YouTube Transcript:
Silicon Valley Insider EXPOSES Cult-Like AI Companies | Aaron Bastani Meets Karen Hao

Skip watching entire videos - get the full transcript, search for keywords, and copy with one click.

Video Transcript

Video Summary

Summary

Core Theme

This content critically examines the current state and trajectory of Artificial Intelligence (AI) development, highlighting its profound societal, environmental, and ethical implications, and questioning the unchecked power and motivations of major tech companies.

I went to school with a lot of the

people that now build these

technologies. I went to school with some

of the executives at OpenAI. I don't

find these figures to be towering or

magical. Like I remember when we were

walking around dorm rooms together in

our pajamas and it it instilled in me

this understanding that technology is

always a product of human choices. And

different humans will have different

blind spots. And if you give a small

group of those people too much power to

develop technologies that will affect

billions of people's lives, inevitably

that is structurally unsound. [Music]

Artificial intelligence is the backbone

of some of the biggest companies in the

world right now. Multi-trillion dollar

companies can talk about nothing else

but AI. And of course, whenever it's

discussed in the media by politicians

and civil society, it's compared

invariably to the steam engine. It is

going to be the backbone for a new

machine age. Some people are really

optimistic about the possibilities it

will bring. They are the boosters, the

techno optimists, the technoutopians.

Others are doomers. They're down on AI.

AGI, artificial general intelligence,

it's never going to happen. And if it

does, well, it's going to look like The

Matrix or maybe even the Terminator and

Skynet. We don't want that, do we?

Today's guest, however, is not

speculating about the future. Instead,

they're very much immersed in the

present and indeed the recent past of

the artificial intelligence industry.

Karen How went to MIT. She studied

mechanical engineering. She knows the

STEM game inside out. But she made a

choice to go into journalism and media

to talk about these issues with a

fluency and a knowledge that very few

people have. Rather than speculate, what

Karen has done with this book is talk to

people in the field. 300 interviews with

260 people in the industry, 150

interviews with 90 employees of OpenAI,

both past and present. She has access to

emails, company Slack channels, the

works. This is the inside account of

open AI and the perils of artificial

intelligence, big tech, and big money

coming after well, pretty much

everything. It's an amazing story told

incredibly well. I hope you enjoy this

interview. Karen, how welcome to

Downstream. Thank you so much for having

me, Aaron. It's a real pleasure to have

you on. Right. We say we say that to

everybody, every guest. Uh, but I have

to say, and this has had rave reviews,

even though I've lost the uh the dust

jacket, Empire of AI with this huge poem

you've written, 421 pages, I think, not

including the acknowledgements.

Really, really interesting book. It's

about AI, this burgeoning industry in

the United States around artificial

intelligence. That word has been in

circulation since the 1950s, I believe.

Yeah. Before we drill down into your

book, what is AI and what do people mean

by AI when they talk about it in 2025 in

Silicon Valley? This is you would think

this is the easiest question, but this

is always the hardest question that I

get because artificial intelligence is

quite poorly defined.

We'll go back first to 1956 because I

feel like it helps understand a little

bit about why it's so poorly defined

today. But the term was originally

coined in 1956 by this Dartmouth

professor, assistant professor John

McCarthy. And he coined it to draw more

attention and more money to research

that he was originally doing under a

different name. And that was something

he has explicitly said a few decades

later. He said, "I invented the term

artificial intelligence to get money for

a summer study." And that kind of that

that marketing route to the phrase is

part of why it's really difficult to pin

down a specific definition today. The

other reason is because generally people

say that AI refers to the concept of

recreating human intelligence in

computers. But we also don't have a

scientific consensus around what human

intelligence is. So quite literally when

people say AI, they're referring to an

an umbrella of all these different types

of technologies that appear to simulate

different human behaviors or human

tasks. Um, but it really ranges from

something like Siri on your iPhone all

the way to chat GBT, which behind the

scenes are actually really, really

different ways of operating. They're

totally different scales in terms of the

consumption of the technologies. Um, and

of course they they often have different

use cases as well. So right now when

OpenAI meta when they use those words AI

in regards to their products

specifically, what are they talking

about? Most often they are now talking

about what are called deep learning

systems. So these are systems that train

on loads of data and you have software

that can statistically compute the

patterns in that data and then that

model is used to then make decisions or

generate text or make predictions. So

most modern-day AI systems built by

companies like Meta, by OpenAI, by

Google are now these deep learning

systems. So deep learning is the is is

the same as machine learning is the same

as neural networks. Deep learning is

these are synonyms. Deep learning is a

subcategory of machine learning. Machine

learning refers to a specific branch of

AI where you build software that

calculates patterns in data. Deep

learning is when you're specifically

using neural networks to calculate those

patterns. So you have a what I call um

one of the founding fathers of AI used

to call AI a suitcase word. So you

because you can put whatever you want in

the suitcase and suddenly it AI means

something different. So we have this

suitcase word of AI and then under that

any datadriven AI techniques are called

machine learning and then any neural

network datadriven techniques are called

deep learning. So it's the smallest

circle within this broader suitcase

work. So deep learning and neural

networks are kind of interchangeable.

Not exactly in the sense that neural

networks are referring to a piece of

software and deep learning is referring

to the process that the software is

doing, right? Yeah. Do you get upset

when when when politicians so in this

country we have a prime minister called

Karma, you know, and they say we think

the NHS can save, you know, 20% by, you

know, using AI applications, right? Do

you sort of think my good like these

people have no idea what they're talking

about because that is such an expansive

term. It can't really it's its political

convenience is precisely doesn't mean

anything. It it does frustrate me a

little bit. I so I often use the analogy

that AI is like the word transportation.

I mean if transportation can refer to

bicycles or rockets or self-driving cars

or gas guzzling trucks, you know, like

they're all different modes of

transportation, serve different

purposes, different costbenefit

analyses. And you would never have a

politician say we need more

transportation to mitigate climate

change. You would be like but what kind

of trans like what are you talking

about? Well yeah

we need more transportation to stimulate

the economy. I mean maybe in that case

it's like it's just yeah like there is a vagueness

vagueness

around the AI discussion that is really

unproductive and I think a lot of that

leads to confusion where people think AI

equals one thing and AI equals progress

and so we should just have all of it but

actually if we were to use the

transportation analogy you know like

having more bicycles having more public

transit sounds great but if someone were

actually referring to just like using

rockets to commute from, you know, um,

Dublin to to London and we were like,

everyone should get a rocket now, like

that's going to bring us more progress,

you'd be like, what are you talking

about? And that's effectively what these

companies are doing with general

intelligence. When you're giving people

tools for free with regards to

generative AI to just generate stupid

images of nonsense, that's kind of what

we're doing, right? I I presume you

would take that analogy to that level.

It's like saying, "Let's use a rocket to

get from Dublin to London to Paris."

Yeah, exactly. Like, it's not fit for

the task. Um, and the the extraordinary

amount of environmental costs for flying

that rocket when you could have flown a

much more efficient plane to do the same

thing is like what are you doing, you

know? Um, and that's some one of the

things that people don't really realize

about artificial or about generative AI

is that the resource consumption

required to develop these models and

also use these models is quite

extraordinary and often times people are

using them for tasks that could be

achieved with highly efficient different

AI techniques and you're but because we

use the sweeping term AI to mean

anything then people just think, "Oh,

yeah, right, right. I'm just going to

use Chat GBT for my one-stop shop

solution for anything AI related." So,

right now, data centers globally, I

think, are about 3 3.5% of CO2

emissions. I think the the data centers

for AI are a tiny fraction of that, but

obviously they're growing at an

extraordinary pace. Yeah.

Are there any numbers out there with

regards to projected CO2 emissions of

data centers globally 5, 10, 15 years

from now or is that also it's so recent

that we can't really speculate about the

numbers involved? There are numbers

around the energy consumption which you

could then use to kind of try and

project uh project carbon emissions. So

there was a McKenzie report that

recently projected that based on the

current pace of data center and

supercomputer expansion for the

development and deployment of AI

technologies, we would need to add

around half to 1.2 times the amount of

energy consumed in the UK annually to

the global grid in the next 5 years.

Wow. Yeah. And most of that will be

serviced by fossil fuels. This is

something that Sam Alman actually even

said in front of Senate the Senate a

couple weeks ago. He said it will most

probably be natural gas. So he actually

picked the nicest fossil fuel. But we

already seeing reports of coal plants

having their lives extended. They were

meant to be retired, but they're no

longer being retired explicitly to power

data center development. We're seeing

reports of Elon Musk's XAI, the giant

supercomputer that he built called

Colossus in Memphis, Tennessee. It is

being powered with around 35 unlicensed

methane gas turbines that are pumping

thousands of toxic air pollutants into

the air into that community. So this

data center acceleration is not just

accelerating the climate crisis. It also

is accelerating the public health crisis

of people's ability to access clean air

as well as clean water. So one of the

aspects that's really undertalked about

with this kind of AI development, the

OpenAI's version of AI development is

that these data data centers need fresh

water to cool because if they used any

other kind of water, it would erode

corrode the equipment. It would lead to

bacterial growth. And so most often

these data centers actually use public

drinking water because when they enter

into a community that is the

infrastructure that's already laid to

deliver the fresh water to companies, to

businesses, to residents. And so one of

the things that I highlight in my book

is there are many many communities that

are already they do not have sufficient

drinking water even for people. And I

went to Monte Vido Uruguay to speak with

people about a historic level of drought

that they were experiencing where the

Monte Vido government literally did not

have enough water to put into the public

drinking water supply. So they were

mixing toxic waste water in just so

people could have something come out of

their taps when they opened them. And

for people that were too poor to buy

bottled water, that is what they were

drinking. And women were having higher

rates of miscarriages. uh elderly were

having an exacerbation or inflammation

of their chronic diseases. And in the

middle of that, Google proposed to build

a data center that would use more

drinking water. This is called potable

water, right? This is a potable water.

Yeah. Exactly. You can't use seaw water

because of the saline aspect that you

Exactly. Exactly. And Bloomberg recently

had a story that said 2third of the data

centers now being built for AI

development are in fact going into water

You said a moment ago about um XAI

unlicensed energy generation using

methane gas. When you say unlicensed,

what do you mean? As in the company just

decided to completely ignore existing

environmental regulations when they

installed those methane gas turbines.

And this is actually a really

one of the one of the things that I

concluded by the end of my reporting was

not only are these companies really

corporate empires, but also that if we

allow them to be unfettered in their

access to resources and unfettered in

their expansion, they will ultimately

erode democracy. Like that is the

greatest threat of their behaviors. And

what XAI is doing is a perfect example

of at the smallest level

the they're enter these companies are

entering into communities and completely

hijacking existing laws, existing

regulations, existing democratic

processes to build the infrastructure

for their expansion. And we're seeing

this hijacking of the democratic process

at every level, the smallest local

levels all the way to the international level.

level.

It's kind of that that orthodoxy of seek

permission after you do something is now

I mean when you start applying this is

business as usual for those companies

that's part of their expansion strategy

which we'll talk about and we're going

to talk about um the sort of global

colonial aspect as well with regards to

resource consumption resource use just

bring it back to the US again because at

the top of this conversation I want to

offer a bit of a primer to people out

there who they maybe know what AI is

they maybe have used chat GPT

what are the major companies we're now

talking about in this space particularly

in the United States of America over the

last 5 years who who are the people in

this race to AGI Mhm. allegedly

um artificial general intelligence

something which you know either might be

sentient probably not or capable of

augmenting its own intelligence more

plausible who are the major players in

that field right now one caveat on AGI

is that it's as illdefined as the term

AI um so I like to think of it as just a

rebranding you know the the entire

history of AI has just been been about

rebranding and the term deep learning

was also a rebranding so anyway but the

players First, OpenAI of course they

were the ones that fired the first shot

with chat GBT anthropic major competitor

Google Meta Microsoft they're the older

uh internet giants that are now also

racing to deploy these technologies um

super safe super intelligence which spun

out of also uh o an open AI splinter

there are many openai splinters so this

was founded very recently by the former

chief scientist of OpenAI and Thinking

Machines Lab founded very recently by a

former chief technology officer of

OpenAI and Amazon is now trying to get

into the game as well. So basic and

Apple is also trying to get in the game.

So basically all the older generation

tech giants as well as a new crop of AI

players are all jostling in this space

and that's just the US, right? And

that's just the US, right? So the the

Chinese ecosystem is interesting because

they're not so

um they don't really use the term AGI

like that this is like a very kind of

unique thing about the US ecosystem is

that there's a quasi religious fervor

around that underpins the construction

of AI products and services whereas in

China it's much more like these are

businesses we're building products that

users are going to use. So, if you're

just looking at companies that are

building chat bots that are sort of akin

to chat GBT, then we're talking about

Bite Dance, owner of Tik Tok. Um,

Alibaba, the equivalent of Amazon, BYU,

the equivalent of Google, Huawei, the

equivalent of Apple, and uh, Tencent,

the um, what is the equivalent of

Tencent? I I guess Meta is the

equivalent of Tencent. So, they're also

building on these things. And there's

similarly a crop of startups that are

moving into the generative AI space. And

in Europe, we've got the little tidlers

like Mistral in France, you know, really

not not at the races cuz we're Europe.

Um what's the business case for all

this? Because obviously you've got

massive companies

often driven by maximizing shareholder

value, multi-trillion dollar valuations.

You do these things, you invest money to

make money as a capitalist society. So

what what is the business case made by

say Microsoft when they have their

shareholder meetings and they say we're

going to allocate 4050 billion dollars

towards building data centers and so on.

So it's really it's interesting that you

mentioned Microsoft because Microsoft

has recently been pulling back their

investments in data centers. They they

went all in and now they're really

rapidly starting to abandon data center

projects. So to answer your question, it

is really unclear what the business case

is and Microsoft has been one of the

first companies to start acknowledging

that and Satia Nadella has come onto

some podcasts recently where he actually

stunned some people in the industry by

being quite skeptical of whether or not

this race to AGI was productive. Um but

one of the things that I I really felt

after reporting what is driving the

fervor is you can't actually fully

understand it as just a story about

money. It has to also be understood as a

story of ideology because when in the

absence of a business case then you ask

why are people still doing this? And the

answer is there are people who genuinely

fervently believe and they talk about it

as a belief in this idea that we can

fundamentally recreate human

intelligence and that if we can do that

there is no other more important thing

in the world because what else like how

else you should you be dedicating your

time other than to bring about this

civilizationally transformative

technology. And so that's part of why

what drives open AI, what drives

Enthropic, what drives safe super

intelligence, these other smaller

startups. And then the bigger giants

which are more business focused and more

classic companies that actually care

about their bottom lines, they end up

getting pressured because shareholders

are seeing the enormous amounts of

investment by these startups and they're

seeing users start shifting from Google

search to using chat GBT as search. Chat

GBT should not be used as search but

consumers think that it is. And then

shareholders ask in Google's shareholder

meetings, what are you doing with AI?

What is your AI strategy? Why aren't you

investing in this technology? And so

then all of the other giants end up

racing in the same direction.

What does Warren Buffett make of it?

That's what I want to know. Is he sort

of like you guys if he's like you guys

are wasting your money? He's like he's

he's probably right. I have no idea. Has

he invested in AI? No, I don't I don't

think so. He just sticks to Coke and

these sorts of things, doesn't he? I

mean there's there's two rational. So I

think one is like you say a quasa

religious fervor has inflected the

investment decisions of some of the

world's most um valuable companies which

is just an extraordinary thing to even

think about. I suppose the other one is

that a lot of people in this space, as

we'll talk about in a moment, are

heavily influenced by people like Peter

Teal. And Peter Teal's orthodoxy is that

competition is for idiots, right? If

you're going to start a business, it has

to be a monopoly. And I can only presume

that companies like Microsoft, etc.,

Although maybe that's not the best

example now given recent events, but

XAI, Open AI, Meta, the only reason you

would invest ultimately hundreds of

billions, trillions of dollars into this

is because first mover advantage gives

you a monopoly on the most

transformational technology since the

steam engine. Yeah. I mean, that's the

only way I can make sense of it, right?

Have Have they has anybody in that space

kind of said that we want a we want the

monopoly on AGI? We want to be the the

Facebook of AGI. Well, what what OpenAI

often says to investors is if you make

this seemingly fantastical bid into our

technology, you could get the biggest

returns you've ever seen in your life

because we will then be able to use your

funding to get to AGI first. So, it's

still riding on this concept of the fact

that there might be an AGI, which is

high, it's not like rooted in scientific

evidence. Um, and even if we fail, we

will successfully be able to automate a

lot of human tasks to the point where we

can convince a lot of executives to hire

our software instead of a labor force.

So that in and of itself could

potentially end up generating enough

returns for you more than you've ever

seen before. So that's usually the pitch

that they make. But you know it is a

huge risky bargain that these investors

are actually pitching into. And and you

know a lot of investors they they have a

bandwagon mentality like they aren't

necessarily doing their own analysis to

say let me do this investment. They're

just seeing everyone glom onto this

thing and they're like well I don't want

to miss out. Why don't we glom on as

well? But you know, there are some

investors that have actually recently

reached out to me to be like, one of the

most under reportported stories right

now is the amount of risk that is not

just being taken on by these VCs is

actually being taken on by the entire

economy because the money that these

investors are investing comes from like

university endowments and things like

that. So if the bubble pops, it doesn't

just pop for Silicon Valley, it actually

has will have ripple effects across the

global economy. I mean, when you look at

the the the sort of e-commerce bubble in

the late 90s, okay, it was a bubble, you

know, pets.com or whatever. It was, you

know, had these crazy valuations, but,

you know, buying and selling goods and

services offline and then taking that

online. I mean, that makes sense. That's

a that's a plausible sort of commercial

model, but like you say, nobody's really

done that with artificial intelligence.

It does kind of feel like, you know, you

read these stories about Chulip Mania in

17th century Holland, and it does kind

of feel very similar. Um, you mentioned

Open AI and we've talked about it many

times and of course OpenAI is is the

central organization in this book.

What's the big idea behind Open AI? When

it starts and when does it start? Let's

let's end of 2015. 2015. So, it's 10

years old. What are the animating values

that give birth to open AAI? So open

started as a nonprofit which many people

don't realize based on the fact that

it's one of the most capitalistic if not

the most capitalistic organization in

Silicon Valley today.

But it was co-founded by Elon Musk and

Sam Alman as a bid to try and create a

fundamental AI research lab that could

develop this transformative technology

without any kind of commercial

pressures. So they positioned themselves

as the anti-Silicon Valley, the anti-

Google because Google at the time was

the main driver of AI development. They

had developed a monopoly on some top AI

research scientists and Musk in

particular had this really great fear of

not just Google but Google's deep mind

uh Google's acquisition of deep mind

where he was very worried that this

consolidation of some of the brightest

minds would lead to the development of

AI that would go very badly wrong. And

what he meant by very badly wrong was it

could one day develop sentience

consciousness, go rogue and kill all

humans on the planet. And because of

that fear, Alman and Musk then thought,

we need to do a nonprofit, not have

these profit- driven incentives. We're

going to focus on being completely open,

transparent, and also collaborative to

the point of self-sacrificing if

necessary. If another lab starts making

faster progress than us on AI and on the

quest to AGI, we will actually

just join up with them. We will we will

dissolve our own organization and join

up with them and uh that didn't hold for

very long.

So what's their theory behind that?

Because you know at that point Google is

now about maybe 2015 is maybe the

world's most valuable company. I don't

know. is certainly up there and this is

a nonprofit. Yeah. So how how are they

going to achieve AGI before Google?

So initially the bottleneck that they

saw was talent like right Google has

this monopoly in talent. We need to chip

away at that monopoly and get some of

those Google researchers to come to us

and also start acquiring PhD students

that are just coming out of uni. And

because of that I have come to speculate

this is not based on any documents that

I read or anything. I've come to

speculate that part of the reason why

they started as a nonprofit in the first

place is because it was a great

recruitment tool for getting at that bottleneck.

bottleneck.

They could not compete on salaries with

Google, but they could compete on a

sense of mission. And in fact, when

Alman was recruiting the chief

scientist, Deia Sudskver, who was the

critical first acquisition of talent,

that then led to many other scientists

being really interested in working for

OpenAI. He appealed to Sudskver's sense

of purpose, like do you want to do you

want a big salary and just to work for a

for-profit company or do you want to

take a pay cut and do something big with

your life? And it was actually that

reason that Sutzk said, you know what,

you're right. I I do want to work for a

nonprofit. And so that's how they

initially conceived of competing with

Google was we we're starting a little

bit late to the game. How do we first

get a bunch of really really smart

people to join us? let's create this

really big sense of mission and and the

I open the book with two quotes in the

epigraph and one of them is from Sam

Alman writing a blog post in 2013 and he

quotes someone else that says successful

people build companies more successful

people build countries the most

successful people build religions and

then he reflects on this and says it

seems to me that the most successful

founders in the world don't actually set

off to build a company. They set off to

build a religion. And it turns out

building a company is the easiest way to

do so. And so, you know, it's not like

2013 and then 2015 he creates OpenAI as

a nonprofit.

It's important to say as well, Sam

Melman is not some sort of idealistic

um porpa, you know, he's working at Y

Combinator. He is very much inshed

within the Silicon Valley elite. Um,

I suppose also there's tax as well,

right? If you're a nonprofit, you've got

the mission, you've also got a bunch of

tax breaks which you don't have as a for

profit. So maybe there's a very cynical

genesis there. Um, but I suppose just

reading your book and becoming more

familiar with the arguments over time,

you know, clearly the amount of compute

you have is is was always going to be

critical. And if you if you believe on

the in the um neural network model, the

deep learning model, the amount of

compute you have is always going to be

critical. And it just seems implausible

that a nonprofit could ever have been

able to compete with Google, for

instance, ever. like it seems

implausible because you have to spend as

we now see tens of billions, hundreds of

billions of dollars on compute.

Did nobody say that? Did nobody say,

"Hey, you know, like the bottleneck

isn't just talent action. It's being

able to spend hundreds of billions of

dollars on these Nvidia GPUs." It's so

interesting because at the time the idea

that you needed a lot of compute was actually

actually

neither very popular nor one that was

seen as that scientifically rigorous.

Right? So there were there were many

different ideas of how to advance AI.

One was we already actually have all the

techniques that we need and we just need

to scale them. But that was considered a

very extreme opinion. And then on the

other extreme it was we don't even have

the techniques yet. And interestingly

recently there's a New York Times story

that says why we likely won't get to AGI

anytime soon by Cade Mets. And he cites

this stat that 75%

of the longest standing most respected

AI researchers actually still think to

this day we don't actually have the

techniques to get to AGI if we will

ever. So, it's we're we're kind of

coming full circle now and it is

starting to become unpopular again. This

idea that you can just scale your way to

so-called intelligence, but that was the

research vibe when openi started was we

can actually maybe just innovate on

techniques, right? And then very quickly

because Ilas Sutskver in particular was

a scientist who anomalously did think

that scaling was possible and because

Altman loved the idea of adding zeros to

things from his career in Silicon Valley

and because Greg Brockman the chief

technology officer also very Silicon

Valley entrepreneur liked that idea as

well then they identified why don't we

go for scale because that is going to be

the fastest way to see whether we can

beat Google.

And once they made that decision about

less than a year in roughly is when they

started actually talking about that,

that's when they decided we actually

need to convert into a forprofit because

the bottleneck has shifted now from

acquiring talent to acquiring capital.

And that is also why Elon Musk and Sam

Alman ended up having a falling out

because when they started discussing a

for-profit conversion, both Elon Musk

and Sam Alman each wanted to be the CEO

of that forprofit. And so they couldn't

agree. And originally Ilascover and Greg

Brockman chose Musk. They thought that

Musk would be the better leader of

OpenAI. But then Altman essentially, and

this is something that is very classic,

a very classic pattern in his career,

became very persuasive to Brockman, who

he had had a long-term relationship with

about why it could actually be dangerous

to go with Musk and like like I would

definitely be the more responsible

leader so on and so forth. And then

Brockman convinces Susper and the two

chief scientist, chief technology

officer pivot their decision and they go

with Alman and then Musk leaves in a

huff and says I don't want to be part of

this anymore which has become rather

typical of the man hasn't it

subsequently but that is incredible

really. So by 2016 there's a recognition

that in terms of capital investment

they're going to have to go toe-to-toe

with maybe at that point the world's

biggest company and they're a nonprofit.

Yeah. I just find it weird that and but

lots of people bought the propaganda

that open AI was in some way open. Yeah.

What did the open stand for by the way?

The open originally stood for open

source which in the first year of open

AI they really did open source things.

They did research and then they would

put all their code online. So it it it

really was like they did they did what

they said and then the moment that they

realized we got to go for scale then

everything shifted. It's such an amazing

story and so emblematic of the 2010s

that you have this organization which

presents itself as effectively an

extension of activism. Yeah. You know,

ends up becoming today some people value

open AI at $300 billion. Yeah. Um and

it's doing all these terrible things

which we're going to talk about. Sam

Alman specifically,

who is he? What's his background? How

does this guy who nobody's heard of

become the CEO of a company which today

is you know it's it's almost more

valuable than any company in Europe for

instance. Yeah. Altman is he's spent his

entire career in Silicon Valley. He was

a first a founder, a startup founder

himself and he was part of the first

batch of companies that joined Y

Combinator, one of now today one of the

most prestigious startup accelerators in

Silicon Valley. But at the time he was

he was the very first class and no one

really knew what YC was. He did that for

seven years. He was running a company

called Looped, which was a mobile-based

social media platform, effectively a

Foursquare competitor, but which

actually started earlier than

Foursquare. It didn't do very well. It

was sold off for parts and but what he

did do very well during that time was

ingratiate himself with very powerful

networks in Silicon Valley. So, one of

the first and longest mentors that he

ended up having throughout his career is

Paul Graham, the founder of Y

Combinator, who then plucked Sam Alman

to be his successor. And Sam Alman then

at a very young age became president of

YC. And then he ended up doing that for

around 5 years. And during his tenure at

YC, he dramatically expanded YC's

portfolio of companies. He started

investing not just in software companies

but also pushing into quantum into

self-driving cars into fusion and really

going for those hard tech engineering

challenges. And if you look at how he

ended up then as a CEO of OpenAI,

I think that he basically was trying to

figure out what is going to be the next

big technology wave. Let me test out all

of these different things. position

myself as involved in all of these

different things. Um, so in addition to

all his investments, he started

cultivating this idea of AI also seems

like maybe it'll be big. Let me start

working on an idea for a fundamental AI

research lab that becomes open AI. And

once open AI started being the fastest

one taking off, then Alman hops over and

becomes CEO.

He hops over. So how does that happen?

Where does he come from? Cuz like you

say originally it's got people like he's

there. Who's there first? him or I sits

technically Alman recruited Satzkever

but Alman was only a a chairman he he

didn't take an executive role at OpenAI

even though he founded the company right

and similarly with Musk Musk didn't have

an executive role he was just a

co-chairman so it was just the two of

them that were chairman of the board and

Ilia Szver and Greg Machmann were the

main people the main executives that

were actually running the company

dayto-day in the beginning I mean I have

to say reading the book Sam Alman he

comes across as a a master manipulator

like masterful manipulator and

understander of human psychology there's

this great quote let me get it up uh

which you have I think it's from Paul Graham

Graham

um Sam Alman has it you could parachute

him into an island full of cannibals and

come back in 5 years and he'd be the

king if you're Sam Alman you don't have

to be profitable to convey to investors

that you will succeed with or without

them I mean

he just sounds He's also described, by

the way, as a once in a generation

fundraising talent. I think that's by

you. Yeah. Um,

how how is he able to just basically

come out of nowhere and compete with

people like Elon Musk, Zuckerberg as

this kind of intellectual heavyweight in

Silicon Valley in regards to one of the

major growth technologies of our of our

decade. So, from the public's

perspective, he came out of nowhere. But

within the tech industry, everyone knew

Sam Alman. You know, like I I as someone

who worked in tech, like I knew Sam

Alman ages ago because Y Cominator was

just so important. It was as a CEO of

potential company that valuable. Was it

always something that he might be in?

No, I don't think people ever thought

that he would jump to become the CEO of

a company because he has such an

investor mindset and his approach has

always been to be involved in many many

companies. I mean he invested in

hundreds of startups as both the

president of YC and running some uh

personal investment funds as well but

people he was well respected within the

valley. He was seen as a critical

lynchpin of the entire startup ecosystem

and not just by people within the

industry but by policy makers which is

key. He started cultivating

relationships with politicians very very

early on in his tenure as the president

of YC. And for example, I talk in my

book about how Ash Carter, the head of

the Department of Defense under the

Obama administration, came to Altman

asking, "How can we get more young tech

entrepreneurs to partner with the US

government?" So, he was seen as a

gateway into the valley. And

obviously the valley isn't just made of

of of startups. There's also the tech

giants. But back then like starting a

startup was way cooler than working at a

tech giant because Google, Microsoft,

they were considered the older safer

options if you really wanted job

security. But if you wanted to be an

innovator, if you wanted to do

breathtaking things, you would build a

startup. And then that start your number

one goal as a startup founder was to get

into YC. So Altman was the pinnacle. He

was he was a he was emblematic of the

pinnacle of success in the valley. And

he even if his net worth wasn't the same

as other people in terms of his social

capital, his networking, he understood

early on that's where the real value

lies. Exactly. So interesting. I mean

some notes that I wrote down um cuz

there are there are points where I'm

thinking why on earth is this gentleman

the CEO of such a valuable company he

seems kind of useless and the notes I

had down were um people pleaser yes liar

how' you become the CEO of such a

successful company

maybe you think that or don't think that

I don't know I mean at points it kind it

comes across as almost psychotic the

capacity to to lie.

Here's an interesting question for me

and I don't know I don't know how

comfortable you are with answering it.

In writing this book, there's another

alternative timeline where you basically

write a hography of Sam

and you leave all of that out, right?

There are other writers out there, I

won't name them, they sell a ton of

books, and they write very positive,

affirming um biographies of these

visionary leaders, whether it's Elon

Musk or Steve Jobs, etc.

Why didn't you just write that book

about Sam Alman? You know, you would

have made a ton more money.

Right. And I'm but I'm reading this

stuff and I'm thinking, my good this and

it's so deaf and nuance your your

portrait of Sam Alman. I just think the

guy I mean this this is going to really

hurt him when he reads this stuff. I

imagine why didn't you do that? Take the

easy route. I don't know that that would

have been the easy route.

I mean, I just wrote the facts and the

facts come out that way, you know, like

I interviewed over 260 people across 300

different interviews and over 150 of

those interviews were with people who

either worked at the company or were

close to Sam Alman. And that's just what

they presented was all of the details

that I ended up putting in. And one of

the things that he that just came

through again and again and again, well,

two two things that came through again

and again,

no matter how long someone worked with

him or how closely they worked with him,

they would always say to me, at the end

of the day, I don't know what Sam

believes. So that's interesting. M and

then the other thing that came through

was I would ask them well what did he

say to you he believed in this meeting

at this point in time for why the

company needed to do this XYZ thing and

the answer was he always said he

believed what that person believed

except because I interviewed so many

people who have very divergent beliefs

and I was like wait a minute he's saying

that he believes what this person

believes and then what that person

believes and they're literally

diametrically opposite. So yeah, so I

just I just ended up documenting all of

those different details to illustrate

how people feel about him. I mean, he's

a polarizing figure both extreme in the

positive and negative direction. Some

people feel he is the greatest tech um

leader of our generation and they but

they don't say that he is honest when

they say that. They just say that he's

one of the most phenomenal assets for

achieving a vision of the future that

they really agree with. And then there

are other people who hate his guts and

say that he is the greatest threat ever.

And it really also comes down to whether

or not they agree with his vision and

they don't. And so then his persuasive

powers suddenly become manipulative

tactics. M I mean if you compare him to

somebody like Elon Musk as a CEO who is

obviously far from perfect but Elon Musk

makes makes big bets. He has gut

instincts. He's very happy to alienate

people if he thinks he's right about

something. And you know obviously I

don't agree with him on many many things

but that's that's quite a sort of

there's an archetype with regards to a

business leader that that looks like

that. And then you got somebody like Sam

Alman. He's doing all of these things.

Like I say, the peopleleasing, the

conflict aversion, and yet he's managed

to lead this company to essentially a

third of a trillion valuation. He must

obviously be doing something right as

well. So what are his sort of

comparative advantages as a business

leader cuz on paper I read all that

stuff and I think the guy wouldn't be

able to get up in the morning and make

breakfast and yet he's accomplished some

extraordinary things. Yeah, I think it

really comes down to he really he does

understand human psychology very well,

which not only is helpful in

getting people to join in on his quest.

So, he's great at at acquiring talent

and then he's said himself like I'm I'm

a visionary leader. I'm not an

operational leader and my best skill is

to acquire the best people that then

operationalize the thing. So, he's he's

good at persuading people into joining

his quest. He's good at persuading what

whoever has access to whatever resource

he needs to then give him that resource

whether it's capital, land, energy, water,

water,

laws, you know. Um, and then he is

people have said that he instills a very

powerful sense of belief in his vision

and in their ability to then do it.

He's good. We say in English soccer, we

would say good man manager. He can

inspire people. He inspires people to do

things that they didn't think that they

would be able to do. Yeah. Um but yeah,

but I mean

this is this is why there's so much

controversy. He is such a polarizing

encount everyone has a very personalized

encounter relationship with him because

he he often um he he does his best work

in one-on-one meetings when he can say

whatever he needs to say to get you to

do believe achieve whatever it is that

he needs you to do. And that's also part

of the reason why there's so many

diverging like people that are like,

"Oh, I think he believes this. I think

he believes that." And they're like

totally diverging. It's because he's

he's having these very personalized

conversations with people. Um, and so

some people end up coming out of those

personalized meetings feeling totally

transformed in the positive direction,

being like, I feel super human. I can

now do all these things and it's in the

direction that I want to go. It's I'm

building the future that that he sees

and I see. and we're like aligned and

and then other people end up coming out

of these meetings feeling like was I

played you know like was this was he

just telling me all these things to try

and get me to do something that's

actually fundamentally against my

values. You said you spoke to 150 people

who were connected with open AI um over

150 interviews. Yeah. Yeah. Sorry. 150

interviews 250 interviews altogether. 27

people altogether. The the numbers were

No, but it's absolutely incredible. I

should have said this right at the start

really. What's your what's your personal

sort of bio on all this stuff? Because

of course when people out of journalism

media cover technology, the intersection

of that with politics, we go, well, they

don't really know what they're talking

about. They're generalists because they

come out of journalism. What's your

background? Because it's quite

particular. I studied mechanical

engineering at MIT for undergrad and I

went and worked in Silicon Valley

because that's what I thought I wanted

to do. I lasted a year before I realized

it was absolutely not what I wanted to

do. And then I went into journalism. And

the reason why I had such a visceral

reaction against Silicon Valley is

because I was quite interested in

sustainability and how to mitigate

climate change. And the why I went to

study engineering in the first place was

I thought that technology could be a

great tool for social change and shaping

consumer behaviors to to prevent us from

planetary disaster. And I realized that

Silicon Valley's technology incentive

structure incentive structures for

producing technology

were not actually leading us to develop

technologies in the public interest. And

in fact, most often it was leading to

technologies that were eroding the

public interest. And the problems like

mitigation of climate change that I was

interested in were not profitable

problems. But that is ultimately what

Silicon Valley builds. They want to

build profitable technologies. And so it

just seemed to me that it didn't really

make sense to try and continue doing

what I wanted to do within a structure

that didn't reward that. Yeah. And then

I thought, well, I've always liked

writing. Maybe I can use writing as a

tool for social change. So I switched to

journalism. You went to MIT Review,

right? And then I went to a few

publications and then eventually MIT

Technology Review to cover AI and then

Wall Street Journal. And then the Wall

Street Journal. I mean, these are big

just just so people know there's real

there's real credibility behind this.

All these interviews, this CV. Um,

and it's interesting as well you say, I

wouldn't write a hography, I just wrote

what was there. I mean, maybe that's

partly an an extension of your sort of

STEM background, right? You know, rather

than writing like propaganda on a puff

piece, which let's be honest is is most

coverage of of the sector, but it's

true, right? Well, you know, people

often ask me this is like how much does

did my engineering degree help me in

reporting on this? And I think it helps

me in ways that are not what people

would typically assume. I went to school

with a lot of the people that now build

these technologies. I went to school

with some of the executives at OpenAI,

you know, and so for me, I do not find

there to be magic. I don't find these

figures to be towering or magical. Like

I remember when we were walking around

dorm rooms together in our pajamas and

it it instilled in me this understanding

that technology is always a product of

human choices. And different humans will

have different blind spots. And if you

give a small group of those people too

much power to develop technologies that

will affect billions of people's lives,

inevitably that is structurally unsound.

You like we should not be allowing small

groups of individuals to concentrate

such profound influence on society when

it is not you cannot expect any

individual to have such great visibility

into everything that's happening in the

world and perfectly understand how to

craft a one-sizefits-all technology that

ends up being profoundly beneficial for

everyone. Like that it just doesn't make

sense at all. Um, and I think the other

thing that it really helps me with is

it g Silicon Valley is an extremely

elitist place and it allows me to have

an honest conversation with people

faster because if they start

stonewalling me or like trying to

pretend that there's certain things that

these technologies are capable of that

they're not actually capable of, I will

just slap my MIT degree down and be

like, "Cut the bull crap." like tell me

what's actually happening. And it is a

shortcut to getting them to just speak

more honestly to me, but it's not

actually because

of what I studied. It's more just that

it signals to them that they need to

speed up their throat clearing. That's

really interesting though. Yeah, because

I do I do feel like lots of coverage of

this sector. I mean I again I can only

speak in regards to the UK and we're a

tidler compared to to you guys but at

the intersection of particularly

politics and technology the coverage by

political journalists at Westminster you

know K star and Rachel Reeves say we're

going to build more data centers isn't

that fantastic actually not necessarily

they're not going to create that many

jobs once they're built they can use a

ton of energy ton of water what's the

upside for the UK taxpayer there is very

little interrogation of just the press

releases yeah um and it's really

interesting to me that you've come out

of MIT and then you've taken this

trajectory is This stuff you just talked

about knowing these people, this tiny

group of people whose decisions now

affect billions already. Is this stuff a

on the present trajectory? Is it an

existential challenge to democracy? And

challenge is is speculative. Is it going

to end democracy?

I think it is greatly threatening and

increasing the likelihood of democracy's

demise. But I I never make predictions of

this outcome will happen because it

makes it sound inevitable. And one of

the reasons why I wrote the book is

because I very much believe that we can

change that and people can act now to

shape the future so that we don't lose

democracy. But on this trajectory,

right, if the next 20 years, like the

last 20 years, on this trajectory for

sure, I think it will end democracy.

We've really screwed up in the last 20

years, right? I wonder, you know, it's

kind of Gosh. Yeah.

I'll give it maybe 20 years. 20 years.

Yeah. Yeah. We used to have this thing

called privacy, high streets, childhood,

all gone. Um, you've said that um what

OpenAI did in the last few years is they

started blowing up the amount of data

and the size of the computers that need

to do this training in regards to the um

in regards to the um deep learning.

Give me a sense of the scale. We've

talked a little bit about the data

centers, but how much energy, land,

water is being used to power open AI

just specifically as one company. Yeah.

To power open. That's really hard. Um

because they they don't actually tell us

this. So we only have figures for the

industry at large and the amount of data

centers. So it's not in their annual

reports for instance. No. Well, they

don't have annual reports because

they're not a public company. Of course.

Yeah. Huh. So that's, you know, one of

the ways that and and actually it

doesn't matter if they're a public

company because Google and Microsoft,

they do have annual reports where they

say how much capital they've spent on

data center construction. They do not

break down how much of those data

centers are being used for AI. They also

have sustainability reports where they

talk about the water and carbon and

things like that, but they do not break

down how much of that is coming from AI

either. And they also massage that data

a lot to make it seem better than it

actually is. But even with the

massaging, there was that story 2 years

ago or sorry la last year 2024 where

both Google and Microsoft reported I

think it was a 30% and 50% jump in their

carbon emissions. Yeah. Because largely

driven by this data center development.

Yeah. And also the context here is over

the last it was one of the good news

stories of the last sort of 10 to 15

years is that CO2 emissions per capita

in the US has kind of plateaued right

across the west had kind of plateaued

and actually in the UK energy

consumption dropped I mean we stopped

making things but still

you know everything's made in East Asia

now but no but it's it it was kind of a

good story and I kind of bought it right

I thought that you know we'd have we'd

kind of plateaued obviously the global

south would consume more energy But we

are as well. Um,

should we look at these companies as

kind of analogous to the East India

Company of the 19th century? That is the

analogy that I have increasingly started

using, especially with the Trump

administration in power because the

British East India Company very much was

a corporate empire and started off not

very imperial. They just started off as

a company, very small company based in

London. And of course through economic

trade agreements with India gained

significant economic power, political

power and eventually became the apex

predator in that ecosystem and that's

when they started being very imperial in

nature and they were the entire time

abetted by the British Empire the nation

state empire. So you have a corporate

empire, you have a nation state empire

and I literally see that dynamic playing

out now where the US government is also

in its empire era. The Trump

administration has quite literally used

words to suggest that he wants to expand

and fortify the American empire and he

sees these corporate empires like OpenAI

as his empire building assets. And so I

think he is probably seeing it in the

same way that the British crown saw the

British East India Company of let's just

let this company acquire all these

resources, do all these things and then

eventually we'll nationalize the company

and then India formally becomes a colony

of the British Empire. So Trump whatever

the equivalent modern day equivalent

would be of nationalizing these these

companies is his endgame. like he is

helping them strike all these deals and

installing all this American hardware

and software all around the world with

the hope that then those become national

assets and then you know there was

actually just a recent op-ed in

Financial Times from Marate Shake one of

the former EU par parliamentarians who

pointed out like isn't it so convenient

for the US to get all of this American

infrastructure installed everywhere

around the world so that the US

government could literally turn it off

at any time. I mean, if you want to talk

about empire building, there's that. But

at the same time, these corporate

empires are also trying to use the

American empire as an asset to their

empire building ambitions. So there's a

very tenuous alliance between Silicon

Valley and Washington right now in that

each one is trying to use the other and

ultimately trying to dominate the other.

And there's a growing popularity in

Silicon Valley of this idea of a

politics of exit. This idea that

democracy doesn't work anymore. We need

to find other ways of organizing

ourselves in society. And maybe the best

way of organizing ourselves is actually

a series ofworked companies with CEOs at

the top. So I don't ultimately know

who's going to win like the nation state

empire or the corporate empire. But

either version is bad because all of the

people in power now both the business ex

executives and the politicians do not

actually care at all about preserving

democracy. I mean the analogy of India

is really interesting. So I think I

might have my dates wrong. Um East India

Company is running things until 1857.

You have the Indian mutiny, basically an

uprising against the East India Company

and then of course that commercial

endeavor has to be underpinned by the

organized violence of the British

imperial state. Um and it does feel it

does feel like that could be the next

step of what happens with regards to US

interests overseas. I suppose one retort

would be well hold on it sounds kind of

good. I'm a I'm a socialist. I kind of

like the idea of SpaceX being

nationalized. I kind of like the idea

of, you know, the federal government

having a 51% stake in Open AI and Tesla

and Meta. What would you say to that? I

don't necessarily know if my critique is

of the nationalization of the company

more as like why are they nationalizing

these companies and what are they what

you know like the because of this

endgame mentality of let's just let

these companies run rampant around the

world so that ultimately whatever their

assets are become our assets is leading

the Trump administration to have a

completely hands-off approach to AI

regulation they're quite literally they

proposed the big beautiful bill which

passed the House and is now going up to

the Senate with a clause that would, if

implemented, put a 10-year moratorium on

AI regulation at the state level, which

is usually the state level is usually

where regulation, sensible regulation

happens in the US. So they're doing all

of these actions now with wide-ranging

repercussions that will be very

difficult to unwind in the name of this

idea that maybe if they just allow these

companies to act with total impunity

that it will ultimately benefit the

nation state. How do people like Sam

Alman look at the rest of the world

outside the US? These kind of tech

leaders and how do they look at Little

Britain and Italy and how do they look

at us? What do they think about us? You

know, you've you've been inside their minds.

minds.

It's it's Yeah, I mean, they see them as

resources. They see different

territories as different types of

resources, which I mean is what older

empires did. you know, they would look

at a map and just draw out the resources

that they could acquire in each geography.

geography.

We we're going to go here and acquire

the labor. We're going to go here and

acquire the lands. We're going to go

here and acquire the minerals. I mean,

that's literally how they talk. Like

when I was talking with some OpenAI

researchers about their data center

expansion, you know, there was this one

OpenAI employee who said, "We're running

out of land and water." and he was just

saying, "Yeah, we're just like trying to

we're just trying to look at the whole world and see where else we can place

world and see where else we can place these things. Where what other

these things. Where what other geographies can we find all the

geographies can we find all the conditions that we need to build more

conditions that we need to build more data centers? Land without earthquakes,

data centers? Land without earthquakes, without floods, without tornadoes,

without floods, without tornadoes, hurricanes, all these natural disasters

hurricanes, all these natural disasters and can deliver massive amounts of

and can deliver massive amounts of energy to a single point and can cool

energy to a single point and can cool the systems." And they they are they

the systems." And they they are they they're looking at that level of

they're looking at that level of abstraction

abstraction to what are the different pieces of

to what are the different pieces of territory and resources that we need to

territory and resources that we need to acquire and that includes other parts of

acquire and that includes other parts of the west. Yeah. That's not just the

the west. Yeah. That's not just the global south. No, it includes other

global south. No, it includes other parts of the west as well. Yeah. So

parts of the west as well. Yeah. So there have been rapid data center

there have been rapid data center expansion in rural communities in both

expansion in rural communities in both the US and the UK and they it always

the US and the UK and they it always ends up in economically vulnerable

ends up in economically vulnerable communities because those are the

communities because those are the communities that often actually opt in

communities that often actually opt in to the data center development initially

to the data center development initially because they are not informed about what

because they are not informed about what it will ultimately cost them and for how

it will ultimately cost them and for how long. And so I spoke with this one

long. And so I spoke with this one Arizona legislator who said I didn't

Arizona legislator who said I didn't know it had to use fresh water. And for

know it had to use fresh water. And for the UK audience, Arizona is a desert

the UK audience, Arizona is a desert territory. There is no there's there's a

territory. There is no there's there's a very very stringent budget on

very very stringent budget on freshwater. And after that legislator

freshwater. And after that legislator found out, she was like, I would have

found out, she was like, I would have never voted for having this data center

never voted for having this data center in. But the problem is that there are so

in. But the problem is that there are so few independent experts for these

few independent experts for these legislators, city council members to

legislators, city council members to consult that the only people that they

consult that the only people that they rely on for the information about what

rely on for the information about what the impact of this is going to be are

the impact of this is going to be are the companies. And all the companies

the companies. And all the companies ever say is we're going to invest

ever say is we're going to invest millions of dollars.

millions of dollars. We're going to create a bunch of

We're going to create a bunch of construction jobs up front and it's

construction jobs up front and it's going to be great for your economy.

going to be great for your economy. Yeah. I mean, that's all we hear about

Yeah. I mean, that's all we hear about data sense in this country. And it's a

data sense in this country. And it's a great it's a great top line for the

great it's a great top line for the chancellor and the prime minister

chancellor and the prime minister because they can say tens of billions of

because they can say tens of billions of pounds worth of investment. Okay. But in

pounds worth of investment. Okay. But in terms of long-term jobs, how many? And

terms of long-term jobs, how many? And also, by the way, for that rural

also, by the way, for that rural community in God knows where, you know,

community in God knows where, you know, the northeast of England or whatever.

the northeast of England or whatever. Yeah. You're not telling them that

Yeah. You're not telling them that actually they can't use their hose pipes

actually they can't use their hose pipes for 3 months a year because all the

for 3 months a year because all the water is going to that local data

water is going to that local data center. Exactly. And

center. Exactly. And it's quite extraordinary. and and and

it's quite extraordinary. and and and the most scary thing about all of it is

the most scary thing about all of it is in the UK at least the politicians don't

in the UK at least the politicians don't know any of that. I sincerely don't

know any of that. I sincerely don't think the chancellor knows any of that.

think the chancellor knows any of that. Uh and there's no real I mean even if

Uh and there's no real I mean even if you use the prism of colonialism,

you use the prism of colonialism, imperialism with regards to exploitative

imperialism with regards to exploitative economic relations between the United

economic relations between the United States and other parts of the world,

States and other parts of the world, they think you're a troskist, right?

they think you're a troskist, right? That's that's the crazy things. They

That's that's the crazy things. They can't even look after their own people

can't even look after their own people because if looking after your own people

because if looking after your own people boils down to being too leftwing well I

boils down to being too leftwing well I think part of it is also that they don't

think part of it is also that they don't really realize that it's literally

really realize that it's literally happening in the UK. So the so to

happening in the UK. So the so to connect it to the UK, data center

connect it to the UK, data center development along the M4 corridor is has

development along the M4 corridor is has literally already led to a ban in

literally already led to a ban in construction of new housing in certain

construction of new housing in certain communities that desperately need more

communities that desperately need more affordable housing. And it's because you

affordable housing. And it's because you cannot build new housing when you cannot

cannot build new housing when you cannot guarantee deliveries of fresh water or

guarantee deliveries of fresh water or electricity to that housing. And it was

electricity to that housing. And it was due to the massive electricity

due to the massive electricity consumption of the data centers being

consumption of the data centers being built in that corridor that led to that

built in that corridor that led to that ban. That's nuts. I mean, that's the

ban. That's nuts. I mean, that's the most valuable real estate for housing in

most valuable real estate for housing in in the country, the M4. Yeah. And do you

in the country, the M4. Yeah. And do you think UK politicians are are aware of

think UK politicians are are aware of that contradiction or is that just

that contradiction or is that just that's I mean, you know, I don't know if

that's I mean, you know, I don't know if they are aware if maybe they're they

they are aware if maybe they're they don't have awareness or maybe they are

don't have awareness or maybe they are aware and they're also thinking of other

aware and they're also thinking of other tradeoffs. I mean now in the UK and and

tradeoffs. I mean now in the UK and and in the EU at large there's just this

in the EU at large there's just this huge conversation around data

huge conversation around data sovereignty and of course technology

sovereignty and of course technology sovereignty there's this whole concept

sovereignty there's this whole concept of developing the EU stack and why is it

of developing the EU stack and why is it that we don't have any of our tech

that we don't have any of our tech giants why don't we have any of this

giants why don't we have any of this infrastructure um and like here Starmer

infrastructure um and like here Starmer just said this week during London tech

just said this week during London tech week we want to be AI creators not AI

week we want to be AI creators not AI consumers so I think in their minds

consumers so I think in their minds maybe this is a viable trade-off. We we

maybe this is a viable trade-off. We we skimp a little bit on housing for the

skimp a little bit on housing for the ability to have more indigenous

ability to have more indigenous innovation. But I think the thing that

innovation. But I think the thing that is often left out of that conversation

is often left out of that conversation is this is a false trade-off. People

is this is a false trade-off. People think that you need colossal data

think that you need colossal data centers to build AI systems. You

centers to build AI systems. You actually do not. This is specifically

actually do not. This is specifically the approach that OpenAI decided to

the approach that OpenAI decided to take. But actually before open AI

take. But actually before open AI started building large language models

started building large language models and generative AI systems at these these

and generative AI systems at these these colossal scales, the trend within the AI

colossal scales, the trend within the AI research community was going the

research community was going the opposite direction towards tiny AI

opposite direction towards tiny AI systems. And there was all this really

systems. And there was all this really interesting research looking into how

interesting research looking into how small your data sets could be to create

small your data sets could be to create powerful AI models and how little

powerful AI models and how little computational resources you needed to

computational resources you needed to create powerful AI models. So there were

create powerful AI models. So there were there were interesting papers that I

there were interesting papers that I wrote about where you could have a

wrote about where you could have a couple hundred images to create highly

couple hundred images to create highly performant AI systems or uh you could

performant AI systems or uh you could have AI systems trained on your mobile

have AI systems trained on your mobile device. That's like a sing not even a

device. That's like a sing not even a one computer chip on running on your

one computer chip on running on your mobile device. And OpenAI took an

mobile device. And OpenAI took an approach that is now using hundreds of

approach that is now using hundreds of thousands of computer chips to train a

thousands of computer chips to train a single system. And those hundreds of

single system. And those hundreds of thousands of computer chips now are

thousands of computer chips now are consuming, you know, city city loads of

consuming, you know, city city loads of energy. And so if we divorced the

energy. And so if we divorced the concept of AI progress with this scaling

concept of AI progress with this scaling paradigm, you would realize then you can

paradigm, you would realize then you can have housing and you can have AI

have housing and you can have AI innovation.

innovation. But once again, there's not a lot of

But once again, there's not a lot of independent experts that are actually

independent experts that are actually saying these things. Most AI experts

saying these things. Most AI experts today are employed by these companies.

today are employed by these companies. And this is basically the equivalent of

And this is basically the equivalent of if most climate scientists were being

if most climate scientists were being bankrolled by oil and gas companies.

bankrolled by oil and gas companies. Like they would tell you things that are

Like they would tell you things that are not in any sense of the word

not in any sense of the word scientifically grounded, but just good

scientifically grounded, but just good for the company. I interviewed a great

for the company. I interviewed a great guy um twice actually now, a guy called

guy um twice actually now, a guy called Angus Hansen who's really just on it

Angus Hansen who's really just on it with regards to the exploitive nature of

with regards to the exploitive nature of the increasingly exploitive nature of um

the increasingly exploitive nature of um uh of the United States um economic

uh of the United States um economic relations with the UK. Just fascinating.

relations with the UK. Just fascinating. fascinating uh a book and man and I just

fascinating uh a book and man and I just don't think it's cut through to our

don't think it's cut through to our politicians here how bad it's getting

politicians here how bad it's getting and you're saying about AI consumers or

and you're saying about AI consumers or or or creators I mean ultimately you're

or or creators I mean ultimately you're talking about meta you're talking about

talking about meta you're talking about alphabet you're talking about XAI you're

alphabet you're talking about XAI you're talking about open AI we are consumers

talking about open AI we are consumers we are dependent it's a colonial

we are dependent it's a colonial exploitative relationship with regards

exploitative relationship with regards to big tech has been for a really long

to big tech has been for a really long time our smartest people which the

time our smartest people which the taxpayer trains here go to the US I

taxpayer trains here go to the US I think one of the top people at Slack as

think one of the top people at Slack as a UK national demis you know um uh deep

a UK national demis you know um uh deep mind now working under the you know the

mind now working under the you know the sort of the the umbrella of alphabet and

sort of the the umbrella of alphabet and yeah it just doesn't make it just

yeah it just doesn't make it just doesn't make sense for me with regards

doesn't make sense for me with regards to that formulation they they simply

to that formulation they they simply don't get it you know every I came here

don't get it you know every I came here using my Mastercard

using my Mastercard millions of Brits use Apple Pay and

millions of Brits use Apple Pay and Google Pay and Mastercard and Visa and

Google Pay and Mastercard and Visa and every time we do 0.1 0.2% 2% crosses the

every time we do 0.1 0.2% 2% crosses the Atlantic and and it's it just goes over

Atlantic and and it's it just goes over the heads of um our political class

the heads of um our political class which is is very unnerving in regards to

which is is very unnerving in regards to the efficiency of these um smaller

the efficiency of these um smaller systems. Where does where does deepseek

systems. Where does where does deepseek fit in all of this? Because of course

fit in all of this? Because of course the scaling laws at the heart of open AI

the scaling laws at the heart of open AI which is you get to AGI by more compute,

which is you get to AGI by more compute, more parameters, more data is kind of

more parameters, more data is kind of untethered a bit by the arrival of

untethered a bit by the arrival of deepseek. Yes. What? Deepseek is such an

deepseek. Yes. What? Deepseek is such an interesting and complicated case because

interesting and complicated case because they basically they it was it's a

they basically they it was it's a Chinese AI model that was created by

Chinese AI model that was created by this company Highf flyier and they were

this company Highf flyier and they were able to create a model that essentially

able to create a model that essentially matched and even exceeded some

matched and even exceeded some performance metrics of American models

performance metrics of American models being developed by OpenAI and Anthropic

being developed by OpenAI and Anthropic with orders of magnitude, less

with orders of magnitude, less computational resources, less money.

computational resources, less money. That said, it's not necessarily the

That said, it's not necessarily the perfect. It's like I I don't think the

perfect. It's like I I don't think the world should suddenly start using Deep

world should suddenly start using Deep Seek and saying DeepS solves all these

Seek and saying DeepS solves all these problems because it's still engaged in a

problems because it's still engaged in a lot of data privacy problems, copyright

lot of data privacy problems, copyright exploitation, things like that. Um, and

exploitation, things like that. Um, and some people argue that ultimately they

some people argue that ultimately they were distilling the models from that

were distilling the models from that that were first developed through the

that were first developed through the scaling paradigm. So you first develop

scaling paradigm. So you first develop some of these colossal scaling models

some of these colossal scaling models and then you end up making them smaller

and then you end up making them smaller and more efficient. So some people argue

and more efficient. So some people argue that you actually have to first do that

that you actually have to first do that scaling before you get the efficiency.

scaling before you get the efficiency. But anyway, what it did show is you can

But anyway, what it did show is you can get these capabilities with

get these capabilities with significantly less compute. And it also

significantly less compute. And it also showed a complete unwillingness of

showed a complete unwillingness of American companies now that they know

American companies now that they know that they can use these techniques to

that they can use these techniques to make their models more efficient.

make their models more efficient. They're still not really doing it. Why

They're still not really doing it. Why do they do they like giving their money

do they do they like giving their money to Nvidia? What's the

to Nvidia? What's the Because when you

Because when you if you continue to pursue a scaling

if you continue to pursue a scaling approach and you're the only one with

approach and you're the only one with all of the AI experts in the world, you

all of the AI experts in the world, you persuade people into believing this is

persuade people into believing this is the only path and therefore you continue

the only path and therefore you continue to monopolize this technology because it

to monopolize this technology because it locks out anyone else from playing that

locks out anyone else from playing that game. And also because path dependence

game. And also because path dependence like these companies are actually not

like these companies are actually not that nimble, they end up the way that

that nimble, they end up the way that they they organize themselves. It it's

they they organize themselves. It it's not so easy for them to just like

not so easy for them to just like immediately swap to a different

immediately swap to a different approach. They end up putting in motion

approach. They end up putting in motion all the resources, all of the training

all the resources, all of the training runs, so on and so forth o o over the

runs, so on and so forth o o over the course of months, and then they just

course of months, and then they just have to run with it. So Deep Seek

have to run with it. So Deep Seek actually wasn't the first time that this

actually wasn't the first time that this happened. The first time that this

happened. The first time that this happened was with image generators and

happened was with image generators and stable diffusion. And stable diffusion

stable diffusion. And stable diffusion was specifically developed by an

was specifically developed by an academic in Europe who was really pissed

academic in Europe who was really pissed that the AI companies like OpenAI were

that the AI companies like OpenAI were taking a scaling approach to image

taking a scaling approach to image generation. He was like, "This is

generation. He was like, "This is literally wholly unnecessary." And

literally wholly unnecessary." And they're spending thousands of chips, all

they're spending thousands of chips, all of this energy to produce dolly. And

of this energy to produce dolly. And ultimately, he ended up producing stable

ultimately, he ended up producing stable diffusion with a couple hundred chips

diffusion with a couple hundred chips with using a new technique called latent

with using a new technique called latent diffusion, hence the name stable

diffusion, hence the name stable diffusion. And you know, arguably it was

diffusion. And you know, arguably it was actually an even better model than Dolly

actually an even better model than Dolly because users were saying that stable

because users were saying that stable diffusion had even better image quality,

diffusion had even better image quality, better image generation, better ability

better image generation, better ability to actually control the images than

to actually control the images than Dolly. But even knowing that latent

Dolly. But even knowing that latent diffusion existed, OpenAI continued to

diffusion existed, OpenAI continued to develop Dolly with these massive scaling

develop Dolly with these massive scaling approaches. And it wasn't until later

approaches. And it wasn't until later that they then adopted the cheaper

that they then adopted the cheaper version. But it was it was just

version. But it was it was just significantly delayed. And and I was

significantly delayed. And and I was asking open air research like why that

asking open air research like why that doesn't make any sense. why did you do

doesn't make any sense. why did you do that? And they were like, well, once you

that? And they were like, well, once you set off on a path, it's kind of hard to

set off on a path, it's kind of hard to pivot. Also, Jensen Huang, the the CEO

pivot. Also, Jensen Huang, the the CEO of Nvidia is really charismatic, right?

of Nvidia is really charismatic, right? I mean, it's quite funny when cuz I'm

I mean, it's quite funny when cuz I'm I'm a Marxist. Just I'm going to make

I'm a Marxist. Just I'm going to make that confession. You have these big sort

that confession. You have these big sort of structural um understandings of how

of structural um understandings of how of how history happens and then you sort

of how history happens and then you sort of realize actually this guy's really

of realize actually this guy's really charismatic and this person's really

charismatic and this person's really manipulative and all of a sudden the

manipulative and all of a sudden the world's hyperpower is, you know, making

world's hyperpower is, you know, making these technological decisions. Okay. Uh

these technological decisions. Okay. Uh quite strange. Um we talked about data

quite strange. Um we talked about data centers. We talked about earth um water

centers. We talked about earth um water energy. I want to talk also about some

energy. I want to talk also about some of the more exploitative practices with

of the more exploitative practices with regards to workers in the global south.

regards to workers in the global south. You use one really grueling example

You use one really grueling example actually in Kenya. Can you talk about

actually in Kenya. Can you talk about some of the research around that? Some

some of the research around that? Some of the people you met. Yeah. So I ended

of the people you met. Yeah. So I ended up interviewing workers in Kenya who

up interviewing workers in Kenya who were contracted by OpenAI to build a

were contracted by OpenAI to build a content moderation filter for the

content moderation filter for the company. And at that point in the

company. And at that point in the company's history, it was starting to

company's history, it was starting to think about commercialization after

think about commercialization after coming from its nonprofit fundamental AI

coming from its nonprofit fundamental AI research roots. And they realized if

research roots. And they realized if we're going to put a text generation

we're going to put a text generation model in the hands of millions of users,

model in the hands of millions of users, it is going to be a PR crisis if it

it is going to be a PR crisis if it starts spewing racist, toxic, hateful

starts spewing racist, toxic, hateful speech. In fact, in 2016, Microsoft

speech. In fact, in 2016, Microsoft infamously did exactly this. They

infamously did exactly this. They developed a chatbot named Tay. They put

developed a chatbot named Tay. They put it online without any content moderation

it online without any content moderation and then within hours it started saying

and then within hours it started saying awful things and then they had to take

awful things and then they had to take it offline and to this day as evidenced

it offline and to this day as evidenced by me bringing it up it's still brought

by me bringing it up it's still brought up as a horrible case study in corporate

up as a horrible case study in corporate mismanagement and so open I thought we

mismanagement and so open I thought we don't want to do that we're going to

don't want to do that we're going to create a filter that wraps around our

create a filter that wraps around our models so that even if the models start

models so that even if the models start generating this stuff it never reaches

generating this stuff it never reaches the user because the filter then blocks

the user because the filter then blocks it. In order to build that filter, what

it. In order to build that filter, what the Kenyon workers had to do was wade

the Kenyon workers had to do was wade through reams of the worst text on the

through reams of the worst text on the internet as well as AI generated text on

internet as well as AI generated text on the internet where OpenAI was prompting

the internet where OpenAI was prompting its models to imagine the worst text on

its models to imagine the worst text on the internet. And the workers then had

the internet. And the workers then had to go through all of this and put into a

to go through all of this and put into a detailed taxonomy, is this hate speech,

detailed taxonomy, is this hate speech, is this harassment, is this violent

is this harassment, is this violent content, is this sexual content? and the

content, is this sexual content? and the degree of hate speech of violence of

degree of hate speech of violence of sexual content. So it was they were

sexual content. So it was they were asking workers to say does it involve

asking workers to say does it involve sexual abuse? Does it involve sexual

sexual abuse? Does it involve sexual abuse of children? So on and so forth.

abuse of children? So on and so forth. And to this day I believe if you look at

And to this day I believe if you look at OpenAI's content moderation filter

OpenAI's content moderation filter documentation, it actually lists all of

documentation, it actually lists all of those categories. And this is one of the

those categories. And this is one of the things that it offers to clients of

things that it offers to clients of their models, business clients of their

their models, business clients of their models that you can toggle on and off

models that you can toggle on and off each of these filters. So that's why

each of these filters. So that's why they had to put this into that taxonomy.

they had to put this into that taxonomy. The workers ended up suffering very many

The workers ended up suffering very many of the same symptoms of content

of the same symptoms of content moderators of the social media era.

moderators of the social media era. Absolutely traumatized by the work

Absolutely traumatized by the work completely changed their personalities

completely changed their personalities left them with PTSD. And I highlight the

left them with PTSD. And I highlight the story of this man Moin who is one of the

story of this man Moin who is one of the workers that I interviewed who showed to

workers that I interviewed who showed to me that it's not just individuals that

me that it's not just individuals that break down. it's their families and

break down. it's their families and communities because there are people who

communities because there are people who rely on these individuals. And so Mofat

rely on these individuals. And so Mofat was on the sexual content team. His

was on the sexual content team. His personality totally changed as he was

personality totally changed as he was reading child sexual abuse every day.

reading child sexual abuse every day. And when he came home, he stopped

And when he came home, he stopped playing with his stepdaughter. He

playing with his stepdaughter. He stopped being intimate with his wife.

stopped being intimate with his wife. And he also couldn't explain to them why

And he also couldn't explain to them why he was changing because he didn't know

he was changing because he didn't know how to say to them, "I read sex content

how to say to them, "I read sex content all day. That doesn't sound like a real

all day. That doesn't sound like a real job. That sounds like a very shameful

job. That sounds like a very shameful job. Chad GBT hadn't come out yet. So

job. Chad GBT hadn't come out yet. So there was no conception of what does

there was no conception of what does that even mean? And so one day his wife

that even mean? And so one day his wife asks him for fish for dinner. He goes

asks him for fish for dinner. He goes out, buys three fish, one for him, one

out, buys three fish, one for him, one for her, one for the stepdaughter. And

for her, one for the stepdaughter. And by the time he comes home, all of their

by the time he comes home, all of their bags are packed and they're completely

bags are packed and they're completely gone. And she texts him, "I don't know

gone. And she texts him, "I don't know the man you've become anymore, and I'm

the man you've become anymore, and I'm never coming back." You say that's the

never coming back." You say that's the case with regards to text. Are people

case with regards to text. Are people also having to engage with images as

also having to engage with images as well? I mean, that was more of a social

well? I mean, that was more of a social media thing. Is that here too? Yeah,

media thing. Is that here too? Yeah, they there were workers that they then

they there were workers that they then so after this they contracted these

so after this they contracted these Kenyon workers that contract actually

Kenyon workers that contract actually was cancelled because there was a bunch

was cancelled because there was a bunch of scrutiny on that company and there

of scrutiny on that company and there the the third party company that they

the the third party company that they were contracting the workers through and

were contracting the workers through and huge scandal um this is Sama right sama

huge scandal um this is Sama right sama yeah and there was a huge scandal around

yeah and there was a huge scandal around Sama and then open ended up shifting to

Sama and then open ended up shifting to other contractors who were then involved

other contractors who were then involved in moderating images

in moderating images And were they remunerated for the kind

And were they remunerated for the kind of work they were doing quite well or

of work they were doing quite well or for the Kenyan workers they were paid a

for the Kenyan workers they were paid a few dollars an hour, right? Yeah. And

few dollars an hour, right? Yeah. And then on the other side of the of the

then on the other side of the of the Atlantic, you talk about people in South

Atlantic, you talk about people in South America um doing effectively, you know,

America um doing effectively, you know, mechanical turk peace work for these

mechanical turk peace work for these companies as well. Can you talk about

companies as well. Can you talk about that a little bit? Yeah, so generative

that a little bit? Yeah, so generative AI is not the only thing that leads to

AI is not the only thing that leads to data annotation. This has actually been

data annotation. This has actually been part of the AI industry for a very long

part of the AI industry for a very long time. And so I ended up years ago

time. And so I ended up years ago interviewing this woman in Colombia who

interviewing this woman in Colombia who was a Venezuelan refugee about the

was a Venezuelan refugee about the specific thing that happened to her

specific thing that happened to her country

country in the global AI supply chain. So when

in the global AI supply chain. So when in 2016

in 2016 when when the AI industry first started

when when the AI industry first started actually looking into the development of

actually looking into the development of self-driving cars, there was a surge in

self-driving cars, there was a surge in demand for highly educated workers to do

demand for highly educated workers to do data annotation labeling for helping

data annotation labeling for helping self-driving cars navigate the road. You

self-driving cars navigate the road. You have to show self-driving cars, this is

have to show self-driving cars, this is a car, this is a tree, this is a bike,

a car, this is a tree, this is a bike, this is a pedestrian. This is how you

this is a pedestrian. This is how you avoid all of them. These are the lane

avoid all of them. These are the lane markings. This is what the lane markings

markings. This is what the lane markings mean. And they're humans that do that.

mean. And they're humans that do that. And it just so happened in 2016 when

And it just so happened in 2016 when this demand was rising that Venezuela as

this demand was rising that Venezuela as a country was was dealing with the worst

a country was was dealing with the worst peacetime economic crisis in 50 years.

peacetime economic crisis in 50 years. So the economy bottomed out. A huge

So the economy bottomed out. A huge population of highly educated workers

population of highly educated workers with great access to internet suddenly

with great access to internet suddenly were desperate to work at any price. And

were desperate to work at any price. And these became the three conditions that I

these became the three conditions that I call the crisis playbook in my book that

call the crisis playbook in my book that companies started using to then scout

companies started using to then scout out more workers that were extremely

out more workers that were extremely cheap for working for the AI industry.

cheap for working for the AI industry. And so the woman that I met in Colia,

And so the woman that I met in Colia, she was not just it she was working in a

she was not just it she was working in a level of exploitation that was not based

level of exploitation that was not based on the content that she was looking at.

on the content that she was looking at. She was labeling self-driving cars and

She was labeling self-driving cars and labeling, you know, retail platforms and

labeling, you know, retail platforms and things like that. The exploitation was

things like that. The exploitation was structural to her job in that she was

structural to her job in that she was logging into a platform every day and

logging into a platform every day and looking at a queue that automatically

looking at a queue that automatically populated with tasks that were being

populated with tasks that were being sent to her from Global North companies

sent to her from Global North companies and most of the time the tasks didn't

and most of the time the tasks didn't appear and when they did she had to

appear and when they did she had to compete with other workers to claim the

compete with other workers to claim the task first in order to do it at all. And

task first in order to do it at all. And because there were so many Venezuelans

because there were so many Venezuelans in crisis and so many of them were

in crisis and so many of them were finding out about data annotation

finding out about data annotation platforms in the end there were more and

platforms in the end there were more and more and more workers competing for

more and more workers competing for smaller and smaller volumes of tasks.

smaller and smaller volumes of tasks. And so these tasks would come online and

And so these tasks would come online and then disappear within seconds. And so

then disappear within seconds. And so one day she was out on a walk when a

one day she was out on a walk when a task appeared in her queue and she

task appeared in her queue and she sprinted to her apartment to try and

sprinted to her apartment to try and claim the task before it went away. But

claim the task before it went away. But by the time she got back it was too

by the time she got back it was too late. And after that she was like I

late. And after that she was like I never went on a walk during the weekday

never went on a walk during the weekday again. And on the weekends which she

again. And on the weekends which she discovered is less often less likely for

discovered is less often less likely for companies to post tasks. She would only

companies to post tasks. She would only allow herself a 30 minute walk break

allow herself a 30 minute walk break because she was too afraid of that

because she was too afraid of that happening again. And did she did she

happening again. And did she did she detail about how that gave her sort of

detail about how that gave her sort of anxiety or insomnia or mental health

anxiety or insomnia or mental health kind of overheads? It's it it

kind of overheads? It's it it that sounds insane sounds insane way to

that sounds insane sounds insane way to live. um

live. um it completely controlled her life. She

it completely controlled her life. She didn't tell me about whether or not it

didn't tell me about whether or not it gave her insomnia, but it completely

gave her insomnia, but it completely controlled the rhythms of her life in

controlled the rhythms of her life in that she had this plugin that she

that she had this plugin that she downloaded that would sound an alarm

downloaded that would sound an alarm every time a task appeared so that she

every time a task appeared so that she could, you know, cook or clean or

could, you know, cook or clean or whatever without literally just looking

whatever without literally just looking at the laptop the whole day. and she

at the laptop the whole day. and she would turn it on to max volume in the

would turn it on to max volume in the middle of the night because sometimes

middle of the night because sometimes task would arrive in the middle of the

task would arrive in the middle of the night and if the alarm rang she would

night and if the alarm rang she would wake up, sprint to her computer, claim

wake up, sprint to her computer, claim the task and then start tasking at like

the task and then start tasking at like 3:00 a.m. in the morning. Um, and she

3:00 a.m. in the morning. Um, and she had chronic illness. Um, one of the

had chronic illness. Um, one of the reasons why she was tethered to her

reasons why she was tethered to her apartment doing this online work in the

apartment doing this online work in the first place was not just because she was

first place was not just because she was a refugee, but also because she had

a refugee, but also because she had severe diabetes. And it got to the point

severe diabetes. And it got to the point where she ended up in the hospital and

where she ended up in the hospital and was completely blind for a period of

was completely blind for a period of time. And the doctor said that if you

time. And the doctor said that if you had not come to the hospital when you

had not come to the hospital when you did, you would have died. And so she was

did, you would have died. And so she was tethered to her home because she had to

tethered to her home because she had to inject herself with insulin like five

inject herself with insulin like five times a day. And it was this really

times a day. And it was this really complicated regime that didn't allow her

complicated regime that didn't allow her to commute to a regular office, have a

to commute to a regular office, have a regular job. So she was doing all this

regular job. So she was doing all this extremely disruptive, disregulating

extremely disruptive, disregulating work on top of just trying to manage

work on top of just trying to manage extreme severe diabetes. I mean, it's

extreme severe diabetes. I mean, it's extraordinary you've managed to un

extraordinary you've managed to un unveil those stories. I think I mean

unveil those stories. I think I mean that's why the book is so interesting,

that's why the book is so interesting, fascinating for me. That's why it's got

fascinating for me. That's why it's got the plates it's got is that you're, you

the plates it's got is that you're, you know, you're speaking to people who are

know, you're speaking to people who are on first name terms as Sam Alman, then

on first name terms as Sam Alman, then you're talking to Venezuelan refugees in

you're talking to Venezuelan refugees in Colombia. Um, and it's really important

Colombia. Um, and it's really important to say that this work is being done for

to say that this work is being done for multi-trillion dollar companies. Yes.

multi-trillion dollar companies. Yes. That's the other side of it, right?

That's the other side of it, right? You're seeing Elon Musk worth 300

You're seeing Elon Musk worth 300 billion plus dollars and then there are

billion plus dollars and then there are people that's where the value is being

people that's where the value is being generated. Yeah. Exactly. And that's

generated. Yeah. Exactly. And that's when the reason why I really wanted to

when the reason why I really wanted to highlight those stories is because

highlight those stories is because that's where you really see the logic of

that's where you really see the logic of Empire. There is no moral justification

Empire. There is no moral justification for why those workers whose contribution

for why those workers whose contribution is critical to the functioning of these

is critical to the functioning of these technologies and critical to the

technologies and critical to the popularity of products like chat GBT are

popularity of products like chat GBT are paid pennies when the people working

paid pennies when the people working within the companies can easily get

within the companies can easily get million-doll compensation packages. The

million-doll compensation packages. The only justification is an ideological one

only justification is an ideological one which is that there are some people born

which is that there are some people born into this world superior and others who

into this world superior and others who are inferior and the superior people

are inferior and the superior people have a right to subjugate the inferior

have a right to subjugate the inferior ones. My last question what does the US

ones. My last question what does the US public do about big tech if it wants to

public do about big tech if it wants to take on some of these issues income

take on some of these issues income inequality regional inequality global

inequality regional inequality global imperial overreach etc. A few proposals

imperial overreach etc. A few proposals and which you know somebody can execute

and which you know somebody can execute on. What would you suggest? Yeah, I

on. What would you suggest? Yeah, I wouldn't even say it's just the US

wouldn't even say it's just the US public. I mean, anyone in the world can

public. I mean, anyone in the world can do something about it. And one of the

do something about it. And one of the remarkable things for me in reporting

remarkable things for me in reporting stories is people who felt like they had

stories is people who felt like they had the least amount of agency in the world

the least amount of agency in the world were actually the ones that put up the

were actually the ones that put up the most aggressive fights and actually

most aggressive fights and actually started gaining ground on these

started gaining ground on these companies in take taking resources from

companies in take taking resources from them. So, I talk about Chilean water

them. So, I talk about Chilean water activists who pushed back against a

activists who pushed back against a Google data center project for so long

Google data center project for so long that they've stalled that project. now

that they've stalled that project. now for 5 years and they forced Google to

for 5 years and they forced Google to come to the table and the Chilean

come to the table and the Chilean government to come to the table and now

government to come to the table and now these these residents are invited to

these these residents are invited to comment every time there's a data center

comment every time there's a data center development proposal which they then

development proposal which they then said is not it's not the end of the

said is not it's not the end of the fight like they still have to be

fight like they still have to be vigilant and at any moment if they blink

vigilant and at any moment if they blink something could happen but but anyone in

something could happen but but anyone in the world I think has an active role to

the world I think has an active role to play in shaping the AI development

play in shaping the AI development trajectory and the way that I Think

trajectory and the way that I Think about it as the full supply chain of AI

about it as the full supply chain of AI development. You have a bunch of

development. You have a bunch of resources that these companies need to

resources that these companies need to develop their technologies, data, land,

develop their technologies, data, land, energy, water, and then you have a bunch

energy, water, and then you have a bunch of spaces that these companies need

of spaces that these companies need access to to deploy their technologies.

access to to deploy their technologies. Schools, hospitals, offices, government

Schools, hospitals, offices, government agencies. All these resources in all

agencies. All these resources in all these spaces are actually places of

these spaces are actually places of democratic contestation. They're

democratic contestation. They're collectively owned. They're publicly

collectively owned. They're publicly owned. So, we're already seeing artists

owned. So, we're already seeing artists and writers that are suing these

and writers that are suing these companies saying, "No, you cannot take

companies saying, "No, you cannot take our intellectual property." And that is

our intellectual property." And that is them reclaiming ownership over a

them reclaiming ownership over a critical resource that these companies

critical resource that these companies need. We're seeing people start

need. We're seeing people start exercising their data privacy rights. I

exercising their data privacy rights. I mean, one of my favorite things about

mean, one of my favorite things about visiting the UK and EU as an American

visiting the UK and EU as an American that has no federal data privacy law to

that has no federal data privacy law to protect me is to reject those cookies

protect me is to reject those cookies every single web page that I encounter.

every single web page that I encounter. That is me reclaiming ownership over my

That is me reclaiming ownership over my data and not allowing those companies to

data and not allowing those companies to then feed that into their models. We're

then feed that into their models. We're seeing just like the Chilean water

seeing just like the Chilean water activists, hundreds of communities now

activists, hundreds of communities now rising up and pushing back against data

rising up and pushing back against data center development. We're seeing

center development. We're seeing teachers and students escalate the a

teachers and students escalate the a public debate around do we actually want

public debate around do we actually want AI in our schools and if so under what

AI in our schools and if so under what terms. And many schools are now setting

terms. And many schools are now setting up governance committees to to to

up governance committees to to to determine what their AI policy is so

determine what their AI policy is so that ultimately AI can facilitate more

that ultimately AI can facilitate more curiosity and more critical thinking

curiosity and more critical thinking instead of just eroding it all away. The

instead of just eroding it all away. The same thing I'm sure wherever your

same thing I'm sure wherever your audience is sitting right now. If they

audience is sitting right now. If they work for a company, that company is for

work for a company, that company is for sure discussing their AI policy. Put

sure discussing their AI policy. Put yourself on that committee for drafting

yourself on that committee for drafting that policy. Make sure that all the

that policy. Make sure that all the stakeholders in that office are at that

stakeholders in that office are at that table actively discussing when and under

table actively discussing when and under what conditions you would accept AI and

what conditions you would accept AI and from which vendors as well because again

from which vendors as well because again not all AI models are created equal. So

not all AI models are created equal. So do your research on which AI

do your research on which AI technologies you want to use and which

technologies you want to use and which companies are providing them. And I

companies are providing them. And I think if we everyone can actually

think if we everyone can actually actively play a role in every single

actively play a role in every single part of the supply chain that they

part of the supply chain that they interface with, which is quite a lot.

interface with, which is quite a lot. Most people interface with the data

Most people interface with the data part. Many people will now have data

part. Many people will now have data center data centers popping up in a

center data centers popping up in a community near them. Everyone goes to

community near them. Everyone goes to school at some point. Everyone works in

school at some point. Everyone works in some kind of office or community at some

some kind of office or community at some point. If we do all of this push back a

point. If we do all of this push back a 100,000 times fold and democratically

100,000 times fold and democratically contest every stage of this AI

contest every stage of this AI development and deployment pipeline, I

development and deployment pipeline, I am very optimistic that we will reverse

am very optimistic that we will reverse the imperial conquest of these companies

the imperial conquest of these companies and move towards a much more broadly

and move towards a much more broadly beneficial trajectory for AI

beneficial trajectory for AI development. Yeah, we've had we've had

development. Yeah, we've had we've had big tech social media for the last 15 20

big tech social media for the last 15 20 years and I suppose the question is is

years and I suppose the question is is the same set of patterns going to apply

the same set of patterns going to apply to this stuff and I I I think when you

to this stuff and I I I think when you speak to someone like Jonathan hate when

speak to someone like Jonathan hate when he talks about um young people and their

he talks about um young people and their consumption now of social media and

consumption now of social media and mobile telephones etc. his real worry is

mobile telephones etc. his real worry is AI. Yeah. And if there is this lass

AI. Yeah. And if there is this lass attitude from policy makers and also

attitude from policy makers and also let's be honest from civil service civil

let's be honest from civil service civil society that there was over the last 15

society that there was over the last 15 20 years I mean he's terrified about the

20 years I mean he's terrified about the implications so it's interesting to see

implications so it's interesting to see that there's congruence between what

that there's congruence between what you're saying what Jonathan hates saying

you're saying what Jonathan hates saying can I ask you one more question have you

can I ask you one more question have you ever read Dune by Frank Herbert I've

ever read Dune by Frank Herbert I've watched the movie and it's sitting on my

watched the movie and it's sitting on my bedside table to actually read the

bedside table to actually read the original and I'm so glad that you asked

original and I'm so glad that you asked me this because this is an analogy that

me this because this is an analogy that I use all the time now to describe the

I use all the time now to describe the AI world. Yeah. But Larry and Jihad.

AI world. Yeah. But Larry and Jihad. So yeah. So one of the things that was

So yeah. So one of the things that was so shocking to me because we already

so shocking to me because we already talked about this like quasi religious

talked about this like quasi religious fervor within the AI community and I was

fervor within the AI community and I was interviewing people who one of the

interviewing people who one of the people that their voice was quivering

people that their voice was quivering when they were telling me about the

when they were telling me about the profound cataclysmic changes on the

profound cataclysmic changes on the horizon. Like these are very visceral

horizon. Like these are very visceral reactions. These are true believers. And

reactions. These are true believers. And Dune strikes me as a really good analogy

Dune strikes me as a really good analogy for understanding this ecosystem

for understanding this ecosystem because Paul Trades mom in the story,

because Paul Trades mom in the story, she creates this myth to help position

she creates this myth to help position Paul as a supreme leader and to

Paul as a supreme leader and to ultimately control the population. And

ultimately control the population. And the people who encounter this myth, they

the people who encounter this myth, they don't know that it's a creation. So,

don't know that it's a creation. So, they're just true believers. And at some

they're just true believers. And at some point, Paul gets so wrapped up in this

point, Paul gets so wrapped up in this own mythology that he starts to forget

own mythology that he starts to forget that it was originally a creation. And

that it was originally a creation. And this is essentially what I felt like I

this is essentially what I felt like I was I was seeing with my interviews of

was I was seeing with my interviews of people in the AI world because because I

people in the AI world because because I w I had the opportunity to start

w I had the opportunity to start interviewing people starting all the way

interviewing people starting all the way back in 2019. You know, I interviewed

back in 2019. You know, I interviewed some people who for back then and for

some people who for back then and for the book to just map out their their

the book to just map out their their trajectory

trajectory and there were non-believers back then

and there were non-believers back then that are true believers now. Like if

that are true believers now. Like if they were able to stay long enough at

they were able to stay long enough at that company, they all in the end become

that company, they all in the end become true believers in this AGI religion. And

true believers in this AGI religion. And so there's this vortex of it's like a

so there's this vortex of it's like a black hole, ideological black hole. I

black hole, ideological black hole. I don't know how to explain it, but people

don't know how to explain it, but people when they swim too long in the water, it

when they swim too long in the water, it just becomes them. So what you're saying

just becomes them. So what you're saying is Sam Sam Alman is the Lisan Algib.

is Sam Sam Alman is the Lisan Algib. That's the character and and Paul Graham

That's the character and and Paul Graham maybe was the, you know, the it would

maybe was the, you know, the it would seem it would seem like that would be

seem it would seem like that would be the most appropriate character to assign

the most appropriate character to assign to him. Yeah. Wow, this has been

to him. Yeah. Wow, this has been fabulous. And I have to say honestly,

fabulous. And I have to say honestly, the book is really, really exceptional.

the book is really, really exceptional. Empire of AI. I read it so much that

Empire of AI. I read it so much that dust jacket I think my daughter actually

dust jacket I think my daughter actually ripped it off. But anyway, uh it is a

ripped it off. But anyway, uh it is a sensational book. Sensational

sensational book. Sensational journalism, fantastic journalist. We

journalism, fantastic journalist. We don't have enough of those in the world.

don't have enough of those in the world. Thank you. Um real pleasure to meet you,

Thank you. Um real pleasure to meet you, Karen. Thanks so much for joining us. It

Karen. Thanks so much for joining us. It was great to meet you.

Click on any text or timestamp to jump to that moment in the video

Most transcripts ready in under 5 seconds

One-Click Copy125+ LanguagesSearch ContentJump to Timestamps

Paste YouTube URL

Enter any YouTube video link to get the full transcript

Most transcripts ready in under 5 seconds

Get Our Chrome Extension

Get transcripts instantly without leaving YouTube. Install our Chrome extension for one-click access to any video's transcript directly on the watch page.

Add to Chrome — Free

Works with YouTube, Coursera, Udemy and more educational platforms

Get Instant Transcripts: Just Edit the Domain in Your Address Bar!

YouTube

←

→

↻

https://www.youtube.com/watch?v=UF8uR6Z6KLc

YoutubeToText

←

→

↻

https://youtubetotext.net/watch?v=UF8uR6Z6KLc

YouTube TranscriptPreparing your results…

YouTube Transcript:Silicon Valley Insider EXPOSES Cult-Like AI Companies | Aaron Bastani Meets Karen Hao