The Deep View: Conversations

So there's a lot of ground to cover, right? AI is a really broad

topic. But I want to start with the book, Taming Silicon Valley. And

I specifically want to start with the very end of the book, where in

the acknowledgments, you write how you wrote

this book out of a sense of urgency and disillusionment at lightning speed.

And I'm wondering about, obviously, a lot of stuff has

been going on, but what kind of inspired that sense of urgency?

Well, I think you and I first spoke around the time that I

gave Senate testimony about a year ago. And I think even

then, everybody knew that having a good AI policy

was urgent for the nation. A lot of what we talked about that

day when I was at the Senate was the fact that with social media, the

Senate had not done well. It waited too long, made some bad choices,

and those bad choices became kind of enshrined permanently.

And we don't want to do that with AI. And everybody in the room seemed to understand that.

And yet, I watched the clock and the calendar tick away and

saw that nothing was really happening in the United States. The EU has the

AI Act there. And I think that's to the good. It may not be perfect, but

it's a great start. The state of California is actually trying to

do something now. The federal government, there

is the executive order, which is great, but it's not law. The

way our country works is the president can't make laws. are

actual laws here. Anything a president puts in executive order

can just vanish overnight. And I

increasingly had this sense that nothing was going to happen in the United States

Senate, even though many individual members were in favor. Chuck

Schumer was taking his time with all these listening meetings.

And I sensed that. And what I predicted would happen is

that nothing would happen. And that's, in fact, what has happened. So nothing

has happened. on the policy side, but all

of the things that I worried about at the Senate have kind of materialized. So

I worried that deepfakes would become a huge problem, that misinformation

would become a big problem. And we've seen all those kinds of

things happening. We've seen people manipulate

markets. When I said that, it never happened, but it's already happened

in the US and India. I guess in the U.S.

we're not sure about the motives, but we at least saw a

deepfake move in the market briefly. It's definitely happening

in other stock exchanges around the world. All the kinds of

things I was worried about are happening, and we don't have protections

against them. All the issues of bias. We now know from

an article that just came out that there's covert bias, where if you talk in

African-American English, you get a different response than if you speak

in standard American English. There's so many problems. And we

have no legislation really to address these

problems yet. There are a few bills. There's no kind

of comprehensive approach. And the problems are

just getting worse and worse. We've seen this movie

You mentioned one of those approaches, what's going on in California with SB

1047. Now, that has kind of, to me, been this perfect example

of why regulation is going to continue to be so challenging because

of the reaction to it. the whole lobbying movement against

it, the millions of dollars that are fighting the idea of this. And, you

know, it's this idea that regulation would

And I'm wondering... Yeah, the talking point is a lie. Well, exactly. Like,

sometimes regulation actually fosters

innovation. You know, that's how you get seatbelts, for example, is you have a law

that says you need We need to protect people. We wouldn't have whole industries like

commercial airlines that people were willing to take if we didn't have regulation.

So there's a certain part of Silicon Valley

that wants you to believe that lie, that regulation and innovation

are in diametric opposition. But it's not true. I mean,

we have innovation. In the car industry, this regulation doesn't

stop it. We have regulation in

drugs. That doesn't mean that everybody goes home. It's not like because we

have an FDA, all big pharma just drops out and gives up. It's

ludicrous. And at the same time, we don't want them to put any old

thing out there and have millions

of people take some drug that causes a lot of problems, which we have

seen occasionally. That's why we have a regulatory framework, so we can have innovation,

Right. It's the right kind of innovation, the innovation that

And some of that innovation needs to be towards safety. I mean, a

dirty secret of AI, and maybe it's not even that secret, is nobody knows

how to keep it safe. And nobody actually knows how to rein it in. So

we need innovation on the safety side. And you

have people like Yann LeCun that say, oh, there's no problem. But

even he, when he's pushed to be a little bit more honest, says, well,

there's no problem we can't fix. But we don't have anything encouraging people

You say in the book that the potential upside of AI is enormous,

just as big as the valley says it is. I'm

wondering to you what that upside looks like and what kind

of AI is needed to kind of access that upside. Are

we talking about a general intelligence here for that or is

Not even always machine learning. I mean, look, a calculator is

something that helps innovation in science. Always

has, always will. Just, you know, basic computation, you

know, going back to the 1940s has always helped with science. Not

everything that helps science that comes out of a

computer is going to be AI at all. Not all of it's going to be

machine learning. And, you know, there's every reason

to think that various forms of AI will continue to contribute

to science. Probably the most exciting game in town right now

is AlphaFold or AlphaFold3 from DeepMind, which

has been tremendously helpful. It's changed how people do protein science.

Everybody in the field uses it or at least knows what it is. That's

not a large language model. more complicated kind

of hybrid system with some careful domain engineering. My

concern mostly right now is with so-called large language models and

chatbots, which have gotten all the attention lately and all the

money or most of the money. Tens of billions of

dollars have gone into them. And they're not actually, I think, a particularly good

way of solving any kind of mission-critical problem. They're fun

to play with, but we're putting way too much money into them

relative to other scientific approaches to AI and other aspects

of science. I mean, people, it's crazy, like, if

an alien dropped in, they would say, these guys were doing pretty

great with science on planet Earth for a while. I mean, they were very slow to

get started, but, you know, eventually they kind of figured out the

scientific method and they're doing great. And then in the, you

know, 2120s, or sorry, 20, excuse me, in the 2020s, They

started doing this really bizarre thing where you had seven companies,

or something like that, all working on exactly the same thing, the

same kind of architecture, with the same kind of data, having the same

kind of problems, all just hoping for a miracle. And

of course that miracle didn't happen. And, you know, thank God in

2026 or whatever, they finally decided to do something different because that was

crazy. And these models were starting to be, you know, a billion dollars

a shot. You know, companies were spending five, 10, 15 billion

dollars on chips and they were all getting the same results. Like you

don't want to run the same experiment seven times. Like, you know,

good scientists like seize the trend. But here, oh, no. Like

the people, the desire for money has has overwhelmed scientific

Right. And so part of that, and you talk about this

a lot in the book as well, which is your point that money and power have

derailed AI from its original mission. And

part of that, too, is this idea of the increasing influence of the unelected tech

leaders on all of our lives where, you know, Sam Altman

can choose to put out whatever model, regardless of whatever harms

it. it may be capable of, and we all

just have to kind of deal with that. I'm wondering about that original mission.

And as you've kind of seen, it seems like AI in

that pure form of trying to help people has maybe lost its

I mean, it hasn't totally lost its way. Like, DeepMind is still, I

think, really trying to do some AI for good. But

it's largely lost its way. And I mean, it's just a

story of money and corruption and A story, I

think, of how AI went from good technical

leaders who really understood the science to people that are better at

marketing and hype and so forth. And they have been rewarded for

that marketing and hype. They've been rewarded by investors. They've

been rewarded by politicians who don't know any better. Now

they're being rewarded by Oprah. It's a

true story. the wrong things are

being rewarded. What's being rewarded right

now is people making crazy claims like that AI is

going to solve physics, which doesn't even make sense. It's not even a coherent claim.

But there's a certain group of people that wants

to feel like we're having this religious moment, we're being in this magical

thing. And so much too much power is being given to

a few people that were in the right place at the right time that can sell

those stories. And the field

of AI, I think, has suffered. It used

to be more of a research endeavor, but now it's a commercial endeavor.

And it also happens that the kind of AI that

people are building right now is very expensive to operate. And that has changed things.

So even you can see within OpenAI's history,

for example, I never loved them, but they were a more

reasonable company early on. And then they realized

that they could make progress, not that it was the only way to make progress, but that they

could make progress by making these very big models and that they were very expensive.

Yeah. And that also, uh, that endeavor kind of pushed

the idea of large language models into everyone's lives in a way of,

you know, dealing with issues of copyright, right. And

The copyright stuff is out of control. I just posted a video

yesterday. Somebody said, wow, look at this amazing new

thing. It's kind of like Dolly, but with videos out

of China. And it showed a lightsaber battle. And

the thing is that these people have obviously not paid for licensing

of Lucasfilm, or I guess maybe Disney, or whoever owns the

properties. But they've trained on it. And so it looks

very much like Star Wars characters. That is Darth Vader.

That is a lightsaber. And it's total copyright violation. Probably they

will eventually get sued. All of this stuff is actually built

on essentially all the data that companies can

scrounge, and that includes copyrighted data. And so you

have these companies that talk about, you know, they want to do AI

for the benefit of humanity, and then they sit here and

steal the property of artists and writers left and right with

no compensation. go around and they say, yeah, we want everybody to

have a universal basic income. But they don't pay licensing rights

Right, and part of that, right, is this idea that, and

you say this in the book, that the view in Silicon Valley is

that anything out there is mine for the taking, right?

And it's kind of... Yeah, and a new line which I don't

have in my book is, let all the lawyers clean it up, which is something Eric

Schmidt said in a private meeting at Stanford. I wish

they could add that in the book because I think it really captures

this whole Silicon Valley ethos right now

of Just move fast, break things, have the lawyers clean it

up. There's no attention to ethics anymore. One

of the things I talk about in the book is how Google used to say, don't be evil. That's

not even an official part of the motto anymore.

And things have really shifted in the last couple of years. I

think the general public doesn't understand how much it's shifted.

So you always had some companies that I would say were marginal on

the ethical side. Meta has never been on top

of my list of ethical companies. But Google, I

think, was more ethical than it has been of

late. Same with Microsoft. They used to talk a

lot about responsible AI. And now everybody's just trying to get market share,

Right. And that necessary shift that we're talking about towards

more ethically minded behavior and the idea of

responsible AI, is regulation the

only way to get that? Because we're talking about such a big shift in culture.

Yeah, exactly. Shifting culture. So part of the reason I wrote the

book is I would like the public to hold these companies accountable. If

the public is like, yeah, I understand that this cool

new software I'm using is pillaging artists, but I

don't really care. It's fun to use. Well, I mean, that

sets one expectation. Another expectation we

could say is, I don't want you ripping off artists, because artists are my

friends. And I don't want you ripping off writers, because writers are my friends. And I

don't want you ripping off anybody, because eventually you're going to come from me. And

so I'm going to take a stand right now and say, if

you're not going to ethically source the stuff that you're training on,

I'm not going to be part of this. Come back to me when you've got your licensing squared

away. And you know what? The companies would. It's not that all of these magical products,

and in some ways they are magical, would disappear. be

a little bit more expensive. But look what happened with Apple Music

and Napster and so forth, right? And there was

a brief period in the beginning of Napster around, I guess, around 2000, 2001, where

everybody was getting music for free. And it was really fun for them. And the artists and musicians

were getting totally screwed. And the court said the right thing, which is you need to

license. And you know what? We moved to a model where you

could have what you had in Napster, which was access to everything, almost

everything, but you paid a little bit each month so you pay

ten dollars a month or whatever for apple music and now you can listen

to almost anything and the artists get something they don't get as much as

before and i think that's unfortunate but at least they get something and

we can do that here we can have licensing

there's no reason that that's impossible the companies even

know it you know behind the scenes they are negotiating licensing deals

but their official line is like, you

have to give us this free so we can make the magical AI. We should

not believe that. And the consumers should not believe that

either. The consumers should say, hey, wait a minute, I want to be ethical

here. I want to support artists. I want to support writers. I'm not

going to use this stuff until they sort that out. That would have

Right. And part of the reason that this feels to

me like such a important moment is what you're talking about has

been built up for 20 years. You're talking about the Internet companies

that evolved into the social media companies and now they're the A.I. companies. Right.

And people didn't read the terms of service. They don't want to. A

lot of people don't care how their data is used and how other people's data

is used because data has become the kind of cost of admission to

I think we're like boiling frogs. People have come to accept way too

much. OpenAI just bought a webcam company. They want to be able to

have access to all your files, to all your calendars. And

they just put someone from the NSA on there, or used to be at the NSA, on

their board. I mean, OpenAI is,

in my view, possibly headed towards becoming a surveillance company that

knows everything about you, and then, you

know, monetizes your data. And you get nothing. You

get some free services and that's all you get out of it. And

the world has, I think, been too quick to accept those

kinds of things. And the consequence is that they're

going to, for example, be able to target political advertising to you and manipulate you.

There's new research showing, for example,

that large language models can implant false

memories in people. Like, we have no control, no checks

and balances over what the large language models are

going to teach you, essentially. And they will do it subtly, you

won't notice it, and you can, for example, wind up with false memories. This

is terrifying stuff. Everything that George

Orwell imagined in 1984 is kind of actually happening, and

we're not being loud enough about it. The reason I wrote the book is

to get the public to realize we need to say this is not acceptable. And

yet here we are, it's two months from the election, nobody's even talking about

AI policy. And the next administration is

going to set AI policy in a way that's going to last. You know, just

like once Section 230 was set in

place for social media, the rest of the movie was written at

that moment. So, like, we have to get this right, and we have to get

Speaking about the public's, the importance of the

public in this space and understanding the space, there's

been, and we talked about it, so much hype, so much

marketing, that's not really accurate to the science, right?

What are the most common, I guess, or most frustrating misconceptions about

Maybe the most frustrating is people just think that chatbots are a lot smarter than

they actually are. So they assume, for example, that

chatbots know what they're talking about. And kind of imagine

that they would like fact-check their work or something, but they don't, right?

They hallucinate constantly. In the book I have the example of

one of the systems just making up the fact, making

up the claim, that I have a pet chicken named Henrietta. And I

don't have a pet chicken named Henrietta. It would take two seconds to look

up and do a web search and see if there's any valid source,

because it's such a crazy thing. I mean, Like, I

live in a city. Where am I going to put the pet chicken? Like, it just made no sense

whatsoever. And who names their pet chicken Henrietta? And so

forth. The systems don't actually fact check. But people are

surprised. Like, every day somebody posts on Twitter, I can't believe how dumb this machine

is. Like, somehow we are not communicating enough to the general public

how dumb the machines actually are. And people get taken. Big

businesses all, you know, last year invested massively in

large language models, and then all realized it was disappointing. Over

and over again, people are learning that lesson. It's

been hard for the public to accept. And that's partly because most people have

no training. Like, how do you recognize what

a machine does, or how good it is, or that it's a machine at all? We're

very good at recognizing lions and tigers, because in the

environment of adaptation, we had to run from lions and tigers. We're not

very good at realizing we're being scammed by a bunch of

chatbots that don't know what we're talking about. Most people don't understand how

to resist that. I mean, partly the book is

Right. I think the most interesting thing

to me is kind of watching... The reason chatbots seem to

have grabbed people on and scammed people in the way you're talking about is

they seem to communicate in language. And we're just primed to

assume that if you can talk, you're a you. You're intelligent. And

And so... And then they do little things like they type words out one at

a time. The answer is actually pre-computed probably,

or could be pre-computed, but they do that to make it look more human.

Developers are trying to trick you. The people

who most want to be tricked are most seduced. The more cynical

among us might reach back at it, but

some people want to be seduced by the machines. They want to fall in love with

Yeah, the whole AI girlfriend apps is a whole thing,

but in terms of the limits of the current architecture,

large language models, the backing of generative AI,

you've talked a lot about how deep learning is hitting a wall, which we're

seeing a decent amount of evidence for, and

the idea that... Yeah, probably a better phrase would be reaching a point of diminishing

We've shifted from a regime where there was spectacular progress

over a period of about three years, like 2020 to

2023. We shifted from that to a much more incremental thing. So,

you know, GPT-4 was trained over two years ago.

Most people don't realize that. And everybody was like talking, oh,

exponential progress, AI gets better and better every day. But

we've only seen minor improvements relative to GPT-4. There's no, you

know, GPT-5 worthy thing. Every time OpenAI comes out with a

new model now, They sit there and like, well, we better not call this

GPT-5 because it's really not that much better than GPT-4. So

what should we call it? And they come out with these answers like GPT-40. Like, what

is that? You know, I think a bunch of these are efforts or

GPT-Turbo, I think was an effort probably to

make GPT-5 and then came out and they're like, yeah, this isn't

really that great. Like, it's a little bit better, but it's not that great. And

so we have hit, I mean, I interrupted your question, but I would say we

haven't absolutely hit a wall overall. There's still little improvement.

We have hit a wall on certain things. So these systems still are

not reliable. They still hallucinate and make up stuff like Henrietta,

the pet chicken. Like that is a wall. Like we have not gotten

past the wall that you don't know what you're getting out of these systems. that

we're just stuck. And then other things like, you know, the graphics get

better. So now we can make movies instead of just still shots.

So there's some progress. But on the core reliability

and reasoning and so forth, it's at best diminishing returns now.

At the same time as we're kind of noticing that, there is so much,

or seems like such a large number of people, right? And

I guess if you're on Twitter like I am, maybe the numbers are

not quite accurate, that think, as you

mentioned, that it's moving exponentially. Every new release

You know, AI influence say that stuff on Twitter and

they got like a million views for the things. Most of them would not know their

ass from an exponential. I mean, an exponential is actually a mathematical thing

where you plot a curve, you fit it and you see like that's

what it would actually mean to be exponential. They don't know

how to do that. Like they're talking about AI and it's like basic, you

know, like grade school math thing or high school math thing that

they just don't understand. Exponential does not mean

that every two months it's a little bit better than last

time. Exponential would be like what we saw before,

where you have these giant qualitative changes every few

Now something that's kind of gone in hand with

all the hype that we're seeing is the investment side

of things and the investment hype and the crazy valuations in

the markets. And as investors have started to realize that

there are diminishing returns, maybe it's not worth all this money, it's

led to the idea of the AI bubble. And you've called OpenAI

possibly the WeWork of AI, that the bubble

I was one of the first people to say there's a bubble. And by that, I

mean a bubble in the valuations. I don't think large language models

will disappear. They're like a tool. We have a lot of different tools for AI.

We need a lot more that we haven't discovered. An analogy would

be like the minute that somebody invented a power screwdriver.

They thought power screwdrivers were going to change the entire world and that

they'd be worth billions. And no, eventually the price would come back down

and, you know, people would pay 30 bucks for them. They'd still be great. People would still love them.

But, you know, they'd be a $30 tool. They wouldn't be like, you

know, constructing your entire home all by itself. And

so that's what's going to happen here. Large language models are not going anywhere.

They have some use. They're not super reliable, but they're good for

brainstorming and autocomplete when you type computer code

and stuff like that. That'll stay. But the idea that open

AI is worth $100 billion is really marginal.

So open AI, right

now it's valued at something like $80 billion. Someone's about to maybe

put in money at a $100 billion valuation. Usually

valuations are relative to profits. They actually have none.

They've not made a profit yet. This is very expensive to operate. These

tools unlike traditional software. You need these expensive chips. You

need very expensive staff You need enormous amounts of

electricity. There's no clear evidence that

they're ever going to make a profit You know if you have to pay 20 billion

dollars a year in order to stay ahead of meta

that's giving a steam stuff away for free Which is also a huge

problem for them and you're making two billion dollars a year. The math just

does not add up And so what I would imagine is at

some point, people are going to stop putting the money in. Right now, OpenAI

is a name brand, and maybe they can get

away with it for a little longer. But they have not transformed the

world in 2024 like they did in 2023. They have

demoed a lot of products that they have not actually delivered. Or

they've delivered products like GPT-4.0. Some

of the things they promised are there and some not. Sal

Khan talking about how you can use it as a tutor. There's no actual tutor

software out there. They talked about Sora, but you can't use it

at home. We don't know how much it would cost. Um, you

know, so they're making demos and now they're going to make an

announcement. Maybe it's going to be called GPT next. They make demos cause

they have learned that making demos drives up the price. But

it doesn't forever, right? So what's gonna, it's a little bit like a Ponzi scheme.

At some point, somebody's gonna say, where is

the money? And in fact, you know, I wrote an article about this a

year ago called, is generative AI

a dud? Or what if generative AI turned out to be a dud or something

like that in my sub stack? So I wrote this article at the time, I

was somewhat alone. There were a few other people maybe saying the same thing. Now, like

every week, somebody's saying that. And the thing about bubbles

is they're all about psychology. So what is a tulip actually worth?

Well, a tulip was mostly worth what people will pay for it.

And so you can, you know, for a few years, people were paying, I don't know, thousands,

tens of thousands of dollars for tulips. And then they all woke up

and said, you know, this is a little crazy. It's just a tulip. It's just a flower. You

can buy another flower for five bucks. Why are you spending, you know,

$10,000 for a tulip? It's not really worth it. And then everybody runs

for the hills. And that's what's gonna happen here, is everybody's gonna

run for the hills. So you could ask, well, who made money and who gets screwed? So the

early employees still make money because they've sold some of the stock, right?

So early employees of OpenAI do well. The

early investors make money because they take a cut of the money that's invested.

And then the people that invested in them get screwed. So you

have, for example, pension funds. And the people who operate the

pension funds make money because they've invested the pension funds

money. But the people who are actually invested in the pension funds,

the employees of California or whatever, are in the end going to get screwed

because OpenAI is most likely not going to make back

the $100 billion valuation. So like, okay, let's say they issue

stock now at $100 billion. And then next year people are

like, yeah, they're still not really making any money. And

now they need more money. They need it badly. So they take money at

a $50 billion valuation. Well, the people who put in money at

$100 billion valuation just lost half their money. So we're going to

see. And then, you know, what happens with these things is once One

thing goes down, then a lot of them go down. Everybody runs

for the hills. And so I think that's going to happen. Again, the

technology will remain, but people are going to be like,

yeah, maybe NVIDIA is not worth $3 trillion after

all, because NVIDIA has a problem. They make a good product. They

really do. I met Jensen Huang. He's a brilliant CEO.

Everything they do is good. But it's all

kind of premised on the notion that the people who buy their chips,

which are very expensive, tens of thousands of dollars, are

going to make a lot of money with those chips. And if that premise turns

out to not be true, like there's still no killer app

for generative AI. There's just conversation. If that conversation doesn't

become profits, then eventually even NVIDIA winds

up in trouble because eventually people stop placing big

orders for chips because they can't quite figure out what to do with them. I mean, you

know, NVIDIA is not a charity, right? I've

made jokes about Jensen Huang's pension fund being a

charity, but it's not, right? I mean, it's just a joke. You

know, eventually, like open AI, if they don't get

enough money to keep buying all of these chips, then NVIDIA suddenly

loses a big order and then the stock goes down. And so this is

how I think it's going to happen. I don't know if NVIDIA will be first or

last. The psychology of all of this is complicated. But the psychology is

it's not really sustainable at these valuations. It just isn't

because the profits aren't there. Now, some form of AI might be worth

trillions of dollars, which is what everybody's imagining. But generative AI

has technical problems. I don't think it's ever going to be worth that. And so

generative AI, you know, people are eventually going

In terms of those other forms of AI that you were just kind of mentioning there, I

really want to talk about the idea of new paradigms, new architectures, neurosymbolic

AI. I know you've been talking about neurosymbolic AI for a while. What

does that architecture look like and why is it different from

We don't know is the first part of the answer. We

know a general direction to look. There's every reason in the world,

and I will explain that reason. But we haven't really done the work yet, so

we don't really know exactly what it's gonna look like. And in

fact, the thing I'm gonna describe to you, neurosymbolic AI, is

part of an answer, but it's not an answer by itself. I mean, everybody is looking

for AI to be solved magically, and it's just not gonna happen.

Intelligence is, as Chas Firestone and

Brian Shull once said, not one thing but many. There's many different facets

to intelligence. And we've kind of solved one of them, which is

kind of pattern recognition. And there are others we just haven't solved

at all, like reasoning. We don't have a good answer to how to

get a machine to reason. We especially don't have a good answer about how

to get a machine to reason over open-ended problems. So in

very simple things like logic puzzles, machines are great. We've

been able to do that for 75 years pretty well. So

some of the kinds of things that like You know, your 10-year-old reads in school,

you know, A believes in B and doesn't know C, blah,

blah, blah, blah. Those kinds of things we can actually solve pretty well. But

logic in the real world where there's a lot of kind of incomplete information, reasoning

in those cases, we're not that good at. We have

some sense from this classical tradition of AI of how to do

it, but we don't know how to do it at scale when there's a lot of knowledge involved.

Many of your readers or listeners might know Daniel Kahneman's

System 1 and System 2 distinction from his book Thinking Fast

and Slow. System 1 is stuff that's automatic, fast,

reflexive, intuitive, data-driven, and System 2 is

more deliberative, more abstract, more reasoning-like. it's

pretty clear that current AI is good at system one, older

forms of AI are pretty good at system two, not perfect, and

we don't have a good way for those to communicate with each other. That's really

where the advances in AI have to come, I think, is how do you

bridge these traditions? And it's not trivial, because even once you do,

you probably have to put a lot of knowledge in machine interpretable form,

and you have to do something called extracting a cognitive model from a

real-world scene. Nobody knows a general way to do

that. So, you know, some people are like, my timeline for

AGI, meaning when I think general intelligence will come, is

like 2027, or Elon Musk says the end of 2025. And

it's so crazy I offered him a million dollar bet, which he didn't get

back to me on. And it's so crazy that a friend of mine actually upped it to

10 million dollars and still, and there's a Wall Street Journalman after Elon,

and still his people wouldn't respond. It's a completely crazy claim

to say that we will have artificial general intelligence by the end

of 2025. The more you understand the artificial intelligence

at all, the clearer that is. My training was in

how human intelligence develops. And once you look at that, like

what a child learns in the first few years of life, it just becomes

obvious that we're not there yet. We have something that sort of like vaguely approximates

it some of the time and makes complete absurd errors that no human

would ever make a lot of the time. So yes, we need to bring these

two traditions together. That's what I would call neuro-symbolic AI. Part

of the book is advocacy for that, saying, for example,

that people think that whoever has the biggest large language model

is going to win the race to AI. I would say it's whoever sponsors

the most innovation to find new things is going to win the race to

Now, in terms of general intelligence, right? And Musk

has his timelines. Musk is a hype man for everything. And,

you know, everyone seems to have their own timeline. And some of

that is tied to the idea of existential risk and

P-Doom, right? And a lot of kind of pseudoscience that

I mean, P-Doom is a real thing in a way, right? audience,

PDoOM means the probability that machines are going to kill us all. My PDoOM is

very low. Some people think it might be like 50% or

even 50% in the next three years. Strangely, some

of those run companies that might cause that if you're to believe their

story. It's a form of hype to say, oh my God, what I'm building

is so amazing and dangerous that might kill

us all. My view is we're not going to annihilate the

human species anytime soon. That that would take considerable work

that is not likely to happen. So I

don't think that scenario is going to happen. But

there's all kinds of other risks associated with AI around misinformation,

disinformation, deepfake porn, bias. I

have a list, as you know, in the book of a dozen different risks There

could be accidental escalation of war. There's many many things that

could happen short of actual extinction Right and the whole

environmental side of things as well. The the massive environmental side

the costs are huge I mean, nobody really knows where this is going that people

are talking about. Well, I have this big model I trained it on the internet didn't

really work So I'll train it on ten times the Internet or a

hundred times the Internet. Well, that's gonna cost a lot of energy I

think one training run might be I'm just making up these numbers might be

like a all the power that the country of Germany uses for

a year at some point. If you keep scaling it up, you're just using insane

amounts of power, insane amounts of water. There's lots of emissions. So

I mean, there's some work, I would say some of the more successful work, to

try to minimize that. But as the models get bigger and bigger, even

Part of what you were saying about the element of human cognition

and human intelligence, where if you study that, we are where we are.

A lot of what I've seen- And we don't just want to replicate it, by the way. Sorry

to interrupt your question, then you can go back. We don't want AI to be just like people.

People have lots of problems. You don't want a calculator to do arithmetic like I

do. I forget to carry the one. I don't pay attention. We're

not here to replicate human intelligence, but we do want to get from

human intelligence the resourcefulness. you know, you can have a

conversation with me in a conversation with someone else on a different topic and

understand them both. Or you can, you know, after we talk, you

can build an Ikea bookshelf or whatever. Like, human beings can do many

different things, even sometimes when the instructions are poor,

you know, you eventually figure it out. It's that resourcefulness of

the adaptiveness of intelligence that we would like to

get into our machines so that we can trust them when we give them new tasks

I don't know if I could do the IKEA bookshelf. It might take me a little while. But

I guess the idea of a general intelligence being possible, right? There's

a lot of debate about whether it will be possible at

I think it's certainly possible. I mean, look, your brain is

just some kind of machine, just like your heart is, you know, a

mechanical pump. Your brain is an information processor. There's

got to be some kind of Um, information processing, we haven't

got the right one, but, you know, I see no principled reason

why we can't build a general intelligence. I just don't think we're doing it

right. I mean, it'd be like, you know, if somebody had

argued in da Vinci's era, you're never going to have a helicopter. That would have been a

wrong argument. It would have turned out you couldn't do it with the materials he

had available. Right. We didn't know enough about internal combustion engines.

We didn't know enough about material science and so

forth. But it wasn't that it was impossible. It was just we we

weren't really ready to do it. That's where we are with AI is like Da Vinci and

helicopters. Like we have the vision now of what this would be like and

why it would be amazing. And we have no reason to think

it's impossible. There was no proof that you couldn't build a helicopter. There's

no proof that you can't build a general intelligence. And I'm

sure that, you know, within 200 years, we'll do it may well

do it in 20 years. I'm also sure that, you

know, we're not going to have it next year that Elon is, you know, either diluted

He did not take it. I mean, that tells you a lot, right? I mean, We

got the bet up to $10 million. It could have been like a symbolic thing. I

mean, obviously, he doesn't need the money. But the fact that he wouldn't even

respond, it doesn't bespeak

Now, with AGI, we're dealing with a hypothetical thing. As you

said, we don't know what it will look like when it will be here. And

there's been a lot of concern on the regulatory front of, you

know, there's so many active harms from AI now.

We shouldn't lose focus on that to regulate for a

potential AGI that we don't know if or when it's coming. How

important is it to kind of balance

that and prepare ourselves in legislation for

What we need to do now is to deal with the current harms

that we know about, most of which are not well handled under American law.

And we need to plant a stake in the ground so that when things

do you know, advanced, we have more intelligent systems

that we have a framework for dealing with them, which includes like communicating with

other countries about risks and sharing information about bad

incidents and so forth. So, like, I think of where we are

right now as a dress rehearsal. It's not the AI that

we're going to have 20 years from now, any more than like the first flip phone

is an iPhone, right? I mean, like the first flip phone was just as

a preview of, you know, what phones could be. And

the AI that we have now is just a preview of where AI is going.

And we don't fully glimpse it yet. But

we already see that there's pressing need to know

what to do with it. And we've already seen how the

companies have gone from publicly saying, oh, yeah, we need

regulation. We want this to be safe for everybody, saying

those things in public, like when Altman said exactly those

things next to me at the US Senate around the time we met, like

privately and sometimes even publicly opposing actual legislation coming

up with excuses like OpenAI said about the California bill what

we need this to be federal. Well that's true we do need federal

law here but like we got to start somewhere and the state law

is here it's not really going to make the federal law Harder the federal law

can supersede the state law if we need to do that like this is

a bogus argument It's an argument of the company that doesn't actually want

to be regulated despite whatever they might tell the public and like

we should be suspicious and we should start to view these companies like cigarette companies

that you know would downplay risks and try

to play the public with expensive lobbying campaigns and

you know, secretive research that, you know, they would put out

under other names and all that. Like, we should think these

guys have a lot of money at stake and they are playing us.

I mean, a whole section of the book is really about how they tend to do that,

Towards the end of the book, you answer a question that

I have asked myself many times, and you say, we

should not stop AI. We should insist that it be

made safe, better, and more trustworthy. At the same time,

there's been a lot of other, I guess, discourse on

that idea from a bunch of other people, right? There's all these categories of

folks within AI, and the kind of Doomer argument, right?

And the pause AI, and then those kinds of folks saying, shut it down now, oh

my god. And I'm wondering, like, that side of the argument, and

part of that argument is not all existential AGI. A

lot of it is focused on the current harms that we're seeing and there's no redress for

them. Is there anything of value in the idea of slowing

I think I would start by making a distinction between research and deployment. So

there is no research that I know about right now that

seems, like, so deadly that I would say, like, just don't do it. I

could imagine such a thing, but I don't... I don't see it

from what I read about. On the

other hand, are these things problematic? Should they be deployed now?

I can see a real argument saying, no, until you get your house in

order, don't deploy it. So this is what we do with

medicines, right? We say, make a safety case and

make a case that the risks outweigh, sorry, that the

benefits outweigh the risks. And if you can't do that, we don't say,

never make your drug, but we say, come back, tell

me how you're gonna mitigate the risks. Um, you

know, maybe it's a simple thing. You can just tell people, don't take it on an empty stomach

and we're good to go. And now, you know, we have a solution here. Um,

don't trust, you know, let's have a big public campaign and say, don't

trust these chatbots. I mean, that's what you need for, you know, one particular thing.

I think there are other problems that are not so easily solved. Like these systems are

clearly discriminating and they're clearly being used in job decisions

and that's not cool. And you could make an argument that the cost of

society for discriminatory behavior and hiring is

pretty high. and you could say that's a reason

why we should slow down on deployment or pause on deployment and

say look you're perfectly well in the ship software of

this sort but we need a solution here figure this out you know

it takes you six months great if it takes you two years then so be it

like come back to us when you have a handle

on this what about misinformation can you make a watermark that

actually works so we'll know what's generated by a machine can

you guarantee your stuff will be labeled you know what can you do for

us here so that Society doesn't pick up all

the costs. Another thing I keep thinking about is all

these companies that used to pump toxic chemicals into

the water and society had to pick up the cost. There's this phrase, I

don't know who made it up, I wish I had, which is to privatize the

benefits and socialize the cost. We've seen that happen a

lot of times in history and I don't want to see that with AI because I'm

part of the field. I built an AI company, I researched it

a lot. I don't want to see AI privatizing the

benefits and socializing the cost. And that's exactly what I'm

seeing. And that's why I wrote the book, is because I don't think that's fair.

In terms of the benefits, too, which part of this equation, as

we've talked about, is the benefits in terms of great

stock valuations and tons of money that's pouring into this kind of small chunk

of people running things. But on the other side, you

know, looking through the chatbot curtain to the

applications of AI and often just

machine learning that are being used, what

stuff is going on there that has you excited

or hopeful, I guess, for the positive impact that

My hope is more around things like DeepMind's AlphaFold than it

is around chatbots per se. Chatbots, to

me, have some value, but not great value. They're

good for brainstorming. You have a human in the loop who can check it. They're

good for computers, basically

for a form of autocorrect that helps coders write

faster. Even there, there's some risks, like quality

of security may go down, some studies have shown. I

worry about their use in medicine. AlphaFold

is a tool built for a problem, which is take this protein

sequence, the sequence of amino acids,

and tell me what its three-dimensional structure is. That itself turns

out to be an incredibly valuable problem because so much of biology is

really about three-dimensional jigsaw puzzles. So having that

tool, which is not a chatbot, is super helpful. And

we should be putting more money into those kinds of things. And I'm glad to see that

Google slash DeepMind has put money into those things. Chatbots themselves,

I'm just not that excited. And I see all of these negative

consequences. Or another study I saw this morning was about

tutoring. It was a math problem

thing with Turkish students, a thousand subjects, and

it was very helpful when kids were working on the practice problems,

but by the time the actual exam came, whatever benefit was there

was lost. Probably, I would hypothesize, though I'm not sure, because

chatbots sort of like, you know, give you some memorized problems

you remember in the moment, but they're not really teaching you the conceptual stuff very well,

and they're also giving you an illusion of understanding things better

than you actually do, which is not particularly healthy. But whatever reason,

like the data there on the educational benefit was actually very,

very weak. And so, you know, over and over we're seeing people

get excited about applications and they don't pan out as well as people thought.

I'm just not sure chatbots are, to use that phrase from

Star Wars, the droids we're looking for. Like there's,

undoubtedly huge benefit from AI. Two other things I love

in AI are GPS navigation directions. I

travel all the time. That's fantastic. I'd be really disappointed to

give that up. I actually love maps, but it's a pain

to work with a map when you're in the city and it's not on a grid and

whatever. And so, you know, I love GPS directions and

I love Google search or I use DuckDuckGo for privacy reasons

or whatever, but, you know, using

machine learning to help you do web search is fantastic. So

like, it's not that I don't like AI, but chatbots, you

know, if you ask me their positive value, I'm just not sure. Like

if chatbots disappeared from the planet Earth, at least until we could figure out

how to make them work as well as they pretend to work, you

know, that'd be okay with me. On Twitter the other day, I said,

I won't remember the exact word, but I basically said, chatbots answer

everything with the apparent authority of an encyclopedia. They

sound like they know what they're talking about, but the reliability of

a magic eight ball, like, I don't need that in

my life. And, you know, I'm surprised as many people do. I'd

I also read the study of the Turkish students. Very fascinating. I

would hope for a lot more studies about that kind of thing. And we're seeing a

lot of integrations of

these current systems, which, as you said, are not reliable. They do hallucinate. We're

seeing it pushed into education in a lot of concerning ways. And

you kind of talked about what's lost, right, in students learning through

whatever this unreliable tutor. Now, in

the goal to get better AI, to get AI that can reason, that is

reliable, that doesn't hallucinate. I've

been wondering if that would be such a good thing, because

right now we kind of have the backstop of we know, or if you know, you know

these systems are not reliable, don't trust them, right? The

idea of this complete and further integration of my GBT

You know, I always get painted as hating AI, but in principle I

love things like AI tutors. Like, if

they could work, you know, they didn't hallucinate anything,

they could reliably build a model of what the student

understands about the problem, you could make them in

principle cheaply enough, then you could help all kinds of

people who can't afford individual tutors right now. I think that'd be

great. Like I see no moral reason not

to do that. I could see arguments that like if you had that technology would

it be dangerous in some other way and we obviously have to look at those things

and understand the whole, but like in principle, I love these

use cases. I love the use case of, you know, home

doctor because every doctor is overburdened and many people in

the world don't have enough access to doctors. I love the idea of

a, you know, dermatology app that you can really trust

because not everybody has access. So, you know, I really want

to see this stuff work, but I just think that

what we have right now doesn't work that well. We're overselling it, that

the overselling, it has multiple problems, including the fact that

people get bad answers. The fact that there may be, I think there already is starting

to be public pushback. You know, I think in some ways that Silicon

Valley is its own worst enemy right now by constantly overselling things.

We're getting to like a boy who cried wolf thing. Like if somebody comes

along now with a perfect home doctor in an app, A

lot of people are going to be like, yeah, I don't know. I tried this thing

a couple of years ago, and it told me to take this medicine, and

I got really sick, and forget it. Or with

driverless cars, we're pushing them out too fast. If we suddenly put

millions of them on the road, they would have accidents every day, and people would die, and

people might cancel the whole thing. And it's better that we have at

least somewhat of a phased rollout. overselling

this stuff has a consequence, and it just doesn't work

that well yet. We may see in software, where people have

been using a lot for coding, that the code can't be maintained very

well. So in software, one challenge is to write code that

works, and another is to come back to it a year, five years, 10 years,

20 years later, and have it still work, and be able to change

it, because circumstances change. The most famous example of this is probably the

Y2K problem, where people Program dates

with two digits instead of four, and it cost millions of dollars

of work had to be done. The public doesn't know too much about it anymore because

they actually managed to solve that problem. It was a big mess at the time. Every

piece of software breaks eventually because something changes. You're

calling some other piece of software. And you need, the

thing about programming is like in the moment that you write it, you know how it works. But then

if you come back later, you want your code to be clear, well documented, et

cetera, et cetera. There's a big risk We

call it technical risk or technical debt that the code that people are

going to write with these machines They don't really understand it and it's

kind of like kludge together. I mean hodgepodge together and

You know, we have trouble fixing it. So we may see some downstream costs as

Right, we're talking about all these problems. And there's so many.

And I think for a lot of people, it's really overwhelming and

frightening in ways that range from, I'm

going to lose my job to, oh my God, what's going to happen? I've

seen the Terminator movie, right? Whether or not that's legit. What

should people be doing here? How do people process this?

I would like to see people get loud about

this, talk to their congresspeople, write op-eds,

maybe consider boycotts. So, you know, one that I suggest at

the end of the book, and I'm going to set something up on this at

teamingsiliconvalley.org, is maybe

we should boycott any art that isn't properly licensed.

I mean, any, sorry, any generative AI that isn't, that's

using art that isn't properly licensed. Right? We

should just say, look, as consumers, we want to have all

of this stuff be ethically sourced. And we'll just wait. We'll

sit and wait until you do that. And so I think

collective action here to say, we want AI

to be done in an ethical way, in a way that

is good for everybody and not just a few people. We're going to stand by

that. Like, if we could actually get enough people To

agree on that, we could change Silicon

Valley. Silicon Valley used to, I think, care

more about the consumers. And nowadays has an attitude of like,

yeah, we're just gonna make money off those people. And we

Yeah, absolutely. Well, Gary, I really appreciate your time. This