff together,

>> but but tenure tenure doesn't

necessarily There's probably people, you

know, for 15 years who you also don't

trust. Yes.

>> So, I think I was trying to figure out

like what is the what is the thing

that's causing humans to trust another

thing. And I guess it would be continual

delivery of a commitment made of sorts.

And so with Claude for example on simple

tasks as we've seen from this

hallucination leaderboard it continually

delivers for people and that's why we've

seen the fast

>> I mean is that what that board says

>> well it's it's saying like is it getting

it wrong is it hallucinating

>> simple task how are those defined

>> I I don't know

>> that's the thing though because this is

actually a very very illustrative thing

of the AI industry they are the what

aboutist masters they have like well

look we got this we got this benchmark

that says we're good at this and look

the numbers higher What's the number

mean? No. What does that mean? And I'm

not using this as a critic against you.

It's

>> when you can't give a direct answer, you

give a side answer. When you as the LLM

industry want to prove your worth, you

can't just be like just use the product.

When the first iPhone came out, go was

Penn State at the time. Oh, I felt like

the uh apes at the beginning of 2001.

official voicemail. It was

immediate. And I showed it to tech

friends. I showed it to the most normal

people in the world. And everyone was

like, "Holy this is They were on

razors. They were on Nokia 3210s. It was

obvious the value." Amazon Web Services,

same deal.

>> It wasn't obvious though.

>> Yes, it was. I mean, I bought it

>> to you. To you, it was.

>> It was. And I also showed it to a bunch

of people because I'm aware that I had

bias when I just love gadgets.

>> But but I remember the famous Steve

Balmer who was the CEO of Microsoft

interview where he was told about the

iPhone and he bursts out laughing.

[laughter]

$500 fully subsidized with a plan. I

said that is the most expensive phone in

the world and it doesn't appeal to

business customers because it doesn't

have a keyboard which makes it not a

very good email machine. You can get a

Motorola Q phone now for $99. It's a

very capable machine. It'll do music.

It'll do internet. It'll do email. It'll

do instant messaging. So, I I kind of

look at that and I say, "Well, I like

our strategy. I like it a lot.

>> He burst out laughing, mocking it

because it was so disruptive. It was way

more expensive

>> and it was way different. No keyboard.

>> Well, phones used to be insanely

expensive and the carriers would cover

them, but you had to sign a long

contract. You were still spending 500

bucks. But the thing I'm getting at is

you didn't have to explain to someone

why perhaps you'd have to get past the

cost part, but you could just be like,

"Look how good this is." And then once

the app was the iPhone 3G with the App

Store, people were like, "Oh this

could actually change things." mobile

web. Even though it was a monstrosity,

it was so bad at first. Even then, you

could get your emails and you could just

look at them. Point is, Blackberries

were also expensive and were still

actually kind of cool, but the way they

worked was not like consumer software.

They didn't have the classic GUI.

iPhones felt like that. It felt like an

a cell phone designed even like a

computer. It was obvious. It was obvious

from the beginning. Everyone I was I was

dating a girl in the center of

Pennsylvania at the time and everyone I

showed it to was like, "Wow, this is

incredible." That to me is the obvious

thing with AI to this day when you're

like, "Okay, why is it so amazing?"

People still dither. People are still

like, "Yeah, you can't run a business

fully with it without this weird system

of pulleys and levers and such."

>> But how come then when you look at the

stats around ChachiBT's growth,

>> 100 million active users in just the

first 60 days after launching? For

comparison, Tik Tok took 9 months.

Instagram took 2.5 years. And the

internet itself for the worldwide web

took roughly 7 years to reach that

scale. Over 60% of the US adults are

integrated into AI tools in their daily

and regular routines within 3 years of

the launch, reaching a 40% of the

population. And that same milestone took

the internet 5 years and personal

computers nearly 12.

>> Okay. So like this is the I think this

is the part that's giving me dissonance

is like when I showed my fiance chachi

okay it was didn't [clears throat]

really work

>> but as a sole entrepreneur who English

isn't her first language

>> who has to write lots of text lots of

copy and generate lots of images and was

paying a graphic designer to help her

make um certain images that she you know

couldn't make herself because she

doesn't have the skills.

>> She would describe it as being

transformative for her business. What

I'm hearing from you is that it's not

transformative and there's no value in

it for people. But she if she was sat

here transformative,

would she pay the per million token

rate? Would she pay the actual rate? Cuz

that's the thing. If this was sold at

its honest cost. Yeah.

>> I would actually if and people were

reacting like that and they were paying

23 $4 every time they did something and

they were genuinely happy. That might be

an argument.

>> What is the what would be the honest

cost if they weren't sub

>> the actual per million token cost? The

actual API cost they should char.

>> Do you know how much that is relative to

God? Depends on it depends on the model.

But there's actually kind of a point I

want to make about the thing you said

with the internet earlier. So when I

first got on the internet 33.4 kilobits

a second modem even back then I was like

if this was faster and that was

like immediate just like if this was

faster cuz it was slow. You go on like

happy puppy or something download take

all bloody day waiting for share word to

download immediately like if I could do

this faster it would be better. And even

back then I'm like, man, you could

probably do video camera stuff with this

stuff that eventually happened. And

actually, there's this guy called Jim

Cavell from Goldman Sachs in a report he

did in 2024 that was geni too much spend

for not enough return. Paraphrasing

there. And he made the point that in the

run-up to the iPhone, there was

thousands of presentations that when GSM

radios get smaller, when Bluetooth

radios get smaller, when Wi-Fi radios

get smaller, it is inevitable that we

will get something like this. And then

he said that there is no such path for

AI. There was no road map to AI becoming

this thing that they promised. And I

must be clear, if these companies had

gone out there and are like, "Yeah, this

is interesting cloud software. It's

generative. It's really expensive. We're

not sure if we can fully not trust it.

Not in the I'm scared way. I mean, just

like we're not sure that this is going

to be a disruptive world changing thing.

It has potential, but we're going to go

slow. It's really expensive. This is an

R&D effort. We're not going to expose

consumers to it." and actually being

like called them like I don't know

language models and no no generative AI

stuff just being not even call it

because it isn't AI it's not autonomous

it's not smart I actually might respect

it but this is not they've gone out

there since 2023 and said it was 2022

this is the best thing since sliced

bread this is changing everything this

is going to do all your work this is

going to take your job you're going to

talk to Bing and it's going to tell you

to leave your wife all of these crazy

things and what's funny is when the

writer uh Kevin Roose I think it was

He was speaking to Kevin Scott, the CTO

of Microsoft, about it. And Kevin Scott

goes, you know, I'm just glad we're

having this conversation. Instead of

being like, "Settle down, Beas. It's a

website. The website told you something.

It's just LLM." They talked it up. And

that's because everyone is talking about

what they wish this was. Rather than

talking about what it can actually do.

This makes it scary to people

deliberately. So, it makes it

environmentally destructive. Look at the

gas turbines poisoning black

neighborhoods. I think it's in

Louisiana. It's one of Musk's data

centers. Look at the incredible energy

draws. It is raising power bills and

also it is creating inflation across all

consumer electronics because of the

massive RAM.

>> You know what's interesting? I almost

feel like so much of what you're saying

is true and also it can be true that

this technology is going to profoundly

change the world. And I think like you

know I think back to the early days of

the internet is maybe the closest

analogy we have of you know in the com

bubble. you know, you wrote this great

essay.

>> Yes. Yes.

>> Which I found really funny um especially

the name the rot economy and you talked

about the rotcom bubble.

>> Yes.

>> Talking about how AI is of less value

than people think.

>> And in that in the sort of com bubble,

what you saw is huge hype, people

overselling the capabilities of their

websites and what they were building.

But in the wake of the dotcom bubble,

yes, 90% of stuff went to zero,

>> but you had generational companies born

that changed the world,

>> right?

>> And so I I do I kind of and that's what

bubbles do, right? Huge hype,

overinvestment, investors get crazy,

delusional. They think it's everything's

going to change. At the same time, you

do have skeptics

>> in these moments. The the dot bubble had

I mean the internet itself had the

biggest skeptics in 1998. Nobel Prize

winning economist Paul Krugman said by

2005 or so it will become clear that the

internet's impact on the economy has

been no greater than the fax machine. In

1995 astrophysicist Clifford stool

famously I wrote about this in my book

wrote famously in Newsweek. Do our

computer pundits lack all common sense?

The truth is no online database will

replace your daily newspaper. No CDROM

can take the place of a competent

teacher. Commerce and businesses will

shift from offices and malls to networks

and modems. Bologoney. So, how come my

local mall does a roaring business and

the cyber mall gets zero business? And

then I'll give you one more from

Krueger, who was the award-winning

economist. He said, "The growth of the

internet will slow drastically as it

becomes apparent most people have

nothing to say to each other."

That's that that that may actually be

the worst one of those predict like hang

around any bar in middle America.

Honestly, the best conversation,

>> but it's just all the same thing.

>> I actually So, Clifford Stall actually

his piece was interesting cuz that there

were some boner points in it, but he

made points about how like an

overwhelming amount of bad information

out there is bad for society. He's

completely right saying how online

education would not be a great

replacement for regular education. I

think we've seen that. But there is an

economic difference that's vastly it's

just completely different. So.com bubble

was actually two bubbles. There was the

website bubble which was just trash on

trash on trash. It was just like I think

what was it? Excite at home bought a

eury incard company for like a billion

dollars. It was insane crap happening

that was so small. The big thing that

people are thinking about is the dark

fiber.

>> Dark fiber. dark fiber was all of the

wires that put in the ground thinking

we're going to have all this demand for

internet and it turned out that demand

for internet I think the analyst

estimate was it was doubling every 90

days when it was doing that every 6 to

12 months maybe maybe longer and just

thus there was a massive overbuild of

fiber optic cable and indeed the

transmission stations and such just

simplifying to bring that to people's

houses and there was the assumption that

well that would all get lit up and

people would want it immediately didn't

really

Now the post.com bubble thing people say

is well but after that there was demand

from the internet. That's the thing

though that's very different to demand

for generative AI. Right now the demand

we have for generative AI is

predominantly subsidized. Just let's

start there.

>> Yeah

>> predominantly subsidized and most people

experience it are not paying the real

cost.

>> I agree.

>> On top of that we already have all of

the possible marketing in the world. We

have the largest, most disingenuous

marketing campaign in the history of

man, pushing this up the hill. We have

the apex predator of cloud software,

Microsoft. They can only get singledigit

billions from selling AI software. And

Christ almighty, outside of OpenAI and

Anthropic, we barely get $22 billion.

And the thing is, $22 billion is a large

amount to you and me. It's not a large

amount of money when you spent a

trillion plus dollars. When you have

anthropic and open AI with $1.1 trillion

worth of cloud commitments and on top of

that, how does this turn into a post.com

bubble thing? A data center built today

is going to be as expensive to run in

2050 as it is today unless there's some

breakthrough in electricity. But again,

that's not happening with AI. AI is not

doing that unless there's some

breakthrough in GPU technology. But we

already have Broadcom, Nvidia, etched.

We have every major chip company ARM

trying to do something about this. And

no one seems to magically be able to

make this profitable or indeed even less

costly. Even Nvidia with Vera Rubin,

their more expensive new GPU system.

Even then, they're like, "Yeah, 10x more

efficient. It's uh more dollars per

megawatt." They're all koi about it.

They don't just say, "Yeah, we worked

with OpenAI and Anthropic and we found

it reduced our cost by 50%." Easiest

thing in the world if it was true. And

that's because it's not happening. And

this isn't a case where

>> So are you saying there's not going to

be the demand for let's say let's you

know there's different types of AI

generative AI we

>> Yeah. And actually that's a good point

to make. The reason they use the term

artificial intelligence is so everyone

would lump everything into it.

>> They [clears throat] would lump uh

protein folding nothing to do with LLMs.

Robotics not LLM.

>> Autonomous weapons even horrible as they

are not LLMs because you couldn't trust

them. But they've mushed everything into

AI so that when you say, "Well, AI

can't," they'll go, "Um, um, sir, you

forgot to give us homework and also AI

it's working on curing cancer." When

it's just like, "No, that's not LLM.

Stop giving them credit."

>> The similarity though is they all need

GPUs, all these.

>> And that's the funny thing. All those

data centers that we're building, all of

them are for just generative AI. They're

not for all of the other stuff. They're

not for the cool AI has been

around for a long time. Google. A lot of

the good stuff that comes out of Google

from the search side is AI but

pre-generative.

>> How would you run the the type of AI

that sits in a robot? Let's say one of

the Optimus robots if you didn't have a

GPU.

>> So Matic Matic has this cleaning robot

for example. That thing is not got a

little GPU in it. What it has and may

indeed have used some GPUs but no year

as many as they need for generative AI

to run the data feed training data into

it so it's able to clean a house. But

when the little buggers going around

cleaning my floor, turdsly I call him,

it goes around mopping my floor, it's

not like burning money the whole time.

But when it comes to these massive

amount of data center, sighteline

climate said in February there's 190

gawatts of data centers under in

planning. Don't know about under

construction that works out if about 12

million megawatt that's what like $1.6

trillion to3 trillion a year in annual

demand you'd need for that. We don't

even have $130 billion worth of annual

demand. And people say, well, it will

grow. how when most of the demand is

coming from Amazon feeding money to open

AAI or anthropic, Microsoft feeding

money to OpenAI and Anthropic, Google

feeding money to Open AI and anthrop

well hasn't fed it to Open AI yet, but

they're a pretty big customer, billions

of dollars. The conside is that we are

building these effiges to capitalism,

these giant GPU data centers, and people

are being told, well, it's for AI, you

know, the thing that's done all this

other stuff that's unrelated. Or the

worst thing I've seen is like, oh, you

don't like you like online banking.

Well, you do like data centers. There's

a big difference between a data center

for regular nonGPU compute for standing

up a server, a content delivery system

like Akami or something that brings the

website to you or how Meta runs

Facebook. That is not the same. It takes

way less power, mostly CPUdriven

compared to these giant GPU data centers

that offer one thing, one thing only.

>> But I was doing the the research and

looking at some of these notes here. It

does say that for tougher types of AI

systems designed to solve concrete

physics, biology, and spatial problems,

they require some of the most intense

data center infrastructure on the

planet.

>> Yeah.

>> AI systems like Deep Mind's AlphaFold,

the protein folding company

>> used for genomic sequencing and climate

forecasting, etc. run on high

performance computing clusters. These

require immense precision and continuous

heavy computing data centers.

>> Yeah. Training the brains for

self-driving cars requires billions of

miles of simulated physics environments.

The AI isn't generating text. It's

learning to navigate 3D spaces and

gravity and relies on data centers,

>> right? And the thing is those data

centers, they might have GPUs in them.

We had GPUs used for this HPC, the high

performance computing before generative

AI. And yeah, that's how AI has been

trained before. That's how Tesla did.

believe they've had their own data

centers when it comes to training the

autopilot system for better or for

worse. That's how we've done it before.

Again, that is not why we're building

these data centers. These data centers

are being built to sell to AI generative

AI companies to either train systems or

run inference. These things are being

built in this brainless way where it's

just well actually maybe this is a good

way of illustrating the con because

everyone saw Google, Microsoft, Amazon

and Meta give Nvidia over call it 800

something billion dollars

because everyone saw that they went well

they wouldn't do that for no reason.

They went we got to build more of these

things. There must be all this demand.

Even though the demand 70% or more of

all that demand comes from these two

companies who were funded by these three

companies and that's the funny thing.

The reason that they don't want to break

out their AI revenues is because it will

become alarmingly obvious that this was

the case. It turns out that the only

real big customers cuz it's not like

they're building a few data centers.

They're building trillion plus revenue

potential. They believe they'll get

speculative. It's entirely speculative.

They're building it because they saw the

biggest companies in the world buy a

bunch of GPUs and they said, "I want in

on that." They must have diverse

customers, right? They wouldn't just

have two unprofitable fail sons that

they're propping up with. Christ,

they've raised $217 billion just in

2026.

>> So, we know that some of the biggest

companies in the world are using AI,

generative AI to write a lot of their

code.

>> Mhm.

>> That is a great productivity gain for

those companies, right? I mean, have you

used Google or Facebook or Instagram or

GitHub recently because they are

catastrophically worse? Amazon Web

Services went down multiple times

because of their AI coding tool. How

>> how is how is Google worse?

>> Well, I'll tell the story of a real

guy called Preaggo Ragavan.

Previously, one of the heads of ads at

Google in 2019, Google called something

called a code yellow, which is when they

said, "We've got a problem." And it was

material weakness in query numbers which

means the amount of times that people

were searching on Google search. Guy

called Ben Gomes internal at Google then

the head of Google search says wait a

minute to increase this number of using

Google more.

>> Mhm. We're going to have to I mean you

what you're suggesting would mean we

give worse answers because if someone

got the answer quickly that would reduce

the amount of queries right and people

at Google Shashi Tako was another

engineer was saying yeah can we please

tell Sunda this because this doesn't

seem good. We can't just increase the

amount of queries. That would just mean

that people would have to search more

which would make the product worse.

>> But but it would make them more money.

You saying you'd show them more ads. So

if you're spending more time on Google

because Google's work,

>> but is this linked to AI doing code?

>> Oh, I'll get there. So

>> this is the problem is is that this guy

called Pragar Ragavan who's the head of

ads at the time was pushing pushing and

saying, "No, we need to make more

queries happen. Got to make it happen."

and Nick Fox who was there as well I

believe was actually taking over Google

search got to make them go up this is

our new reality sometime in early 2020

propagar ragavan takes over Google

search from then and this is this is

what I believe can't prove it if you go

and look around the various SEO sites

such journal and the various forums

Google stripped back a lot of the

suppression of spammy sites so that

people would be on Google more and then

over the course of time Google wanted to

create more queries and Google search

became much worse. It's why people

always do like plus Reddit or from

Reddit or what have you. It's because

the actual underlying search results of

Google had got worse. And then

Generative AI came along and Praagar,

wouldn't you know, it gets put to run

part of Gemini. And Google also was

having trouble getting people back on

Google. And what did they think they'd

do? Well, everyone's talking about

this AI thing. We'll just put it right

at the top so people have to stay at

Google. And actually, they'll use it

more because instead of searching

websites and doing that annoying thing

where they click away from Google,

they'll just only use Google. Instead of

generating answers, by which I mean

giving you search results you click

through, now Google is the answer. Is it

right? God know. It might tell you to

eat rocks, might eat poisonous

mushrooms. Maybe it'll give you a little

few links you could click through. But

the ideal situation was that AI was the

ultimate form of Google's evil which was

>> But I'm saying here I'm saying here but

that's not the fact that coders could

code on Google that's made Google worse.

That's human decisions have made it

worse.

>> Yes. And then there's the instability of

Google's platform which is actually I

should have probably led with that a

problem across the whole tech industry.

>> Okay. So you're saying that you're

saying Google is going down more.

>> Yes. Google is less stable. Google Docs

is a bugfest right now and has been for

a while. Google Sheets, same deal. And

the thing is, you're right, I'm being a

little unfair. This is everyone. It's

the same with Microsoft. It's the same

with Amazon. It's the same across.

>> How do we quantify that outside of

anecdotes? Like, is there a way to

>> You're right. I mean, GitHub downtime is

the best example. Amazon Web Services

went down two or three times this year

because of AI tools. And honestly,

you're right. It is kind of hard to

quantify outside of anecdotes. But I

challenge anyone listening to this. Go

and use a website these days and tell me

how well it works. Tell me how buggy it

is. Tell me how many problems even with

my iPhone. The supposed best UX in town.

Even the iPhone is a flipping mess these

days.

>> Okay, so the research says the short

answer is yes. Tech downtime and

software outages have demonstrabably

increased over the last few years and

industry data points directly to the

explosion of AI assisted coding as a

primary culprit. The problem is hitting

the tech industry from two entirely

different directions. The code itself is

getting buggier and the sheer volume of

AI activity is literally crashing the

underlying infrastructure. Interesting.

>> Yeah, that's because GitHub people are

just writing a bunch of code, pushing

it, and thus there's just more code on

there.

>> That's interesting.

>> Yeah, it's it's a real mess as well

because

open source has had this problem as well

because it's well-meaning people.

They're like, I learned a bit of code

with an LLM. I'm going to go out and do

some stuff. I'm going to make this

project better. And these people barely

understand what they're shipping. Or

maybe they understand a bit of code and

they say, "Oh, Dunning Krueger, this

I'm going to I'm just

like, I can understand some of this."

And now the code's all written and just

push it right now. So GitHub is flooded

with AI code.

>> This sounds like it's making humans

complacent.

>> It is

>> because we're going, "Okay, look, I let

it write the the code for the last 100

lines and it was broadly right. So the

next 100 lines, I won't check them as

much."

>> Yeah. Yeah. And that's human nature is

to get sort of to take shortcuts to

spend less energy on an activity if you

can right but the AI's still making the

mistake and we're still making all the

promises of AI that's the thing this

thing is meant to be this autonomous per

you say it can't be perfect I don't know

based on what Samman has been saying for

the last few years clammy Sammy has been

promising the world saying this will

replace software engineers Dario

Ammedday Wario himself has been saying

oh yeah 50% of white collar labor is

going to go away in the next few years.

These people are promising the world.

Again, if they were saying it would be

smaller and they were like, yeah, it

does have issues and we must be none of

this, oh, what if it wakes up and it's

super powerful. Just like, yeah, it's

probabilistic. It's going to make

mistakes and if you don't know what

you're doing, you don't really know what

you're looking at, you're going to miss

those mistakes and it's going to get

multiplicatively worse as you go when

you don't know what you're doing. So

yeah, human nature is part of it, but so

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>> I sat here with Dra from Uber and he was

saying that I think in a couple of years

time

we won't need drivers um for Uber

because the cars will drive themselves

like they'll be fully autonomous.

>> And I think if I'm not mistaken

driving is one of the biggest

professions on planet earth. So when you

hear people when you hear these CEOs

saying that there will be job disruption

>> you say that they are not telling the

truth.

>> Yes. Or they're guessing in a way that's

very good for them. Think about it from

perspective of Microsoft Sachin Nadella.

He's not going to be like yeah we don't

know if this is going to work mate. Of

course he's going to talk his book and

he's going to say yeah this is going to

replace all workers. It's going to be

amazing. He it's going to be so

powerful. And then he'll change his tune

and say actually it's not going to

replace workers. that make him more

powerful because the things aren't

catching up. Dor from Uber for example,

of course he's going to say if this

happens then that would be good for Uber

because Uber would just become an

autonomous taxi service. There's a

reason that Whimo's taken I I find Whimo

fascinating. I think that it's

really cool. I think there are

socioeconomic problems that will come

from it. I think there are actual real

problems that will emerge and also

>> what kind of problems?

>> Well, I mean socioeconomically there are

like you said one of the largest

employment centers in the world. I mean

just the economics of cabs will fall

apart but again we are nowhere nowhere

nowhere near that. We're not even close.

Whimo has had to do the smallest

rollouts and the most control things

because the problem with pretty much

every AI system but especially driving

is not the getting 95% of the way. It's

those edge cases. It's raining which is

a big problem for them in San Francisco.

It's a kid runs across the road but

they're wearing a high viz thing. Does

it even notice it's a child? Again, this

is a really interesting but very very

applicable example of uh the right

comparison to be made shouldn't be

autonomous vehicles versus perfection.

It should be autonomous vehicles versus

human drivers. I mean, I don't know if I

agree because a human driver might make

mistakes, sure, but again, not an expert

in autonomous cars. Just want to be

clear. But if we're pushing autonomous

cars out there willy-nilly and we're not

doing so in extremely controlled

environments, those edge cases will

multiply and be dangerous. Yeah, they

might be better at human drivers in some

ways, but they might also I was in Vegas

the other day and I was in a hotel and I

watched a bunch of Zuk's cars just get

stuck.

>> They're autonomous cars.

>> Yeah, they these weird boxy things. They

just blocked the exit. They just all

kind of lined up and just fell asleep. I

saw the same thing actually happen

outside of a hotel when I got out of a

Whimo in San Francisco. Just stopped at

the and then a bunch of cars and another

Whimo got stuck behind it. And these are

kind of

>> I've seen some human bad drivers as

well. I I agree, but it's just we have

control over deploying these bad or good

drivers. We have an ability to roll them

out slowly, which is exactly what we

should do. I'm not saying autonomous

cars are bad. I'm saying we need to be

so so so careful and treat them as

guilty and pro till proven innocent

because we can prove and also they have

people overlooking them. They actually

have people monitoring the roots. It is

something they cannot rush out and it

doesn't seem like they're rushing it,

which is good. and they're not promising

the world.

>> I do agree. Listen, I I'm a big fan of a

big fan of taxi drivers generally in

part because I spend a lot of time in

taxis and I think I'm not just getting

in there because I want to get to from A

to B. I'm getting in there for lots of

other reasons.

>> Yeah.

>> However, when I look at the stats