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The Man Who Calls BS On AI: They’re LYING About AI, 2027 Is When It All Breaks! | Ed Zitron — Transcript

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0:00I think generative AI is at its heart
0:02con and seeing these ultra rich ultra
0:05powerful people lie through their teeth
0:07turns my stomach. The word con is a
0:09strong word.
0:10>> Well, what do you call something where
0:11from the very beginning they've sold
0:13[music] it in the terms of magic but
0:14it's just a halfass arcery machine. They
0:16are misleading the entire world.
0:18>> You are the first person that I've
0:19spoken to that has that opinion.
0:20>> Well, the fact that this is happening is
0:22insane and the fact it's not a scandal
0:24is insane. And I've been in the tech
0:26industry for 16 years now and I love
0:28technology and I'm enthusiastic about
0:29it, but I don't like being misled. And
0:32this is the largest non-consensual push
0:35of technology in history.
0:36>> So, we're going to play a game, Ed. I
0:38have the things that you consider to be
0:40myths about the AI industry.
0:42>> Let's play it. The AI industry is
0:44creating enormous economic growth. No,
0:46it's not. All of these companies run at
0:47a horrifying loss. Open AI lost $20.9
0:50billion last year. None of these people
0:52can just say, "Yeah, we're on the path
0:53to making this profitable." because they
0:55can't.
0:55>> Next one.
0:56>> AI will replace all human jobs. That
0:58just isn't happening and there's no
1:00economic data to support it. Next, the
1:02United States need to spend trillions to
1:04beat China in the AI race. What's the
1:06race to do for us to constantly piss our
1:07pants worrying about China? But people
1:09keep saying, "What if these models fall
1:11into the wrong hands? They're already in
1:12the wrong hands." Mark Zuckerberg, Sam
1:14Olman, Dario Amade.
1:16>> Mark Zuckerberg says, "We'll continue to
1:18invest aggressively in infrastructure to
1:20meet the demand." God met as a
1:21monstrosity. Makes me think of Shrek
1:23with L fogquad. Some of you may die, but
1:26that's a risk I'm willing to accept. If
1:27only these people gave a about
1:29poverty or actual problems in the world
1:31versus are we buying enough GPUs. If
1:34this continues, [music] what does the
1:35future look like?
1:41This is super interesting to me. My team
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1:44of you that watch this show subscribe.
1:46And some of you have told us according
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1:54if you are a regular viewer of the show
1:56and you like what we do here. We're
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2:29Please help us. Really appreciate it.
2:31Let's get on with the show.
2:33[music]
2:36>> Ed Zitron,
2:38there are a number of things that you
2:39believe that a lot of other people don't
2:41believe, right? You have, I think, a
2:44couple of controversial opinions and
2:45opinions that are in contrast to the
2:48other guests that I've sat here with.
2:50What exactly are those opinions, Ed? I
2:53think generative AI is at its heart con.
2:57I don't think it is sold as honest
2:59software. I think that they overstate
3:02both what it can do, what it will do,
3:04and the underlying financials to the
3:06point that they are misleading the
3:07entire world. And they're actively
3:09exploiting the weaknesses in journalism,
3:11in our economies, and indeed within the
3:14responsible parties with sellside
3:15analysts, governments, and all over the
3:17shop.
3:17>> The word con is a strong word.
3:19>> Yeah. I mean, what do you call something
3:21where from the very beginning they've
3:23sold it in the terms of magic as this
3:25thing that will replace all jobs, that
3:27will cure cancer, and all of these
3:29things? And when you look at it, it's
3:31boring cloud software that's extremely
3:33expensive and unprofitable and also
3:35unreliable at its core.
3:37>> People will be asking where are you
3:38drawing from in terms of your
3:40references, your personal experiences?
3:41Where were you educate? What you study?
3:43What you write about? What do you do Ed?
3:44>> So that's the funny thing is people say
3:46he's not got a finance experience. He's
3:48not going to take. I've been in the tech
3:49industry 15 16 years now in PR but still
3:52had practical experience and I love
3:54technology and I'm enthusiastic about
3:56it. And this thing just comes along that
3:58everyone is telling me is the best thing
3:59since sliced bread. And it can't even do
4:01the basics. It can't even do search.
4:03Well, whenever you ask an AI person,
4:05well, what's your setup? They describe
4:07this PeeWee's Playhouse thing of like,
4:09well, you got to harness here and you
4:10got to use the right prompt. Well, you
4:11don't want to use that prompt. You want
4:13to use this prompt here with this model,
4:14but don't use this model for the
4:16beginning, but at the end, you're going
4:17to want to use this model. And this is
4:19meant to be artificial intelligence.
4:22It's meant to be smart. It's meant to be
4:24autonomous. It's meant to be something
4:25that you set and forget.
4:26>> We have the sort of six leading AI
4:28companies on the table here. Anthropic
4:30Amazon, Nvidia, Microsoft, OpenAI,
4:32Google. You're saying that their
4:34fundamental business model is a con.
4:37>> Well, their revenues are not really
4:39coming from AI. Up until fairly
4:41recently, none of their revenues were
4:43coming from AI. Like dribbles a bit.
4:44Right now, 70% of all AI revenues across
4:48those three companies are from OpenAI
4:49and Anthropic to unprofitable,
4:51unsustainable companies that literally
4:53cannot afford to exist without these
4:55very same companies giving them money.
4:57Amazon sent $50 billion to OpenAI this
5:01year. They sent $5 billion to Anthropic.
5:03Google sent $10 billion to Anthropic.
5:06And in the next three and a half years,
5:08OpenAI and Anthropic based on actual
5:10sellside analyst evaluations, their
5:12estimates that inform whether stock is
5:14going to go up or down after earnings,
5:16they are expecting 400 or more billion
5:19dollar of revenue, 30 or something% of
5:22cloud growth just from these two
5:24unprofitable companies that will need to
5:26be given the money from somewhere. And
5:28on top of that, these companies have
5:30such low respect for the average
5:33investor, for the analyst, for everyone
5:35really that they don't even disclose
5:36their AI revenues. The few times they
5:38dain us worthy, they use something
5:40called a run rate, an annualized run
5:42rate, which means well, nothing. They
5:44never define it. It can mean months 12.
5:47It can mean month 13. It can mean last 4
5:49weeks time 13. It's different every
5:51time, and they never define it. And then
5:53they sometimes just don't mention it.
5:54So, you've got this big thing that is
5:56meant to be the biggest, most
5:58influential change to software ever. And
6:00whenever you ask them about it, when you
6:02say, "What? How much you making from
6:03this?" They go, "Oh, I couldn't possibly
6:04say. I'm too shy." These are public
6:06companies, or at least the ones that
6:08aren't anthropic and open AI. When they
6:10have good news, they'll tell you. And
6:11when they don't tell you something,
6:13well, that actually speaks volumes.
6:15>> Have you you used these tools, the AI
6:17tools, Gemini, Anthropic, Chat, GBT,
6:20etc., and you found no value in them?
6:22There's some value, but it's not there's
6:25they have spent over a trillion dollars
6:26in capex. What
6:27>> does capex mean for you?
6:28>> Capital expenditures. So, when you are a
6:30business and you have operating expenses
6:32like electricity, for example, those
6:34come right off immediately. Capital
6:36expenditures are long-term investments
6:37that are theoretically one-off. So, a
6:39data center or indeed the GPUs you put
6:41inside an AI data center.
6:43>> Okay? So, you've got a data center
6:45>> and then you have these GPUs which are
6:47like computer chips. So AI GPUs are much
6:51bigger, much more power intensive. They
6:52take a bunch of high bandwidth memory
6:54and they because of how many of them you
6:57need. You need thousands of them, tens
6:58of thousands, hundreds of thousands in
7:00some case. You need a bunch of power. So
7:03an example, OpenAI and Oracle are
7:05building a data center in Texas in
7:06Abalene, Texas. 1.2 GW called Stargate
7:10Abene. Within that, with each one of the
7:12eight buildings, there'll be 50,000
7:15Nvidia GB200 GPUs. So, city of Bristol
7:19takes about 7800 megawatt of power a
7:22year, right? Well, Stargate Abene is
7:26condensing more power than that, 1.2
7:28gawatt into a space around 1,172
7:31times smaller. City of Bristol is about
7:331.2 billion square ft. Star Evelyn is
7:36about 998,000.
7:38So, you're condensing all of this power,
7:40all of this money, all of this labor
7:41into this one spot. And all of these
7:44data centers cost billions of dollars.
7:46All of these companies other than
7:47Microsoft are now to take out debt. And
7:50the thing is they've spent over a
7:51trillion dollars so far and they want to
7:52spend another trillion dollars next
7:54year. And for what? To make tens of
7:56billions of dollars, most of which comes
7:58from two unprofitable companies,
8:00Anthropic and Open AI. One of the
8:02rebuttals to that would be that the
8:04adoption, the customer adoption of
8:07people using Open AI and Enthropic has
8:09been absolutely insane. These are the
8:11fastest growing products in all of
8:12history, especially as it relates to
8:14sort of technology. If we just focus in
8:16on technology, they are, you know,
8:18hundreds and hundreds of millions of
8:19people, billions of people are using
8:21these tools every single day for things
8:23that they have subjectively decided are
8:26problems they need solving. So, you
8:28know, money is a lagging indicator of
8:30value. So, one would argue that they're
8:33just investing ahead of the monetization
8:36options.
8:36>> The first let's start with this
8:38adoption. Is it honest adoption when you
8:41are forced to use generative AI when you
8:43load Google? When you load Google Docs,
8:45Gemini screams in your ear. When you
8:47load Word, co-pilot's bugging you. When
8:50you use Amazon, whatever rofus AI is
8:52wants has opinions on what socks you're
8:54buying. This is the largest
8:57non-consensual push of technology in
8:59history. Chat GPD for example, every
9:01single media outlet has been screaming
9:03about this for 3 years. They've been
9:05saying, "This will take your job. You
9:07must use this. If you don't use this,
9:09you're going to be falling behind." So
9:11people are using it because they've been
9:13told to use it constantly and they're
9:14using it like search predominantly and
9:16that's partly because Google fell behind
9:18search and also because it's better at
9:20ingesting queries sometimes. Sometimes
9:21if you use a generative search it's like
9:23a trolling vessel. It's not very good at
9:25specifics but if you're like does this
9:27thing exist? Has this person ever said
9:29anything like this? It'll still probably
9:30get it wrong but it'll scour the ocean
9:32for you. Nevertheless, that's not worth
9:34a trillion dollars. None of it is. The
9:37amount of money being sunk into this is
9:40just incomparable to anything. Railways,
9:42it blows everything out of the water
9:44because there is no postbubble story
9:48even for this. AIG GPU is not useful for
9:50other things either. There's it's a
9:53directionless egregor of capitalism.
9:56this headless beast that lumbers around
9:59desperate to seek out growth everywhere
10:01in the hopes that if it harasses people
10:03and scares people and demonizes labor
10:07enough, people will be forced to use it.
10:10>> The the reason I I pause is because I
10:13just I think about my own company.
10:14Obviously, everybody thinks about their
10:15own personal situation. So, you have
10:16people listening now that don't use any
10:17AI tools. Then you'll have people that
10:20are using it for everything from coding
10:22new software tools to everything they
10:24write to, you know, images, whatever.
10:26And when you look at the the stats
10:27around enterprise adoption, it says 88%
10:29of organizations regularly use AI at
10:31least once for one particular business
10:34function. And I'd say in our company,
10:3695% of people use a one of these AI
10:40tools like anthropical chatbt or Gemini
10:42every day,
10:43>> right? And that exists on some kind of
10:44spectrum of like the super users that
10:47are using it probably, you know, every
10:49hour of every day for almost everything
10:51to, you know, someone maybe hiring the
10:54executive team that's using it less
10:55because their job doesn't require of it
10:57as much,
10:57>> right?
10:58>> And when you look out into the world,
11:00you know, at how the world is changing
11:03from a content perspective, if we're
11:04looking at generative AI, it is obvious
11:06that these tools are being widely
11:09adopted. Part of the symptom is the AI
11:10slop you see all over the internet,
11:12>> right?
11:13So, I I don't know this this this idea
11:16that it's not being used. I struggle
11:19with
11:19>> it's being used. Here's the thing with
11:21the slop. Before we had AI slop, we had
11:24SEO slop because Google incentivized
11:27doing the lowest common denominator that
11:28would rank well in search. There's a
11:30whole story about how they pulled back
11:32spam guards thanks to Bravagar Ragavan,
11:33which we can get into,
11:35>> where they made the internet worse by
11:37allowing worse content to rank higher.
11:39It's why we have when you used to
11:41Google, oh, best washing machine,
11:43there's 11 different horrible blogs that
11:45read like somebody got a concussion.
11:47They are built to rank rather than be
11:49read by humans or built to be good made
11:52good. So AI helps weaponize that at
11:54scale. Yeah, you can make a bunch of
11:56generic slop. We've had slop for years.
11:59We've just found a slop machine. But
12:01then also there's the problem of cost.
12:03So when you use AI services, you burn
12:05tokens and it's per million tokens. So
12:07>> what's a token? So it's around 3/4 of a
12:10word. So it's characters.
12:12>> So the AI companies have a currency in
12:14which they charge you. Like a taxi in
12:16New York has a meter.
12:17>> Yeah.
12:18>> And they call it tokens.
12:20>> Yeah.
12:20>> And every word, let's just say for ease
12:22it's a word. You're paying per word.
12:24>> About a word. Yeah. And it's per million
12:26tokens. So you'll be charged per million
12:28input tokens. The stuff you feed into it
12:30like a document or a bunch a code base.
12:32And the output tokens are both the stuff
12:34it spits out at the end but also when it
12:36thinks. So, okay, you've asked me to
12:38give you the best restaurants in this
12:40area of New York. I should find the best
12:41restaurants in New York. All of that's
12:43output tokens as well.
12:44>> However, when you're paying for a
12:46monthly service, you don't see any of
12:48that. Put all that crap to the side.
12:50They just have rate limits. So, you can
12:51use them a certain amount and then when
12:53you run out, but they kind of offiscate
12:54what that was. Now, someone recently
12:57found, semi analysis actually found
12:58this, a big analyst group. They found
13:00that on a $200 a month chat GPD
13:03subscription, you can burn $14,000
13:06worth of tokens and on anthropics you
13:09can burn $8,000 for 200 bucks. That is
13:13how most and even on the 20 buck a month
13:15service you can burn $400.
13:18Now most people don't realize that. Most
13:20people have no idea what AI costs. Most
13:22people just think, "Oh, it's 20 bucks a
13:24month." No. All of these companies run
13:26at a horrifying loss. OpenAI lost $20.9
13:29billion last year because people can
13:31burn as many tokens as they want. And
13:33when they tried to move everybody on the
13:36enterprise side, so companies bigger
13:37than 150 onto actually paying the cost
13:40of AI in around March of 2026, to quote
13:43Sam Orman, they said, uh, people have a
13:45big problem with it. I think it's a huge
13:47issue, which is not really what the air
13:50apparent text history is meant to be
13:52saying, but the point is enterprises
13:54immediately started freaking out. Uber
13:56burned through their entire annual token
13:58budget in three months. So suddenly
14:01after everyone saying AI is the most
14:02productive thing ever. It's amazing.
14:04It's changing everything. The moment
14:05people actually had to pay for it, they
14:06go, [snorts]
14:08I don't know actually. Um maybe it's
14:11obviously we all love it. It's all
14:13great, right? But it's costing too much.
14:15So we need to reduce the cost because
14:17people are just dumping stuff into it
14:19being like what do I do here and getting
14:21whatever the median is out because
14:23that's what these things do. they
14:24provide the median answer.
14:26>> So essentially, someone like me who's a
14:28power user of these tools,
14:30>> I could be costing Anthropic or OpenAI
14:34$1,000, but they're only charging me
14:36$100, let's say. So they are having to
14:38subsidize $900 of my usage because of
14:41the electricity costs and the costs at
14:43their data centers. And so your
14:45assertion here is that that is
14:46unsustainable.
14:47>> Yes. And just to be clear, they're
14:49probably not one for$1. It might be 30
14:51for. We don't we don't know. I think
14:53it's unprofitable. These companies don't
14:55disclose them even in their auditive
14:56financials. They play funny games with
14:57how they categorize things. But
14:59nevertheless, yes. And on top of that,
15:01the way that you stand up inference,
15:03which is the thing that creates the
15:05output within these data centers, you're
15:08not just saying, "Okay, turn the
15:09inference machine on. Let's go." You are
15:12standing up the GPUs necessary to take
15:14in the demand, and if you buy too much,
15:17you've wasted the money. You You have to
15:19pay for the hourly GPU use regardless.
15:22If you buy too few, your customers can't
15:23use it. They get pissed off at you. They
15:25cancel. They go with someone else. But
15:26nevertheless, yeah, they would get
15:27demand selling $20 or $40 for a dollar.
15:31And that's what these services do. And
15:33really, the simplest way to explain it
15:34is they were actually profitable if they
15:36were actually just they believed that
15:38these services were worthwhile and that
15:39they were worthy of the cost, they'd
15:41charge it. Regular people wouldn't be
15:43able to get a monthly subscription.
15:45They'd just be paying what it's worth,
15:47unless, of course, there was an economic
15:50problem. And it's very simple. You pay
15:53when you use an LLM regardless of
15:55whether you get what you want. When
15:56these things hallucinate, say you're
15:58doing something, you're coding something
15:59and they go through a code base and they
16:02up a bunch of stuff, they break a
16:03bunch of stuff, you're paying for that.
16:04You're paying for it whether it works or
16:06not, unless of course you're using one
16:07of these subscriptions. I think the the
16:09really interesting point is are they
16:13spending ahead of the value showing up
16:16which is I imagine what they would argue
16:19or are they spending all of this money
16:22and subsidizing all of their users in a
16:24way that's unsustainable and that will
16:26never be justified like does it you know
16:28because you think back through the
16:29history of technology you often get
16:31people
16:32losing money to grab market share
16:34>> right
16:35>> and they're also focusing on bringing
16:37the costs down and making it more
16:40profitable for them as well. But they
16:42can't afford to underinvest.
16:44>> If they were bringing the cost down,
16:46they would have brought the cost down,
16:47which they have not. It seems to be
16:49getting more expensive. In fact,
16:51everyone inference providers don't seem
16:52to be profitable. Even the companies
16:54renting out GPUs don't seem to be
16:56profitable. I imagine that it wasn't
16:59like they started out and they were
17:00like, "Shit, this is unprofitable at the
17:01beginning. We know it. Screw it. We'll
17:03keep doing it any screw." I don't think
17:05it's some big conspiracy. They probably
17:07thought at some point, yeah, this will
17:09go profitable. The chips will catch up.
17:12Customers will pay for the overwhelming
17:13value because you don't know in 2023
17:15where it's going to be in 2026. You
17:16assume it's going to go up. That's the
17:18nature of venture capital. They should
17:20have stopped in like 2024 when OpenAI
17:22lost over $5 billion. They should have
17:24been like, "Yep, this is not going to
17:26work." But they kept going because it
17:28helped number go up so much. It helped
17:30stock values pump. It helped everyone
17:32pump. It helped Nvidia pump, Microsoft,
17:34everyone. and not from the revenues.
17:37Because here's the funny thing about
17:39Google, Microsoft, and Amazon. People
17:41for years have been saying their AI bets
17:43have paid off. Wow, their AI bets have
17:45paid off. As these companies refused to
17:47say how much they're making from AI, but
17:49because their existing businesses
17:50continued to grow and did so, by the
17:52way, through price increases, changes to
17:55how Google and Meta uh did advertising.
17:58Amazon bumped up prices and changed how
18:00they did actually Amazon started a
18:01remarkable ad business during this whole
18:03time as well. and the selling through
18:05Amazon platform anyway nothing to do
18:06with AI but because number go up because
18:08revenue go up everyone went it's AI
18:11because these companies wouldn't spend a
18:12trillion dollars for for no reason right
18:15except in fiscal year 2026 which just
18:18ended for Microsoft annoying I know they
18:21made total according to Bloomberg about
18:23$34.33 billion $24.1 billion of that was
18:27from OpenAI so that leaves them with
18:29about $10 billion in a year when they
18:31spent 115 billion on capital
18:33expenditures just intend to spend 175
18:36billion next year. The math does not
18:39make sense. I imagine their plan was
18:41okay, this is just going to get
18:42exponentially more valuable and at some
18:44point the costs will be outpaced by the
18:46return. Problem is that large language
18:48models need a bunch of money to train
18:50them. They need constant data flow. They
18:52need customized data. It's just this big
18:54expensive monster. And when you try and
18:58talk to people about it and you try and
19:00say, "Hey, look, this is really bad.
19:02Nvidia has sold it was $215.9 billion in
19:07the last fiscal year worth of GPUs
19:08mostly. And you try and go, yeah, that's
19:11to support like $22 billion of revenue
19:15total in the entire world outside of
19:18these two companies that literally
19:19require money being fed into them
19:21sometimes by Nvidia to keep alive. When
19:24you tell people that, they go, "Well,
19:25companies just lose money, right?
19:26Companies because we have this quote
19:29Edson from Prophy Markets. We have this
19:31cult-like worship of the wealthy where
19:33we think that someone wouldn't spend all
19:34this money for no reason. Right? Because
19:36reconciling with that with this idea
19:39that the ultra wealthy, the ultra
19:42powerful didn't get there through big
19:44brains. They didn't get there through
19:47anything other than luck and opportunism
19:49and getting an MBA perhaps with the
19:51right people. That they just got there
19:53because they're regular people and they
19:55just happen to be in the right place at
19:56the right time. reconciling with that
19:57and realizing that the world is not
19:59controlled by people like a meritocracy
20:01is kind of grim. So it's easy to be like
20:03no they're not making a mistake I must
20:05be missing something and that's what
20:07they want. So you know I think back
20:09through the history of technological
20:11breakthroughs and I think about I mean
20:13you can look at different industries and
20:14one of my favorite books on this subject
20:15is the innovator's dilemma. not read it.
20:18>> And one of the things it talks about is
20:19how the the innovation that ends up
20:21taking out or transforming an industry
20:25often starts worse, doesn't make
20:28economic sense, none of your customers
20:30are asking for it. And this is typically
20:32why we end up ignoring it. So like
20:33you've got horse and carriages in the
20:351800s.
20:36>> Amazing form of transport according to
20:37the 1800s, you know, people of the
20:391800s. And then you have this thing
20:40called cars come along. Now the problem
20:42with cars is they broke down all the
20:43time. It's kind of like AI hallucinates
20:45now. um they were more expensive and the
20:47the economics of it didn't make sense.
20:49You might as well walk than buy a car.
20:50There was a law at the time that meant
20:52you had to walk in front of it with a
20:53red flag and wave and someone had you
20:55had to employ someone to walk in front
20:56of it waving a red flag. Obviously, it's
20:58worse. It's like a worse solution.
21:00However, these things that are
21:02disruptive innovations, they have a
21:04higher ceiling of growth and so they
21:07eventually overtake the horse. And I
21:09when I think about that analogy in the
21:11context of all of this, I go, okay, it's
21:13imperfect at the at the moment. the
21:15economic models aren't perfectly ironed
21:18out. They're still figuring out how to
21:19make it cheaper, the infrastructure,
21:21etc. But as if you think about the rate
21:23of improvement versus other you know
21:26let's say coding how much could I train
21:28a human coder to improve and to increase
21:31their output versus an AI agent one
21:33would go if you just imagine any rate of
21:35improvement in these AI tools at some
21:38point if you just imagine a 5% rate of
21:39improvement per month at some point it's
21:43you know and then you imagine a 5%
21:45reduction in cost which is what we did
21:46with the internet what we did with cars
21:48but Mo's law
21:49>> mos law is a mos law is not with GPUs.
21:52So let me let me actually explain. So
21:54Nvidia Nvidia invented I think it was in
21:56the 2000s they put out something called
21:58CUDA which is the underlying software
22:00library and the way to run software on
22:03GPUs. took them solid decade or more to
22:06make it something where they could do
22:07data analytics, one of the early things,
22:09mapper and such. And then when AI came
22:11along, they'd had lots of experience
22:13with it. But nevertheless, this company
22:15has got more money, more attention, more
22:18geniuses behind them, more people
22:20focused on making their things more
22:22efficient than anyone could ever ask
22:24for.
22:24>> And Nvidia, for anyone that doesn't
22:26know, makes the chips.
22:27>> They So, and that CUDA thing I
22:29mentioned, they were the ones with CUDA
22:30and CUDA allowed generative AI to grow.
22:32Okay, so they're chips.
22:34>> Chips and chips are needed. Those are
22:35the things that go into the data
22:36centers.
22:37>> And there specific chips are the ones
22:38where you can run AI software on it. So
22:40the training runs and also the
22:41inference. Now, here's the thing. The
22:44the car example back then you didn't
22:47have pretty much every mathematician and
22:49scientist going into the car industry.
22:51You didn't have the combined world's
22:52governments never shutting up about
22:54this. And by the way, giving them credit
22:57early since 2023, they've been saying
23:00this is inevitable. Even in what you
23:01said, 5% improvement. I don't even know
23:03how you'd measure that because a junior
23:05software engineer can still experience
23:08things and learn things from context,
23:10from how people deal with problems. And
23:12the way that people deal with problems
23:13is not as simple as looking at the code
23:15or reading some emails. It's context
23:17cues from speaking to a person. It's
23:19being in different environments. And
23:21there may there are uses for LLM's
23:23encoding. I don't dispute that. But even
23:26saying 5% uh what does that mean? Is it
23:28better at Rust? Is it better at C++?
23:30>> I'd say productivity just like yeah
23:32shipped. If we did it in the context of
23:34coding, it would be like shipped code.
23:36>> That's the thing that would be like he's
23:38the best writer in the world cuz his
23:39newsletter's really long. That's an
23:41insane way of evaluing it. With coding,
23:43it would be I mean it's even difficult
23:45to evaluate because it's is the software
23:48out there better is actually a great way
23:50of evaluating it. And I would say
23:51uniformly not. I would say the standard
23:54of software across Google, Microsoft,
23:56Amazon, Meta, especially God, Meta is a
23:59monstrosity, is worse. GitHub, GitHub,
24:02someone posted on Twitter earlier today,
24:04we should get a notification when GitHub
24:05is up rather than when it's down because
24:07that would be more reliable. Microsoft's
24:09one of the largest companies in the
24:10world, and they can barely wipe their
24:11own ass when it comes to GitHub. The
24:13quality of software is going down
24:15weirdly enough as more people use LLMs
24:18and more businesses demand and I really
24:20do mean demand that people use these
24:22services. So on this point of if we go
24:24back to this horse and carriage and car
24:26analogy say that we're at whatever point
24:29today if you imagine any rate of
24:31improvement in the technology which we
24:32have seen since tragedy came out
24:34>> I remember when tragy came out and I was
24:36in Asia and I was there showing it to my
24:37fiance I was like look it can do this
24:39and it was hallucinating once in a while
24:40and getting things wrong. I actually
24:42don't have that experience anymore. I
24:45have moments where I believe it's
24:47reasoning is weak, but I don't have
24:49outright hallucinations anymore. See
24:52that? I I disagree. So,
24:54>> give me an example of what you define as
24:55a hallucination.
24:56>> Okay, great one. So, I have a Bloomberg
24:57terminal. Yeah. The very useful thing
24:59they have on there is ask B. So, when
25:01you do a Bloomberg inquiry to like look
25:03up what we think Nvidia's revenue is
25:05going to be next quarter, it runs
25:07something called BQL, which is its own
25:08programming language. Now, instead of
25:10having to learn that, you can just type
25:13into RSB and it will generate it and run
25:14it for you. And so, you get it pulled up
25:16and you know where the data is coming
25:17from. It deals with hallucinations real
25:19well. The other day, I was like, you
25:20know what, get a little spicy. I'm going
25:22to look up the growth rate of stocks of
25:25Microsoft, Google, Meta, and Amazon over
25:28the course of 5 years, I think it was.
25:30>> And I was about to I was copy pasted it
25:32over to something looked at in Excel. I
25:34was about to was writing the newsletter.
25:35I went, Microsoft stocks never been $575
25:39a stock.
25:41You know what? When it's a cute little
25:42thing like, oh, it's a stock price and I
25:44kind of call it was no harm, no foul.
25:46That's fine. But when you're talking
25:48about, I don't know, like a transcribing
25:50tool for a doctor or a financial model
25:53that a hedge fund is dependent on, at
25:56that point it becomes a little more
25:57dangerous. And the thing is a
26:00hallucination with a software package.
26:03For example, you're refactoring a code
26:04base and it leaves a door open
26:06security-wise or it just breaks
26:08something and you I don't know maybe
26:10you've been vibe coding for 6 months.
26:12You haven't really been coding with your
26:13own hands for a while. Maybe you've
26:14forgotten a few things. You had this
26:16slop to look for. I'm not doing
26:18it. And so the problems become
26:21multiplicative. And I don't really know
26:23how you train them out of that. And
26:25they've certainly not succeeded. So on
26:28one hand they have got better but one of
26:31the main ways they evaluate them getting
26:32better are benchmarks that are adjusted
26:35specifically for large language models
26:37because you can't just have them do
26:39tasks. They've got better at that. They
26:41found some tasks they can have them do
26:42on them like meter me they have this
26:45thing where it's like check out this
26:46chart look how much better it's getting
26:48at running tasks. Wow it can go for an
26:50hour and then you look it's like yeah
26:51and successfully completing them 50% of
26:53the time. They they have a hallucination
26:55leaderboard and it really focuses on
26:57basic tasks and it shows that the
26:59four-year trend according to historical
27:00data from the Victaria hallucination
27:03leaderboard shows that hallucination
27:05rates on simple summarization tasks have
27:07plummeted from around 21% 21.8% 4 years
27:11ago down to 0.7%
27:14roughly on today's top frontier models
27:16like Gemini and Chat GPT. Again the
27:19point of nuance here is that these are
27:21on simple tasks which is kind of what
27:23I've experienced. I've experienced that
27:24on day-to-day things that hallucinates
27:26less again rate of improvement thinking.
27:28So if I just imagine the trajectory to
27:30continue there is going to become a time
27:33where hallucinations become rarer than
27:35they are today increasingly and also
27:38what I would say is when I think about
27:39other technologies there's two more
27:40points other technologies at their
27:42inception when they first came to the
27:43world like the internet also had
27:45technical difficulties. I remember
27:47growing up with dialup modems and I
27:49couldn't go on the phone at the same
27:51time as going on the internet. I'd have
27:52to stop Runescape upstairs to go on the
27:54phone. And you thought this is crap.
27:56This is technology crap. All the
27:57>> I I don't know, mate. I loved it.
27:59>> Yeah, I know. You It felt like magic.
28:01And then in hindsight, you go, "Wow, I
28:03now have Starink and 5G internet from my
28:05phone. It's unbelievable." You couldn't
28:07leave the house with internet before.
28:09And that's what I mean by the rate of
28:10improvement thinking. I'd say the last
28:12point is we often compare AI to
28:16perfection,
28:17>> right?
28:18>> Whereas that's not actually the
28:19alternative in the working world. Like
28:22if I wanted to do let's say a simple
28:24writing task, I should compare AI to my
28:27alternative alternative way of doing
28:29that simple writing task which is both
28:31measured in my time right and my ability
28:34to hallucinate as a person who doesn't
28:35know everything
28:37or if I'm hiring someone an intern who
28:40might also be prone to hallucination or
28:42have gaps in their knowledge.
28:44>> So it's not actually like we're
28:45comparing we should compare AI to
28:46perfection. It's AI to the other
28:48alternatives. And if someone
28:49hallucinates 0.7% of the time, but knows
28:52way more and is faster, maybe on a net
28:56basis, that's a good trade. Maybe I
28:58should use AI. So, let's start with an
29:00example. Someone I love dearly, Matt
29:02Hughes, my editor, lives out of
29:04Liverpool. Wonderful guy. I don't pay
29:06Matt Hughes because he knows everything.
29:09I pay him because he has incredible
29:11context and a ton of knowledge and he's
29:13willing to expand it and work with me
29:15and moral sport and he's a great editor,
29:18but he's also someone who gets into the
29:20guts of it and has the experiences of
29:21it. He's a decorated tech journalist and
29:24on top of that a wonderful loving being
29:26with empathy and joy in his heart for
29:29the stuff he loves and absolute
29:30venom for the people he hates. That's I
29:33can't get that from a large language
29:34model. But on top of that, I don't I
29:36push back on just the assumption there.
29:38>> When you say knows everything, what good
29:40is something that knows everything when
29:42it sometimes doesn't know anything when
29:43it's sometimes? And on the thing is, are
29:45you really paying an intern for
29:47something basic? Are you really going to
29:49them and saying, "Yeah, can you look up
29:51what the date is?" No, you're doing that
29:52on Google. Whatever the task is, you are
29:55trying to also train an intern. The
29:57point of an intern is to train them and
29:59turn them in, take them out of Pinocchio
30:01status,
30:02>> but it's also an intern learns. And in
30:04turn gets context and in turn learns
30:05your habits. Learns
30:06>> AI gets context and learns.
30:08>> No, it doesn't. It
30:09>> doesn't learn.
30:09>> I mean, it doesn't. The way it learns is
30:12you create a giant claw. MD file that it
30:14sometimes doesn't read, sometimes does
30:16read. You create a harness. You put it's
30:18like it's Pee-Wee's breakfast machine
30:20from PeeWee's Playhouse. You have to do
30:22all these controversies to mitigate the
30:24hallucinations. And even then at the
30:26end, how much effort have you put in?
30:28>> But so, okay, this is an extreme
30:29simplified example. If I went on my
30:31Claude now and said, "What's my dog? my
30:32dog's name.
30:33>> Uhhuh.
30:33>> It would know my dog's name.
30:35>> Jesus Christ. This this company raised
30:3795 billion.
30:38>> I'm saying I'm I'm using an extreme
30:40simplified example to show that it can
30:41remember things from the past.
30:43Obviously, it knows much more complex
30:44things as well, but I just use that as
30:46an example. So, we we we accept the fact
30:48that it can it does have memory of the
30:50past.
30:51>> It has files it can access that have
30:52stuff on it, but that's not the same as
30:55memory. And it's also just okay. So, it
30:57remembers your dog's name. It might
30:59remember your habits. It might be able
31:01to read things you've said before.
31:03>> Does it know your moods? Does it know
31:05what's going on in the world around it?
31:06Does it have good days and bad days? Is
31:08it there for you? Because it's just a
31:10text machine. And the thing is
31:12the intern example. An intern is
31:15something that can grow. It's something
31:16that you invest in. That's not something
31:18you do through feeding files and text to
31:20it. The way that we store memories
31:22ourselves, the way in which we acrue
31:24experiences is a a milerum of emotion
31:29and feelings and facts
31:30>> completely different. So I think there's
31:32two things here. There's the process in
31:34which something happens and then there's
31:35the output.
31:37>> So the process you're describing the
31:38process of how a human does memory,
31:40>> right?
31:41>> The way that an AI does memory is
31:43different. But the thing that people
31:45care about is there value in the output.
31:47I.e. You know, if I dump all of my files
31:50into Claude, I don't really care how it
31:52processes it as long as when I ask it,
31:54what's my revenue? It has the number.
31:56And one could say the same thing about
31:58training someone. You could say, you
31:59teach them, you put lots of effort into
32:00them. You give them lots of context. You
32:03you educate them and give them
32:04experiences. And then you might come and
32:06say to them, by the way, what's my
32:07revenue? Now, the processes are entirely
32:09different, but the outcome is what I
32:10care about. Do they know the revenue
32:12number when I ask them? And so, I think
32:14that's the part that we sometimes get
32:15lost. we get, you know, cuz I have I've
32:16heard this debate about like can AI be
32:18creative,
32:19>> right?
32:19>> I think like the way to answer that
32:21question is like it's about the output
32:23when I ask it to do a creative thing
32:25does it give me the answer not is the
32:27process the same as a human process cuz
32:30actually no who cares what the people
32:32care about they pay for the outcome the
32:34product.
32:34>> I actually disagree about the process
32:37because Matt Hughes for example
32:39>> your editor
32:40>> Yeah.
32:40>> Yeah. watching him go down a rabbit hole
32:43and being there with him and actually
32:44vice versa him doing the same thing. We
32:46wrote these well I mean we were working
32:48on the research I ended up sitting there
32:50for like the dayong session of writing
32:5311,000 words and he he had given me a
32:55bunch of notes. It was actually just
32:56even describing that process, I feel so
32:59happy cuz it was like us being like I
33:00can't believe how these Jesus
33:02Christ they can't do like just like the
33:04misanthropy of just the horrible cynical
33:07people of asset managers like Blackstone
33:09just learning about them and being like
33:10it can't be this and having a back and
33:12forth with him that is fundamentally
33:14different because we were both learning
33:16together and the learning process was as
33:18much about creating the output as the
33:20output itself. When you learn something,
33:22you're not creating the average, which
33:23really is what these things do, of the
33:26documents it could find. You're not
33:28getting particularly novel outputs. If I
33:31needed a generic slop output, sure, but
33:34I've I've used some of the higherend LLM
33:37harness machines that the hedge funds
33:39use, and they all give the same shite.
33:41It's all the same the same generic
33:43reports, the same, oh, we noticed this
33:45analysis, things that you can find on
33:46any kind of AI slop out there. what you
33:48described to me there, what I heard
33:50anyway is there's two points of value
33:51you're getting from your time with that.
33:53I mean, I mean, there's many more, but
33:54you said you're you're learning and then
33:57you're getting this book edited blog
33:59blog. You're getting a blog edited,
34:00which is the output, and you're getting
34:02learning and you're also really getting
34:03connection and all these other things.
34:05But when I come to when people sort of
34:06think about the value of AI, of course,
34:08they could use it to learn. But in the
34:10example I gave of like repeat my revenue
34:11number back to me or do this number, I I
34:13just care about the output. I could use
34:15it to learn. I could say what if the
34:16revenue number was wrong once you should
34:18have defined deterministic ways of
34:21knowing those numbers you should not
34:23rely on them even with the terminal
34:25running BQL which I trust I will double
34:27triple treble check everything just to
34:30be sure partly because also the process
34:32of learning for me I don't want just a
34:34report I go like that I want something
34:36that I fully understand and also
34:38understand the context around it I don't
34:41think that LLM do that and I just don't
34:43see them getting
34:45in a way that does that because it's
34:48it's just not what they do. And also
34:50there's the other problem of the more
34:51detailed the report, the more likely
34:53there are things to be wrong with it. If
34:54you are with Matt Hughes, for example, I
34:57can trust he's got it right. I can trust
34:59he understood and I can trust that I can
35:01have a back and forth with him that will
35:02inform me if I've missed something. I
35:04can read the stuff that he's read and
35:07actually trust him because there's a big
35:09trust part as well. What is the basis of
35:11your trust in Matt? Could it be his
35:14historical performance?
35:16>> I mean, yes.
35:17>> Okay.
35:17>> And also the fact we've learned half of
35:19this stuff together,
35:20>> but but tenure tenure doesn't
35:22necessarily There's probably people, you
35:23know, for 15 years who you also don't
35:25trust. Yes.
35:25>> So, I think I was trying to figure out
35:26like what is the what is the thing
35:28that's causing humans to trust another
35:29thing. And I guess it would be continual
35:31delivery of a commitment made of sorts.
35:34And so with Claude for example on simple
35:37tasks as we've seen from this
35:38hallucination leaderboard it continually
35:41delivers for people and that's why we've
35:43seen the fast
35:44>> I mean is that what that board says
35:45>> well it's it's saying like is it getting
35:47it wrong is it hallucinating
35:49>> simple task how are those defined
35:51>> I I don't know
35:52>> that's the thing though because this is
35:53actually a very very illustrative thing
35:55of the AI industry they are the what
35:58aboutist masters they have like well
36:00look we got this we got this benchmark
36:02that says we're good at this and look
36:03the numbers higher What's the number
36:05mean? No. What does that mean? And I'm
36:08not using this as a critic against you.
36:09It's
36:10>> when you can't give a direct answer, you
36:12give a side answer. When you as the LLM
36:14industry want to prove your worth, you
36:17can't just be like just use the product.
36:18When the first iPhone came out, go was
36:20Penn State at the time. Oh, I felt like
36:23the uh apes at the beginning of 2001.
36:25official voicemail. It was
36:27immediate. And I showed it to tech
36:29friends. I showed it to the most normal
36:31people in the world. And everyone was
36:32like, "Holy this is They were on
36:34razors. They were on Nokia 3210s. It was
36:37obvious the value." Amazon Web Services,
36:38same deal.
36:39>> It wasn't obvious though.
36:40>> Yes, it was. I mean, I bought it
36:42>> to you. To you, it was.
36:43>> It was. And I also showed it to a bunch
36:45of people because I'm aware that I had
36:46bias when I just love gadgets.
36:48>> But but I remember the famous Steve
36:50Balmer who was the CEO of Microsoft
36:52interview where he was told about the
36:54iPhone and he bursts out laughing.
36:59[laughter]
37:00$500 fully subsidized with a plan. I
37:03said that is the most expensive phone in
37:06the world and it doesn't appeal to
37:07business customers because it doesn't
37:09have a keyboard which makes it not a
37:11very good email machine. You can get a
37:14Motorola Q phone now for $99. It's a
37:17very capable machine. It'll do music.
37:20It'll do internet. It'll do email. It'll
37:22do instant messaging. So, I I kind of
37:25look at that and I say, "Well, I like
37:27our strategy. I like it a lot.
37:30>> He burst out laughing, mocking it
37:32because it was so disruptive. It was way
37:34more expensive
37:35>> and it was way different. No keyboard.
37:37>> Well, phones used to be insanely
37:39expensive and the carriers would cover
37:40them, but you had to sign a long
37:41contract. You were still spending 500
37:43bucks. But the thing I'm getting at is
37:44you didn't have to explain to someone
37:46why perhaps you'd have to get past the
37:48cost part, but you could just be like,
37:49"Look how good this is." And then once
37:51the app was the iPhone 3G with the App
37:52Store, people were like, "Oh this
37:54could actually change things." mobile
37:56web. Even though it was a monstrosity,
37:58it was so bad at first. Even then, you
38:00could get your emails and you could just
38:01look at them. Point is, Blackberries
38:02were also expensive and were still
38:04actually kind of cool, but the way they
38:06worked was not like consumer software.
38:07They didn't have the classic GUI.
38:09iPhones felt like that. It felt like an
38:11a cell phone designed even like a
38:14computer. It was obvious. It was obvious
38:16from the beginning. Everyone I was I was
38:18dating a girl in the center of
38:19Pennsylvania at the time and everyone I
38:20showed it to was like, "Wow, this is
38:21incredible." That to me is the obvious
38:24thing with AI to this day when you're
38:27like, "Okay, why is it so amazing?"
38:28People still dither. People are still
38:30like, "Yeah, you can't run a business
38:32fully with it without this weird system
38:35of pulleys and levers and such."
38:37>> But how come then when you look at the
38:39stats around ChachiBT's growth,
38:42>> 100 million active users in just the
38:45first 60 days after launching? For
38:47comparison, Tik Tok took 9 months.
38:48Instagram took 2.5 years. And the
38:50internet itself for the worldwide web
38:51took roughly 7 years to reach that
38:53scale. Over 60% of the US adults are
38:56integrated into AI tools in their daily
38:58and regular routines within 3 years of
39:00the launch, reaching a 40% of the
39:02population. And that same milestone took
39:05the internet 5 years and personal
39:06computers nearly 12.
39:08>> Okay. So like this is the I think this
39:10is the part that's giving me dissonance
39:11is like when I showed my fiance chachi
39:14okay it was didn't [clears throat]
39:14really work
39:15>> but as a sole entrepreneur who English
39:18isn't her first language
39:20>> who has to write lots of text lots of
39:22copy and generate lots of images and was
39:23paying a graphic designer to help her
39:24make um certain images that she you know
39:27couldn't make herself because she
39:28doesn't have the skills.
39:30>> She would describe it as being
39:32transformative for her business. What
39:35I'm hearing from you is that it's not
39:37transformative and there's no value in
39:38it for people. But she if she was sat
39:40here transformative,
39:42would she pay the per million token
39:44rate? Would she pay the actual rate? Cuz
39:46that's the thing. If this was sold at
39:48its honest cost. Yeah.
39:49>> I would actually if and people were
39:50reacting like that and they were paying
39:5223 $4 every time they did something and
39:54they were genuinely happy. That might be
39:55an argument.
39:56>> What is the what would be the honest
39:57cost if they weren't sub
39:58>> the actual per million token cost? The
40:00actual API cost they should char.
40:02>> Do you know how much that is relative to
40:04God? Depends on it depends on the model.
40:06But there's actually kind of a point I
40:09want to make about the thing you said
40:10with the internet earlier. So when I
40:12first got on the internet 33.4 kilobits
40:14a second modem even back then I was like
40:17if this was faster and that was
40:20like immediate just like if this was
40:21faster cuz it was slow. You go on like
40:23happy puppy or something download take
40:25all bloody day waiting for share word to
40:27download immediately like if I could do
40:29this faster it would be better. And even
40:30back then I'm like, man, you could
40:32probably do video camera stuff with this
40:34stuff that eventually happened. And
40:35actually, there's this guy called Jim
40:36Cavell from Goldman Sachs in a report he
40:39did in 2024 that was geni too much spend
40:41for not enough return. Paraphrasing
40:43there. And he made the point that in the
40:44run-up to the iPhone, there was
40:47thousands of presentations that when GSM
40:49radios get smaller, when Bluetooth
40:50radios get smaller, when Wi-Fi radios
40:52get smaller, it is inevitable that we
40:55will get something like this. And then
40:57he said that there is no such path for
40:59AI. There was no road map to AI becoming
41:03this thing that they promised. And I
41:04must be clear, if these companies had
41:06gone out there and are like, "Yeah, this
41:08is interesting cloud software. It's
41:10generative. It's really expensive. We're
41:12not sure if we can fully not trust it.
41:15Not in the I'm scared way. I mean, just
41:16like we're not sure that this is going
41:18to be a disruptive world changing thing.
41:21It has potential, but we're going to go
41:23slow. It's really expensive. This is an
41:25R&D effort. We're not going to expose
41:26consumers to it." and actually being
41:28like called them like I don't know
41:30language models and no no generative AI
41:32stuff just being not even call it
41:34because it isn't AI it's not autonomous
41:36it's not smart I actually might respect
41:38it but this is not they've gone out
41:40there since 2023 and said it was 2022
41:43this is the best thing since sliced
41:44bread this is changing everything this
41:46is going to do all your work this is
41:48going to take your job you're going to
41:50talk to Bing and it's going to tell you
41:51to leave your wife all of these crazy
41:52things and what's funny is when the
41:55writer uh Kevin Roose I think it was
41:58He was speaking to Kevin Scott, the CTO
41:59of Microsoft, about it. And Kevin Scott
42:01goes, you know, I'm just glad we're
42:02having this conversation. Instead of
42:04being like, "Settle down, Beas. It's a
42:06website. The website told you something.
42:08It's just LLM." They talked it up. And
42:10that's because everyone is talking about
42:12what they wish this was. Rather than
42:14talking about what it can actually do.
42:16This makes it scary to people
42:18deliberately. So, it makes it
42:20environmentally destructive. Look at the
42:21gas turbines poisoning black
42:22neighborhoods. I think it's in
42:24Louisiana. It's one of Musk's data
42:25centers. Look at the incredible energy
42:28draws. It is raising power bills and
42:30also it is creating inflation across all
42:33consumer electronics because of the
42:35massive RAM.
42:36>> You know what's interesting? I almost
42:37feel like so much of what you're saying
42:40is true and also it can be true that
42:45this technology is going to profoundly
42:47change the world. And I think like you
42:49know I think back to the early days of
42:51the internet is maybe the closest
42:52analogy we have of you know in the com
42:55bubble. you know, you wrote this great
42:56essay.
42:57>> Yes. Yes.
42:57>> Which I found really funny um especially
43:00the name the rot economy and you talked
43:02about the rotcom bubble.
43:04>> Yes.
43:05>> Talking about how AI is of less value
43:07than people think.
43:09>> And in that in the sort of com bubble,
43:11what you saw is huge hype, people
43:13overselling the capabilities of their
43:15websites and what they were building.
43:17But in the wake of the dotcom bubble,
43:21yes, 90% of stuff went to zero,
43:23>> but you had generational companies born
43:26that changed the world,
43:27>> right?
43:28>> And so I I do I kind of and that's what
43:30bubbles do, right? Huge hype,
43:32overinvestment, investors get crazy,
43:34delusional. They think it's everything's
43:36going to change. At the same time, you
43:39do have skeptics
43:40>> in these moments. The the dot bubble had
43:42I mean the internet itself had the
43:44biggest skeptics in 1998. Nobel Prize
43:46winning economist Paul Krugman said by
43:502005 or so it will become clear that the
43:52internet's impact on the economy has
43:54been no greater than the fax machine. In
43:561995 astrophysicist Clifford stool
43:59famously I wrote about this in my book
44:01wrote famously in Newsweek. Do our
44:04computer pundits lack all common sense?
44:06The truth is no online database will
44:08replace your daily newspaper. No CDROM
44:11can take the place of a competent
44:12teacher. Commerce and businesses will
44:14shift from offices and malls to networks
44:16and modems. Bologoney. So, how come my
44:19local mall does a roaring business and
44:22the cyber mall gets zero business? And
44:24then I'll give you one more from
44:26Krueger, who was the award-winning
44:28economist. He said, "The growth of the
44:30internet will slow drastically as it
44:32becomes apparent most people have
44:33nothing to say to each other."
44:36That's that that that may actually be
44:38the worst one of those predict like hang
44:41around any bar in middle America.
44:43Honestly, the best conversation,
44:44>> but it's just all the same thing.
44:45>> I actually So, Clifford Stall actually
44:47his piece was interesting cuz that there
44:49were some boner points in it, but he
44:50made points about how like an
44:52overwhelming amount of bad information
44:53out there is bad for society. He's
44:54completely right saying how online
44:56education would not be a great
44:58replacement for regular education. I
45:00think we've seen that. But there is an
45:02economic difference that's vastly it's
45:05just completely different. So.com bubble
45:07was actually two bubbles. There was the
45:08website bubble which was just trash on
45:10trash on trash. It was just like I think
45:13what was it? Excite at home bought a
45:15eury incard company for like a billion
45:17dollars. It was insane crap happening
45:19that was so small. The big thing that
45:22people are thinking about is the dark
45:24fiber.
45:24>> Dark fiber. dark fiber was all of the
45:27wires that put in the ground thinking
45:28we're going to have all this demand for
45:30internet and it turned out that demand
45:32for internet I think the analyst
45:35estimate was it was doubling every 90
45:37days when it was doing that every 6 to
45:3812 months maybe maybe longer and just
45:41thus there was a massive overbuild of
45:43fiber optic cable and indeed the
45:46transmission stations and such just
45:48simplifying to bring that to people's
45:49houses and there was the assumption that
45:52well that would all get lit up and
45:53people would want it immediately didn't
45:54really
45:55Now the post.com bubble thing people say
45:57is well but after that there was demand
45:59from the internet. That's the thing
46:01though that's very different to demand
46:03for generative AI. Right now the demand
46:06we have for generative AI is
46:07predominantly subsidized. Just let's
46:09start there.
46:10>> Yeah
46:10>> predominantly subsidized and most people
46:12experience it are not paying the real
46:14cost.
46:14>> I agree.
46:15>> On top of that we already have all of
46:18the possible marketing in the world. We
46:20have the largest, most disingenuous
46:22marketing campaign in the history of
46:24man, pushing this up the hill. We have
46:27the apex predator of cloud software,
46:30Microsoft. They can only get singledigit
46:32billions from selling AI software. And
46:35Christ almighty, outside of OpenAI and
46:37Anthropic, we barely get $22 billion.
46:40And the thing is, $22 billion is a large
46:42amount to you and me. It's not a large
46:44amount of money when you spent a
46:45trillion plus dollars. When you have
46:47anthropic and open AI with $1.1 trillion
46:50worth of cloud commitments and on top of
46:52that, how does this turn into a post.com
46:54bubble thing? A data center built today
46:57is going to be as expensive to run in
46:592050 as it is today unless there's some
47:01breakthrough in electricity. But again,
47:04that's not happening with AI. AI is not
47:06doing that unless there's some
47:07breakthrough in GPU technology. But we
47:09already have Broadcom, Nvidia, etched.
47:12We have every major chip company ARM
47:15trying to do something about this. And
47:17no one seems to magically be able to
47:18make this profitable or indeed even less
47:21costly. Even Nvidia with Vera Rubin,
47:24their more expensive new GPU system.
47:26Even then, they're like, "Yeah, 10x more
47:28efficient. It's uh more dollars per
47:30megawatt." They're all koi about it.
47:32They don't just say, "Yeah, we worked
47:33with OpenAI and Anthropic and we found
47:35it reduced our cost by 50%." Easiest
47:37thing in the world if it was true. And
47:38that's because it's not happening. And
47:41this isn't a case where
47:42>> So are you saying there's not going to
47:43be the demand for let's say let's you
47:46know there's different types of AI
47:48generative AI we
47:49>> Yeah. And actually that's a good point
47:50to make. The reason they use the term
47:52artificial intelligence is so everyone
47:54would lump everything into it.
47:56>> They [clears throat] would lump uh
47:57protein folding nothing to do with LLMs.
47:59Robotics not LLM.
48:01>> Autonomous weapons even horrible as they
48:02are not LLMs because you couldn't trust
48:04them. But they've mushed everything into
48:06AI so that when you say, "Well, AI
48:09can't," they'll go, "Um, um, sir, you
48:12forgot to give us homework and also AI
48:14it's working on curing cancer." When
48:15it's just like, "No, that's not LLM.
48:17Stop giving them credit."
48:18>> The similarity though is they all need
48:20GPUs, all these.
48:21>> And that's the funny thing. All those
48:23data centers that we're building, all of
48:25them are for just generative AI. They're
48:28not for all of the other stuff. They're
48:30not for the cool AI has been
48:32around for a long time. Google. A lot of
48:35the good stuff that comes out of Google
48:36from the search side is AI but
48:38pre-generative.
48:39>> How would you run the the type of AI
48:42that sits in a robot? Let's say one of
48:44the Optimus robots if you didn't have a
48:46GPU.
48:47>> So Matic Matic has this cleaning robot
48:49for example. That thing is not got a
48:51little GPU in it. What it has and may
48:54indeed have used some GPUs but no year
48:57as many as they need for generative AI
48:59to run the data feed training data into
49:01it so it's able to clean a house. But
49:03when the little buggers going around
49:04cleaning my floor, turdsly I call him,
49:06it goes around mopping my floor, it's
49:08not like burning money the whole time.
49:10But when it comes to these massive
49:12amount of data center, sighteline
49:13climate said in February there's 190
49:15gawatts of data centers under in
49:17planning. Don't know about under
49:19construction that works out if about 12
49:21million megawatt that's what like $1.6
49:23trillion to3 trillion a year in annual
49:26demand you'd need for that. We don't
49:27even have $130 billion worth of annual
49:30demand. And people say, well, it will
49:31grow. how when most of the demand is
49:33coming from Amazon feeding money to open
49:36AAI or anthropic, Microsoft feeding
49:38money to OpenAI and Anthropic, Google
49:40feeding money to Open AI and anthrop
49:42well hasn't fed it to Open AI yet, but
49:44they're a pretty big customer, billions
49:46of dollars. The conside is that we are
49:49building these effiges to capitalism,
49:51these giant GPU data centers, and people
49:53are being told, well, it's for AI, you
49:56know, the thing that's done all this
49:57other stuff that's unrelated. Or the
49:59worst thing I've seen is like, oh, you
50:01don't like you like online banking.
50:02Well, you do like data centers. There's
50:04a big difference between a data center
50:05for regular nonGPU compute for standing
50:08up a server, a content delivery system
50:10like Akami or something that brings the
50:12website to you or how Meta runs
50:14Facebook. That is not the same. It takes
50:16way less power, mostly CPUdriven
50:19compared to these giant GPU data centers
50:21that offer one thing, one thing only.
50:23>> But I was doing the the research and
50:25looking at some of these notes here. It
50:27does say that for tougher types of AI
50:29systems designed to solve concrete
50:30physics, biology, and spatial problems,
50:32they require some of the most intense
50:34data center infrastructure on the
50:36planet.
50:36>> Yeah.
50:37>> AI systems like Deep Mind's AlphaFold,
50:39the protein folding company
50:41>> used for genomic sequencing and climate
50:44forecasting, etc. run on high
50:45performance computing clusters. These
50:47require immense precision and continuous
50:49heavy computing data centers.
50:51>> Yeah. Training the brains for
50:52self-driving cars requires billions of
50:54miles of simulated physics environments.
50:58The AI isn't generating text. It's
51:00learning to navigate 3D spaces and
51:03gravity and relies on data centers,
51:04>> right? And the thing is those data
51:06centers, they might have GPUs in them.
51:08We had GPUs used for this HPC, the high
51:12performance computing before generative
51:14AI. And yeah, that's how AI has been
51:16trained before. That's how Tesla did.
51:18believe they've had their own data
51:19centers when it comes to training the
51:21autopilot system for better or for
51:22worse. That's how we've done it before.
51:24Again, that is not why we're building
51:26these data centers. These data centers
51:28are being built to sell to AI generative
51:31AI companies to either train systems or
51:33run inference. These things are being
51:36built in this brainless way where it's
51:39just well actually maybe this is a good
51:41way of illustrating the con because
51:43everyone saw Google, Microsoft, Amazon
51:47and Meta give Nvidia over call it 800
51:52something billion dollars
51:54because everyone saw that they went well
51:56they wouldn't do that for no reason.
51:57They went we got to build more of these
51:58things. There must be all this demand.
52:00Even though the demand 70% or more of
52:04all that demand comes from these two
52:05companies who were funded by these three
52:07companies and that's the funny thing.
52:10The reason that they don't want to break
52:11out their AI revenues is because it will
52:14become alarmingly obvious that this was
52:16the case. It turns out that the only
52:18real big customers cuz it's not like
52:21they're building a few data centers.
52:22They're building trillion plus revenue
52:25potential. They believe they'll get
52:27speculative. It's entirely speculative.
52:29They're building it because they saw the
52:31biggest companies in the world buy a
52:32bunch of GPUs and they said, "I want in
52:34on that." They must have diverse
52:36customers, right? They wouldn't just
52:37have two unprofitable fail sons that
52:40they're propping up with. Christ,
52:42they've raised $217 billion just in
52:442026.
52:47>> So, we know that some of the biggest
52:49companies in the world are using AI,
52:51generative AI to write a lot of their
52:52code.
52:53>> Mhm.
52:53>> That is a great productivity gain for
52:55those companies, right? I mean, have you
52:58used Google or Facebook or Instagram or
53:00GitHub recently because they are
53:03catastrophically worse? Amazon Web
53:04Services went down multiple times
53:06because of their AI coding tool. How
53:08>> how is how is Google worse?
53:10>> Well, I'll tell the story of a real
53:11guy called Preaggo Ragavan.
53:13Previously, one of the heads of ads at
53:15Google in 2019, Google called something
53:17called a code yellow, which is when they
53:19said, "We've got a problem." And it was
53:21material weakness in query numbers which
53:24means the amount of times that people
53:26were searching on Google search. Guy
53:28called Ben Gomes internal at Google then
53:29the head of Google search says wait a
53:32minute to increase this number of using
53:33Google more.
53:34>> Mhm. We're going to have to I mean you
53:37what you're suggesting would mean we
53:38give worse answers because if someone
53:40got the answer quickly that would reduce
53:41the amount of queries right and people
53:44at Google Shashi Tako was another
53:46engineer was saying yeah can we please
53:47tell Sunda this because this doesn't
53:49seem good. We can't just increase the
53:52amount of queries. That would just mean
53:53that people would have to search more
53:54which would make the product worse.
53:56>> But but it would make them more money.
53:57You saying you'd show them more ads. So
54:00if you're spending more time on Google
54:02because Google's work,
54:03>> but is this linked to AI doing code?
54:04>> Oh, I'll get there. So
54:07>> this is the problem is is that this guy
54:09called Pragar Ragavan who's the head of
54:11ads at the time was pushing pushing and
54:13saying, "No, we need to make more
54:14queries happen. Got to make it happen."
54:16and Nick Fox who was there as well I
54:17believe was actually taking over Google
54:18search got to make them go up this is
54:20our new reality sometime in early 2020
54:23propagar ragavan takes over Google
54:25search from then and this is this is
54:28what I believe can't prove it if you go
54:30and look around the various SEO sites
54:32such journal and the various forums
54:34Google stripped back a lot of the
54:36suppression of spammy sites so that
54:38people would be on Google more and then
54:40over the course of time Google wanted to
54:43create more queries and Google search
54:45became much worse. It's why people
54:47always do like plus Reddit or from
54:49Reddit or what have you. It's because
54:50the actual underlying search results of
54:52Google had got worse. And then
54:53Generative AI came along and Praagar,
54:56wouldn't you know, it gets put to run
54:57part of Gemini. And Google also was
55:00having trouble getting people back on
55:02Google. And what did they think they'd
55:03do? Well, everyone's talking about
55:05this AI thing. We'll just put it right
55:07at the top so people have to stay at
55:09Google. And actually, they'll use it
55:10more because instead of searching
55:12websites and doing that annoying thing
55:13where they click away from Google,
55:15they'll just only use Google. Instead of
55:17generating answers, by which I mean
55:20giving you search results you click
55:21through, now Google is the answer. Is it
55:23right? God know. It might tell you to
55:24eat rocks, might eat poisonous
55:27mushrooms. Maybe it'll give you a little
55:28few links you could click through. But
55:30the ideal situation was that AI was the
55:33ultimate form of Google's evil which was
55:35>> But I'm saying here I'm saying here but
55:36that's not the fact that coders could
55:39code on Google that's made Google worse.
55:40That's human decisions have made it
55:42worse.
55:42>> Yes. And then there's the instability of
55:44Google's platform which is actually I
55:46should have probably led with that a
55:47problem across the whole tech industry.
55:49>> Okay. So you're saying that you're
55:50saying Google is going down more.
55:52>> Yes. Google is less stable. Google Docs
55:55is a bugfest right now and has been for
55:57a while. Google Sheets, same deal. And
55:59the thing is, you're right, I'm being a
56:01little unfair. This is everyone. It's
56:03the same with Microsoft. It's the same
56:04with Amazon. It's the same across.
56:05>> How do we quantify that outside of
56:07anecdotes? Like, is there a way to
56:09>> You're right. I mean, GitHub downtime is
56:11the best example. Amazon Web Services
56:13went down two or three times this year
56:15because of AI tools. And honestly,
56:18you're right. It is kind of hard to
56:20quantify outside of anecdotes. But I
56:22challenge anyone listening to this. Go
56:23and use a website these days and tell me
56:24how well it works. Tell me how buggy it
56:27is. Tell me how many problems even with
56:28my iPhone. The supposed best UX in town.
56:32Even the iPhone is a flipping mess these
56:34days.
56:35>> Okay, so the research says the short
56:39answer is yes. Tech downtime and
56:41software outages have demonstrabably
56:43increased over the last few years and
56:45industry data points directly to the
56:46explosion of AI assisted coding as a
56:48primary culprit. The problem is hitting
56:51the tech industry from two entirely
56:52different directions. The code itself is
56:54getting buggier and the sheer volume of
56:56AI activity is literally crashing the
56:59underlying infrastructure. Interesting.
57:01>> Yeah, that's because GitHub people are
57:03just writing a bunch of code, pushing
57:04it, and thus there's just more code on
57:07there.
57:08>> That's interesting.
57:09>> Yeah, it's it's a real mess as well
57:11because
57:12open source has had this problem as well
57:14because it's well-meaning people.
57:15They're like, I learned a bit of code
57:16with an LLM. I'm going to go out and do
57:18some stuff. I'm going to make this
57:19project better. And these people barely
57:21understand what they're shipping. Or
57:23maybe they understand a bit of code and
57:24they say, "Oh, Dunning Krueger, this
57:26I'm going to I'm just
57:28like, I can understand some of this."
57:29And now the code's all written and just
57:30push it right now. So GitHub is flooded
57:32with AI code.
57:33>> This sounds like it's making humans
57:36complacent.
57:37>> It is
57:37>> because we're going, "Okay, look, I let
57:39it write the the code for the last 100
57:41lines and it was broadly right. So the
57:44next 100 lines, I won't check them as
57:45much."
57:45>> Yeah. Yeah. And that's human nature is
57:48to get sort of to take shortcuts to
57:50spend less energy on an activity if you
57:52can right but the AI's still making the
57:55mistake and we're still making all the
57:56promises of AI that's the thing this
57:59thing is meant to be this autonomous per
58:01you say it can't be perfect I don't know
58:03based on what Samman has been saying for
58:05the last few years clammy Sammy has been
58:07promising the world saying this will
58:09replace software engineers Dario
58:10Ammedday Wario himself has been saying
58:13oh yeah 50% of white collar labor is
58:16going to go away in the next few years.
58:18These people are promising the world.
58:20Again, if they were saying it would be
58:22smaller and they were like, yeah, it
58:23does have issues and we must be none of
58:26this, oh, what if it wakes up and it's
58:28super powerful. Just like, yeah, it's
58:30probabilistic. It's going to make
58:32mistakes and if you don't know what
58:33you're doing, you don't really know what
58:34you're looking at, you're going to miss
58:36those mistakes and it's going to get
58:37multiplicatively worse as you go when
58:40you don't know what you're doing. So
58:41yeah, human nature is part of it, but so
58:44is the marketing. So are the promises.
58:47One of the smartest things a business
58:49can do is build like a bigger company
58:52without actually hiring like one. But
58:54the problem we all face is that most
58:56companies don't have every skill in
58:57house. So when I look at the businesses
58:59seeing real success today, the
59:01consistent pattern with all of them is
59:03how quickly they move. They bring in
59:05specialists with skills in emerging
59:06areas to keep themselves ahead. Even in
59:08our company, we spent the last year
59:10pulling in talent across areas like AI
59:12native strategy, no code builds, and
59:15product workflows. And we find this
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1:00:51my car that drives itself that is AI
1:00:53technology.
1:00:54>> Yes.
1:00:54>> I sat here with Dra from Uber and he was
1:00:57saying that I think in a couple of years
1:00:59time
1:01:01we won't need drivers um for Uber
1:01:04because the cars will drive themselves
1:01:06like they'll be fully autonomous.
1:01:08>> And I think if I'm not mistaken
1:01:11driving is one of the biggest
1:01:12professions on planet earth. So when you
1:01:14hear people when you hear these CEOs
1:01:16saying that there will be job disruption
1:01:19>> you say that they are not telling the
1:01:21truth.
1:01:22>> Yes. Or they're guessing in a way that's
1:01:24very good for them. Think about it from
1:01:26perspective of Microsoft Sachin Nadella.
1:01:28He's not going to be like yeah we don't
1:01:30know if this is going to work mate. Of
1:01:31course he's going to talk his book and
1:01:32he's going to say yeah this is going to
1:01:34replace all workers. It's going to be
1:01:36amazing. He it's going to be so
1:01:38powerful. And then he'll change his tune
1:01:39and say actually it's not going to
1:01:40replace workers. that make him more
1:01:41powerful because the things aren't
1:01:43catching up. Dor from Uber for example,
1:01:45of course he's going to say if this
1:01:47happens then that would be good for Uber
1:01:49because Uber would just become an
1:01:51autonomous taxi service. There's a
1:01:53reason that Whimo's taken I I find Whimo
1:01:56fascinating. I think that it's
1:01:57really cool. I think there are
1:01:57socioeconomic problems that will come
1:01:59from it. I think there are actual real
1:02:00problems that will emerge and also
1:02:02>> what kind of problems?
1:02:03>> Well, I mean socioeconomically there are
1:02:05like you said one of the largest
1:02:07employment centers in the world. I mean
1:02:09just the economics of cabs will fall
1:02:10apart but again we are nowhere nowhere
1:02:12nowhere near that. We're not even close.
1:02:14Whimo has had to do the smallest
1:02:16rollouts and the most control things
1:02:18because the problem with pretty much
1:02:19every AI system but especially driving
1:02:22is not the getting 95% of the way. It's
1:02:25those edge cases. It's raining which is
1:02:27a big problem for them in San Francisco.
1:02:29It's a kid runs across the road but
1:02:30they're wearing a high viz thing. Does
1:02:32it even notice it's a child? Again, this
1:02:34is a really interesting but very very
1:02:36applicable example of uh the right
1:02:38comparison to be made shouldn't be
1:02:40autonomous vehicles versus perfection.
1:02:42It should be autonomous vehicles versus
1:02:44human drivers. I mean, I don't know if I
1:02:46agree because a human driver might make
1:02:48mistakes, sure, but again, not an expert
1:02:51in autonomous cars. Just want to be
1:02:52clear. But if we're pushing autonomous
1:02:54cars out there willy-nilly and we're not
1:02:56doing so in extremely controlled
1:02:58environments, those edge cases will
1:03:00multiply and be dangerous. Yeah, they
1:03:02might be better at human drivers in some
1:03:04ways, but they might also I was in Vegas
1:03:05the other day and I was in a hotel and I
1:03:08watched a bunch of Zuk's cars just get
1:03:10stuck.
1:03:10>> They're autonomous cars.
1:03:12>> Yeah, they these weird boxy things. They
1:03:14just blocked the exit. They just all
1:03:16kind of lined up and just fell asleep. I
1:03:18saw the same thing actually happen
1:03:19outside of a hotel when I got out of a
1:03:21Whimo in San Francisco. Just stopped at
1:03:22the and then a bunch of cars and another
1:03:24Whimo got stuck behind it. And these are
1:03:26kind of
1:03:27>> I've seen some human bad drivers as
1:03:29well. I I agree, but it's just we have
1:03:31control over deploying these bad or good
1:03:34drivers. We have an ability to roll them
1:03:37out slowly, which is exactly what we
1:03:39should do. I'm not saying autonomous
1:03:41cars are bad. I'm saying we need to be
1:03:43so so so careful and treat them as
1:03:46guilty and pro till proven innocent
1:03:48because we can prove and also they have
1:03:50people overlooking them. They actually
1:03:52have people monitoring the roots. It is
1:03:53something they cannot rush out and it
1:03:55doesn't seem like they're rushing it,
1:03:56which is good. and they're not promising
1:03:58the world.
1:03:58>> I do agree. Listen, I I'm a big fan of a
1:04:01big fan of taxi drivers generally in
1:04:02part because I spend a lot of time in
1:04:03taxis and I think I'm not just getting
1:04:05in there because I want to get to from A
1:04:06to B. I'm getting in there for lots of
1:04:08other reasons.
1:04:08>> Yeah.
1:04:09>> However, when I look at the stats
1:04:11>> around what is more dangerous
1:04:14>> driving myself or having an autonomous
1:04:16vehicle drive me, there's an 68% lower
1:04:19overall crash involvement rate when
1:04:21you're in an an autonomous vehicle. Mhm.
1:04:23>> Autonomous vehicles experience roughly
1:04:252.1 police reported crashes per million
1:04:27miles compared to humans that are at
1:04:29roughly 4.68 per million miles. So, a
1:04:3255% reduction when you get in an
1:04:34autonomous vehicle. And autonomous
1:04:35vehicles show an 80 to 81% reduction in
1:04:38crashes resulting in injuries versus
1:04:41human drivers.
1:04:42>> Uhhuh.
1:04:42>> So, you're 85% less likely to be
1:04:45involved in a single vehicle crash like
1:04:47hitting a wall or a tree if you're an
1:04:49autonomous vehicle
1:04:51>> versus being driven by I agree. But
1:04:53>> so it's safer
1:04:55>> in also that data is what's the sample
1:04:58size of human drivers? I mean we've got
1:05:00many many many many many many more years
1:05:02of drivers and many many many more years
1:05:04of accidents and also man does that not
1:05:06have anything to do with generative AI.
1:05:08If we were just talking about that be
1:05:11having a different conversation.
1:05:12>> I guess the question here was really
1:05:13around job disruption. Like you know we
1:05:15we look across industries and we go
1:05:16driving is a massive profession. Is
1:05:18there going to be job disruption because
1:05:19cars can now drive themselves? If we
1:05:21think about white collar, you know,
1:05:22jobs, you know, lawyers and accountants,
1:05:25people sit here and they tell me that
1:05:27lawyers and accountants would the
1:05:29profession, right? I should say some of
1:05:31the skills within the profession will be
1:05:33relegated to AIS to do.
1:05:35>> Here's the thing. Lawyers, for example,
1:05:37great example. Always hearing
1:05:40legal partners talking about AI. Never
1:05:42the associates. The associates are the
1:05:44ones that go out and find the president.
1:05:46They're the ones that go and do the
1:05:47grunt work. They're the ones who are
1:05:48pulling motions half the time. The
1:05:50partner is the one that might be the
1:05:51litigant. It may be the client facing,
1:05:53but the ones that are actually doing the
1:05:54day-to-day work. I'm not hearing from
1:05:56them. I'm not hearing associates being
1:05:57like, "This is awesome." I'm
1:05:59hearing a bunch of well- paid people
1:06:02that have sat on Chat GPT and gone,
1:06:04"Yeah, yeah, I'm the greatest lawyer
1:06:06ever." They're not the ones that I want
1:06:07to hear from the actual workers. White
1:06:09collar labor disruption is not
1:06:11happening. Open AAI had a study that
1:06:13came out I think like a week ago that
1:06:15said there was no corre connection
1:06:17between spending on AI tokens and
1:06:18revenue per employee. Like this is open
1:06:21and that's
1:06:21>> what does that mean? Could you explain
1:06:22that to me?
1:06:23>> As in the more tokens you spend has no
1:06:25no correlation at all with the amount of
1:06:28money you make. It's the second report
1:06:30they've put out. The other one was like
1:06:31hallucinations are mathematically
1:06:33guaranteed kind of almost the one thing
1:06:35I respect about that company that
1:06:36occasion they just put out a study. It's
1:06:38like, yeah, kind of sucks.
1:06:40[clears throat] But the people that are
1:06:42having their lives disrupted work-wise
1:06:44are art directors. It's people, art
1:06:47directors, transcribers, translators,
1:06:49who have bosses that don't care about
1:06:51the output. It's what they consider
1:06:53cheap work. And the problem is is those
1:06:56people would have automated your work
1:06:57away anyway. They would have sold it.
1:06:58They would have taken the cheapest for
1:07:00they would have sold it to the global
1:07:01self. They would have taken the
1:07:02shittiest option they could. That is
1:07:04something that AI is doing. And again,
1:07:05those people are not paying the actual
1:07:07cost of AI. They're using a
1:07:08subscription. The actual white collar
1:07:11labor force might have some things that
1:07:15are slightly changing, but there is no
1:07:17evidence of like productivity gains. In
1:07:20fact, if there were, they would be
1:07:21screaming it from the rooftops. There
1:07:23was an Oxford economics study last year
1:07:25where it's like, oh, young people are
1:07:27finding less jobs because of AI. We
1:07:29actually read the study, which multiple
1:07:31journalists did not. It was a single
1:07:32line that said, "Yeah, we saw some
1:07:34correlation." Didn't give a number.
1:07:37Didn't actually say what the correlation
1:07:38was. We are so conditioned to believe
1:07:41that the rich and powerful know what
1:07:43they're doing that we internalize these
1:07:46narratives about like, well, previous
1:07:48booms lost a lot of money. Well,
1:07:49technology takes time to do stuff. And
1:07:51they are intentionally playing on those
1:07:54mythologies. They are playing on these
1:07:56knowing that journalists, analysts,
1:07:59investors will believe them. And this is
1:08:01partly because our our realities are
1:08:03defined by stock prices. Because the
1:08:05stock prices of these companies went up,
1:08:07we're like, "Oh, look, it must be
1:08:09working, right?"
1:08:10>> Both of those things you said were true,
1:08:11though, right? Like that previous
1:08:12technologies didn't make money at the
1:08:14start and you The other one you said was
1:08:16um they'll get better.
1:08:17>> But that's the thing. Okay. Because
1:08:19another thing got better, this will get
1:08:21better.
1:08:21>> No, but there's there's got to be
1:08:22something that they're saying that is
1:08:24fundamentally not true because those are
1:08:25two true statements that okay,
1:08:27technology often starts
1:08:28>> I know. I get what you mean. What they
1:08:30are fundamentally misleading people
1:08:32about is how possible it is. How many
1:08:34actual signs they have because they
1:08:35don't have the signs. If they had the
1:08:36signs as in the signs of this getting
1:08:38cheaper as in the signs of this being
1:08:40able to autonomously do work without the
1:08:42Rub Goldberg machine and even then in a
1:08:45reliable way that was making the
1:08:47customer more money being productive in
1:08:49a way you can say with your whole chest
1:08:51without a series of asterisks and that's
1:08:54how it is across the board. The people
1:08:56that are most excited about this,
1:08:59psychopaths on Twitter in many cases are
1:09:01people that I believe there really are
1:09:03some I'm sorry, there are some people on
1:09:05Twitter because the other thing about
1:09:07this is this is really unique to the AI
1:09:09industry. I've never seen it any other
1:09:11industry outside of maybe like sports
1:09:13teams. The attachment that some people
1:09:15online have to these companies. If you
1:09:17dare dare to criticize anthropic, it's
1:09:20almost this religious attachment. Good
1:09:23example was this week Bloomberg reported
1:09:25that OpenAI was on track to hit $40
1:09:27billion in annualized revenue. Month
1:09:29times 12, four weeks times 13, we don't
1:09:31know. They don't define it. I saw
1:09:33multiple people and I going actually
1:09:35it's 60 billion. It's actually 60
1:09:37billion. I heard from someone it is like
1:09:40a cult and it's a cult of software
1:09:42driven around growth and this idea that
1:09:45by backing the right horse you will have
1:09:48some grand thing and open AI in
1:09:51particular in particular Mr. Baltman
1:09:54they have been fermenting this that Tibo
1:09:56as well the Tibbo the one of the guys at
1:09:59uh OpenAI they ferment this thing online
1:10:01they build this kind of parasocial
1:10:03relationship with both the large
1:10:05language model themselves and the
1:10:07companies and one's allegiance to the
1:10:09companies is so important it's truly
1:10:12vile if only these people gave a
1:10:14about I don't know Medicare for all or
1:10:17poverty or thing like actual problems in
1:10:19the world versus are we buying enough
1:10:21GPUs Do you know what's interesting is
1:10:23some of what your narrative
1:10:26one would argue actually helps them.
1:10:29How? Because you know the AI doomers
1:10:31that have come here and told you know
1:10:32some of the original founding fathers of
1:10:34AI like Jeffrey Hinton have told me that
1:10:37what they're building is highly highly
1:10:38dangerous and that it will be
1:10:40fundamentally disruptive to society. And
1:10:43it's interesting because some of the
1:10:45CEOs who you've mentioned, their
1:10:46historical narrative was also, by the
1:10:48way, this is really dangerous
1:10:50and there is a significant chance it
1:10:51could f we could up the planet.
1:10:53>> And what we've seen is this slow pivot
1:10:55away from it because now they're getting
1:10:57booed and they're being attacked.
1:10:59There've been this slow pivot away from
1:11:00it. And the pivot almost sounds a little
1:11:04bit like your narrative.
1:11:05>> It now sounds like actually no, it's not
1:11:07going to change anything and you're all
1:11:08going to be fine. And it's now there's
1:11:10just not it's nah it's not dangerous at
1:11:11all.
1:11:12>> But that's the funny thing
1:11:13>> and that's why I'm saying like you're
1:11:14you're not they I actually think there
1:11:16might be a couple PR people at these big
1:11:18AI companies thinking thank god for Ed
1:11:22some [laughter] of it because you're
1:11:23like you're saying actually don't worry
1:11:25everything's going to be fine. It's not
1:11:26going to take your job. It's not going
1:11:27to disrupt the economy. It's just a fad.
1:11:28There's no technology. And I think they
1:11:30don't think that.
1:11:31>> Here's the thing. I think Alman and
1:11:33Amday are some of the most deeply
1:11:34corrupt and cynical people in the world.
1:11:36I don't think of course they were going
1:11:37to say from the it was early 2023 or man
1:11:40said we're a little bit scared about
1:11:41what we're creating. Oh, shut up. I'm
1:11:44just I hear that and I feel so
1:11:46frustrated because I've met so many of
1:11:48these rich liars, these people.
1:11:50And you know why he wants to say that?
1:11:52So you'll invest in his company and buy
1:11:54the software. So you'll be scared that
1:11:56if you don't use AI today, you'll be
1:11:57left behind in the future, which is
1:11:59their continual narrative that if you
1:12:01don't get on the train today,
1:12:03then you'll be left behind. By the way,
1:12:05every single scam and con starts with
1:12:07rushing you. Every single trick in
1:12:10history begins with saying you must do
1:12:12this now. And best piece of advice I
1:12:14ever got was if anyone tries to rush you
1:12:16and it's not literally a mortal thing
1:12:18like you are bleeding or on fire or the
1:12:19house is on fire, slow down. And yet all
1:12:22of these companies saying it's so scary.
1:12:24And now they're talking about slowdowns.
1:12:26But you ever noticed that Amade and
1:12:28Ortman, they say, "Oh, maybe we should
1:12:29slow down progress." And then they
1:12:31don't. Right now, Orman's saying, "Oh,
1:12:33we slow down progress because we're so
1:12:34delayed." No, they're out of compute.
1:12:36Now, they're doing it. I can guarantee
1:12:37you, by the way, their PR people do not
1:12:39like me. I know for I know I don't think
1:12:40OpenAI's PR people are super fond of me.
1:12:43>> But I bet there's elements of what
1:12:44you're saying because you're calming
1:12:46people. You You are theoretically
1:12:47calming down the general public.
1:12:49>> And you know what? I hope I am because
1:12:51>> the fear based tactics is horrible.
1:12:53These companies don't want that. These
1:12:54companies want people scared. I'm 100%
1:12:56sure.
1:12:57>> Uh I don't I just fundamentally
1:12:59disagree. I think it
1:13:00>> can I so the timelines there and I sit
1:13:02here and what I do is I log their quotes
1:13:04over time
1:13:05>> and I read them out from 2015
1:13:08>> to 2026 and the change you see is them
1:13:12going from there could be extinction
1:13:14that's the narrative the early narrative
1:13:16Elon said it himself he says it's the
1:13:17single most dangerous thing in
1:13:18>> Elon and then you track it over time and
1:13:21it evolves to this age of abundance
1:13:23we're all going to have unlimited stuff
1:13:25and then um the the new slogan at
1:13:28trackbt is intelligence for everyone.
1:13:30It's suddenly and all the and and
1:13:32whenever Daario comes out and says, "By
1:13:34the way, it's really dangerous."
1:13:35They attack Daario. Yeah. They hate him.
1:13:38>> That man [laughter] Daario is
1:13:40>> They're like, "Dario, shut the up."
1:13:41>> Honestly, I I've been saying Dario, shut
1:13:44the up for years. But it's But the
1:13:46thing is, I get your point where it's
1:13:47like I don't think they've changed to
1:13:49calm the public down so much as they're
1:13:51desperate to not get regulated, which is
1:13:53laughable. We don't regulate tech. We
1:13:55don't regulate America doesn't
1:13:57regulate We are in the We are
1:14:00still trapped in the hands of Milton
1:14:02Freriedman, Margaret Thatcher, and
1:14:04Ronald Reagan. We're still stuck
1:14:06in the neoliberalistic hellscape, which
1:14:09is growth at all cost, free market
1:14:11capitalism. So, no, no one's regulating
1:14:13the regulation of these companies should
1:14:15have been, I don't know, breaking up.
1:14:17Put these bastards to the side. Break up
1:14:19these for sure. We shouldn't
1:14:20have companies this big. It makes things
1:14:22worse.
1:14:22>> But these technologies are dangerous.
1:14:24>> I mean, they're dangerous, but not in
1:14:26the ways they've been warning about.
1:14:27Let's if we think about cyber hacking,
1:14:30>> right? And just to be clear, those cyber
1:14:32hacking things that happened were not a
1:14:33result of they were like break out of
1:14:35the sandbox and then they set the
1:14:36sandbox up wrong. They set up the server
1:14:39they were on wrong. But I mean, you
1:14:41know, advanced AI models could very
1:14:43easily cuz they can go out onto the open
1:14:45internet as agents. They could very
1:14:47easily go and look at code bases of
1:14:48different websites, find vulnerabilities
1:14:50and exploit those vulnerabilities.
1:14:52>> Yeah. in at scale and arguably um at a
1:14:56higher intelligence and faster and wider
1:14:59than humans a human hacker could
1:15:01theoretically. So that's dangerous.
1:15:02>> Well, here's the funny thing. We don't
1:15:05know how much compute was spent to do
1:15:07the hugging face attack, the open AI
1:15:09one. We also do know that they
1:15:10improperly set up the server to keep it
1:15:12in. They thought they'd turn the
1:15:13internet off and they didn't. That's
1:15:15human error. And that's human error in a
1:15:17sense that yeah, they threw about an
1:15:19indeterminately large amount of compute.
1:15:21This is dangerous, but people keep
1:15:23saying we can't let the the Chinese get
1:15:25a hold of these models. We couldn't
1:15:27possibly because what if these models
1:15:28fall into the wrong hands? They're
1:15:30already in the wrong hands. Mark
1:15:32Zuckerberg, Sam Olman, Dario Amade. The
1:15:35wrong hands are the hands of those who
1:15:37are running these companies. We should
1:15:39not be training these models to do these
1:15:41things. I don't know why the we're
1:15:43doing it other than they've run out of
1:15:45other things they can train on. There's
1:15:46a ton. And the fact that they can do it,
1:15:48it's kind of interesting. But you do
1:15:50would you agree that it's an
1:15:52intelligence and I'll call it that you
1:15:54know you might disagree with that
1:15:55terminology but an intelligence that can
1:15:57go out onto the internet and click
1:15:59around and take actions is inherently
1:16:03there's risks associated with that. Well
1:16:06the second part I agree with the risks
1:16:08we've had people running automated
1:16:10scripts hacking scripts for a while
1:16:11we've had hackers doing that for years
1:16:12and years and years. This is brute
1:16:14forcing it with a bunch of compute and
1:16:16yet it is dangerous. These companies are
1:16:18doing something dangerous. That is not
1:16:21what Jeffrey Hinton at have been warning
1:16:23about. They've been saying, "Oh, these
1:16:25things could destroy society. They could
1:16:26manipulate people." When you actually
1:16:28look at the underlying things, not so
1:16:29much. Jeffrey Hinton as well talking his
1:16:31book still got his Google stock, I
1:16:32think. And weirdly enough, he left
1:16:34Google because he was worried about the
1:16:35AI there, but then immediately made a
1:16:37comment being like, "Yeah, actually
1:16:39though, Google's very responsible."
1:16:40Strange thing. But let's get back to the
1:16:42the cyber security side. I agree this is
1:16:44dangerous. These people should not have
1:16:46access to so much comput. They clearly
1:16:47don't know what to do with it. There's a
1:16:49really easy way of dealing with this.
1:16:51It's not letting them use so much
1:16:52compute. It's regulating that part out
1:16:54of existence. What if the Chinese do it?
1:16:57The Chinese were able to distill the
1:16:58models. And also,
1:17:01I don't know, regulate it and stop I I
1:17:04feel like with this particular thing as
1:17:06well, we got to this point and let the
1:17:09genie out of the bottle to use an
1:17:11annoying Samman term. We let this happen
1:17:14because we let these companies be
1:17:15unregulated and use as much computers we
1:17:17want. We had these enablers
1:17:19allowing them to burn as much computers
1:17:20as they want. And also we for all of
1:17:24these dire warnings about AI dangers, no
1:17:26one seems to have done anything.
1:17:28>> Okay, we're going to play a game, Ed.
1:17:29>> Let's play it.
1:17:30>> On these cards here,
1:17:31>> I have the things that you consider to
1:17:33be myths about the AI industry.
1:17:37>> The challenge is I want you to give me
1:17:39one sentence.
1:17:40on each myth.
1:17:42>> Oh, Christ.
1:17:43>> So, just your first reaction. You're
1:17:44going to pick it up, you're going to
1:17:45read it,
1:17:45>> and then you're going to give me one
1:17:46sentence on your opinion of that
1:17:49>> um belief.
1:17:50>> Okay, let's go.
1:17:51>> So, let's do this.
1:17:56>> What does it say in your says the the AI
1:17:59industry is creating enormous economic
1:18:01growth?
1:18:02>> No, it's not. It's nowhere in the data.
1:18:05>> Okay. [laughter] Like, it's just May I
1:18:07do a second sentence?
1:18:08>> Go ahead. pretty much all of the
1:18:10economics is either Nvidia feeding money
1:18:12to it companies like Corewave or these
1:18:14three companies feeding money to these
1:18:16ones to spend it with the them.
1:18:18>> Okay. And what evidence do you have that
1:18:20there's it's not causing economic
1:18:22growth?
1:18:23>> Just to be clear, other than the spend
1:18:25on semiconductors, so the speculative
1:18:27investment in GPUs and data center
1:18:29infrastructure that's happening, but as
1:18:31far as like spend on AI goes, barely
1:18:33cracking hundred billion. And most of
1:18:35that is just these two running their
1:18:37services and paying these three
1:18:39companies, Oracle, Core, and others.
1:18:41>> But a hundred billion is a lot of money
1:18:43for a relatively new technology.
1:18:45>> Not when you've spent $300 billion in
1:18:47equity funding. And it if we're going
1:18:50with just these three, I think $600
1:18:52billion in capital expenditures.
1:18:53>> Yeah, I get that. That means it's not
1:18:55profitable. But the hundred billion is
1:18:57an expression of consumer demand
1:18:58>> when the compute is mostly driven by
1:19:00subscriptions that subsidized. No, it's
1:19:02not. When you're giving someone $20 or
1:19:04$40 for a dollar, they're going to use
1:19:06it more. If this was all on a per
1:19:08million token basis, we'd be having a
1:19:09different conversation.
1:19:10>> Okay, fair. Fine. Cool. Next one.
1:19:14>> The United States need to spend
1:19:15trillions to beat China in the AI race.
1:19:19Let's see.
1:19:21What AI race?
1:19:23That's actually That's actually my
1:19:25point. It's what AI race is there. Is it
1:19:27to make big scary LLMs? They they did
1:19:30that already without the Nvidia GPUs. By
1:19:32the way, they've got Blackwell GPUs.
1:19:34Kakashi and Jastario, two amazing
1:19:35analysts I love. They've been on this
1:19:37for years. It's like China's already had
1:19:40Nvidia GPUs that they're not meant to
1:19:41have for years. But also to do what?
1:19:43They already got the LMS. What What's
1:19:45the race to do? To make us spend more
1:19:47money than them? For us to constantly
1:19:48piss our pants worrying about China?
1:19:50Because u they won if that's the case.
1:19:53Myth number three, AI will replace all
1:19:56human jobs.
1:19:58that just isn't happening and there's no
1:20:00economic data to support it.
1:20:02>> Will it replace some jobs?
1:20:04>> I mean, it's replaced some contract
1:20:06labor that would otherwise be replaced
1:20:07with cheap labor out in the global
1:20:09south. It's a digital globalization in
1:20:11that sense, but all jobs, most jobs, a
1:20:15lot of jobs. No.
1:20:16>> What about robotics?
1:20:17>> Robotics is not what we're talking
1:20:19about. Robotics is a very different
1:20:20thing. And even then,
1:20:21>> robotics will be powered by AI.
1:20:23>> I mean, yes, but there are tons of
1:20:24different kinds of AI. We're talking
1:20:26explicitly about generative AI. And
1:20:27that's what I this mythbusters piece
1:20:29that was definitely about generative AI.
1:20:31>> Okay. But what about robotics? Like the
1:20:33thing is the Optimus robot that Elon's
1:20:35working on at Tesla.
1:20:36>> The one where even in the demo of the
1:20:39hand he like they had to have a guy
1:20:41controlling it. Wasn't doing it
1:20:42autonomously. Here's the thing. If they
1:20:44can beat all these challenges, yeah,
1:20:46robotics would be really cool. I don't
1:20:48know how long that's that's one I'd
1:20:50actually be willing to believe in a
1:20:52couple decades.
1:20:53>> Have you seen them ch them Chinese
1:20:55robots? I know you've seen them. the
1:20:56uni, what's it called? The one that can
1:20:58dance and that, but they can't really do
1:21:00human things.
1:21:01>> Well, it's just it is pretty
1:21:02mindblowing.
1:21:04>> Robotics are cool. I like I'm
1:21:06not going to pretend. I don't think
1:21:07robots are cool. I wish they were
1:21:09building robots and actually doing cool
1:21:11I wish the tech industry still
1:21:12made fun stuff and interesting stuff.
1:21:14Instead, we get these large
1:21:16language models. But with AI plus
1:21:18robotics is, you know, I was in San
1:21:20Francisco and I went to this massive um
1:21:22incubator there. And when I'd gone there
1:21:24three years earlier, it was all software
1:21:26startups, right? And when I went back
1:21:27three years later, it was all these
1:21:29robot startups. And I remember saying to
1:21:30the founder of the incubator, I was
1:21:32like, "Why is everything robots now?"
1:21:34There was this one robot where it was
1:21:35just the arm and it had a frying pan on
1:21:37it. Yeah.
1:21:38>> And it whole thing is it cooks for you.
1:21:39>> Yeah.
1:21:40>> So it was he was showing me it cooking
1:21:41whatever. And he goes, "Well, you know
1:21:43the arm." He goes, "The the hardware
1:21:45part, the physical parts,
1:21:47>> that's always been fairly cheap." Yeah.
1:21:48>> He goes, "The expensive part was the
1:21:50intelligence. And now that's come down
1:21:52to pennies." So what you're seeing is
1:21:53this explosion in the robotics industry
1:21:55because robotics is a function of
1:21:57intelligence plus hardware. We've always
1:21:58had the
1:21:59>> and a ton of data though as well and the
1:22:00data is very expensive.
1:22:02>> Yeah.
1:22:03>> The thing is cyber cabs rolled out real
1:22:05slow. It's going to take a long time. It
1:22:08could be a threat if they do a robot
1:22:10that could replace a human job. Sure it
1:22:12could. But that human jobs are
1:22:13multifaceted. Human jobs change with
1:22:15environments. And also a lot of human
1:22:17jobs that you might think of like I
1:22:19don't know dishwashing robot for
1:22:21example.
1:22:21>> Yeah.
1:22:22some guy at a restaurant isn't paying 10
1:22:2420 grand for a robot to replace the job
1:22:26that they're already not paying enough
1:22:28for. The point is, yeah, it could if you
1:22:31can replace the jobs. That is not what
1:22:33we're talking about with this.
1:22:34>> Yeah. I I just I just I ask these
1:22:36questions not because I'm trying to be
1:22:38like I actually I'm trying to form my
1:22:39own opinion on these things and
1:22:42>> I I do think, you know, as it's written
1:22:45there, it says AI will replace all human
1:22:48jobs. Obviously not. Obviously, that's
1:22:49Yeah.
1:22:50>> But um I'm trying to figure out if the
1:22:51truth is somewhere in the middle that
1:22:53there's a certain type of job which
1:22:55actually humans probably shouldn't have
1:22:57ever been doing really.
1:22:58>> Um if you think back through history,
1:23:00there was someone's job just to sit in
1:23:01an elevator and press the buttons.
1:23:02>> That's an example of a job that humans
1:23:04probably shouldn't have been doing. And
1:23:05as technology gets more advanced, it
1:23:07takes on a lot of that
1:23:09>> sort of automated monotonous stuff.
1:23:11>> Right? The thing is with this particular
1:23:14thing that I know that this is from,
1:23:15it's a specific blog I wrote. I was
1:23:17explicitly talking about generative AI
1:23:18though. I was explicitly [clears throat]
1:23:20talking about people when they say this
1:23:22they are referring to that.
1:23:23>> So you're not talking about agentic AI
1:23:24which is
1:23:25>> agentic AI is LLMs. Agentic AI is just a
1:23:27fancy way of saying an LLM talking to
1:23:29another LLM with a harness on top. That
1:23:31is still LLM. Agentic AI is one of the
1:23:34big the bigger lies they to tell. It's
1:23:35like when you hear agent you're meant to
1:23:37think autonomous AI can do what you
1:23:38want. It's still LLMs. It's still LM
1:23:40talking to other LMLs
1:23:42>> taking screenshots and putting them in
1:23:44LLM and stuff.
1:23:44>> Oh god. Yeah.
1:23:45>> Okay. But but you know I could I could
1:23:47make the case that
1:23:49I'm just thinking about my personal
1:23:51usage. I definitely use agents to do
1:23:54things that I would have previously
1:23:55asked people to do. It's not to say that
1:23:56I didn't I still don't hire cuz we're
1:23:57hiring like crazy.
1:23:58>> Yeah.
1:23:59>> And I still in that particular function.
1:24:00I'm thinking about like the chief of
1:24:02staff role. So my chief of staff would
1:24:04have triaged all of my inboxes
1:24:06previously and put them somewhere and
1:24:08told me about them or maybe once upon a
1:24:09time shown me a piece of paper back in
1:24:11the day. I guess now my chief of staff
1:24:13is no longer doing that job. You still
1:24:14have a chief of staff though.
1:24:16>> This is what I'm saying. They're doing
1:24:17other things,
1:24:18>> right? But the thing is again what you
1:24:20were describing is
1:24:22fairly basic automation. I don't know
1:24:23what the tasks are triaging.
1:24:25>> Basic spend a trillion dollars on
1:24:27triaging email. Like that's the the
1:24:29promise. If they'd spent $10 billion and
1:24:31this was much smaller and you I go cool
1:24:33software. Yay. A lot of the things that
1:24:35people are impressed with like script
1:24:36stuff as well. It's just LM's doing
1:24:38Python. You should be impressed by
1:24:39Python code. Python's incredible. You
1:24:41can scrape websites. You can download
1:24:43It's awesome. But the point I'm
1:24:45making is none of this would be anywhere
1:24:47near as much of a problem if they didn't
1:24:50ask for all of the attention, all of the
1:24:51money, and promise the world. It's their
1:24:53promises that are the problem. And the
1:24:55journalists who went along with it, and
1:24:56the analysts and the Twitter people who
1:24:58went along with this, saying that this
1:24:59would change everything and replace
1:25:00everything and leaving the realm of
1:25:02reality. Is there any technological
1:25:04innovation through history that was
1:25:06really, really game-changing where that
1:25:08didn't happen?
1:25:10I mean
1:25:12the internet
1:25:13>> I mean people overpromised that
1:25:15>> I mean they overpromised on the
1:25:16businesses but I've read through a great
1:25:19many pieces about the early internet a
1:25:21lot of people were excited but hesitant
1:25:24they were worried that there was not
1:25:26enough demand but they were still like
1:25:28oh yeah this could have potential
1:25:30ramifications if it happened. People
1:25:32were not super negative about the
1:25:34internet. A lot of the skeptics were
1:25:36saying we're worried about an overload
1:25:37of bad information. Look at where we
1:25:39are. A lot of people were worried about
1:25:41the social consequences of everyone
1:25:42talking online, which they were correct
1:25:44about. With the economic things, they
1:25:46were specifically talking about like the
1:25:47globe, which I think made hundreds of
1:25:49thousands of dollars and had like a I
1:25:51think a billion dollar market cap, but
1:25:53they were talking.
1:25:54>> Yeah, there was massive hype in the com
1:25:56era.
1:25:56>> I read a lot of those stories. The hype
1:25:57was nowhere in it. You didn't have
1:25:59articles everywhere that were saying if
1:26:01you don't get online, you'll be left
1:26:02behind. You didn't have professional
1:26:05consequences. Nick Sesh mentioned his
1:26:07blog earlier. He described this thing
1:26:08global uh AI sisterating global
1:26:11decision-m where he said that you have
1:26:13businesses you work at where if you
1:26:16don't say that you're more productive
1:26:17with AI whether or not it's true is
1:26:19irrelevant you have professional
1:26:21consequences you can get fired there are
1:26:23people having to AI wash their jobs by
1:26:25saying AI did it otherwise their bosses
1:26:28who don't do will get mad at them
1:26:31this did not happen with the internet it
1:26:33was not present and part of the thing is
1:26:35social media was not like it is today
1:26:37the kind of uh was it decentralization
1:26:40of media in general has caused this as
1:26:42well and also the fact of day trading
1:26:45there's so many different things that
1:26:46are different it's crazy
1:26:47>> I I do think AI is different from the
1:26:50internet in part if you just measured it
1:26:52on the speed of adoption especially if
1:26:54we just think about generative AI AI
1:26:56>> but the this adoption of the internet
1:26:58required physical connections to your
1:27:00house the adoption of generative AI
1:27:02involves having a web browser it took a
1:27:04vast amount of effort to bring internet
1:27:06to people Even with dialup connections,
1:27:08it still required the distribution
1:27:09>> and that's why it was so slow and there
1:27:11was less, you know, there was less hype
1:27:13than AI. I do agree that there's way
1:27:14more hype and we again going back to
1:27:16this point that we're clustering AI in
1:27:19this big category of lots of different
1:27:21things.
1:27:21>> There's generative AI.
1:27:22>> There's generative AI. There's like real
1:27:24world AI.
1:27:24>> Generative AI is explicitly what I'm
1:27:26talking about here. When bosses are
1:27:27saying you need to use AI, they're not
1:27:29saying I need you to go and buy a
1:27:30Unibeam robot. They're saying use LLM so
1:27:32that I and that's the thing. They have
1:27:35this theory, the era of the business
1:27:36idiot where it's like we are ruled by
1:27:38people that don't do work because nobody
1:27:39who actually does a bunch of work who
1:27:41really is productive is harassing
1:27:44someone who works for them for not being
1:27:45productive enough.
1:27:47>> They're not they don't have the time.
1:27:48They're doing work. Someone who is
1:27:50sitting there with the ingratiation
1:27:51machine that's telling them that every
1:27:52beautiful idea out of their messy little
1:27:54skull is amazing. Yeah. They're going,
1:27:57"Damn, this thing says I'm a genius. Why
1:27:58are you not using the genius machine to
1:28:00do more work?" And yeah, if you're a
1:28:02boss that goes to lunch, leaves lunch,
1:28:04and sometimes reads your emails, LM are
1:28:06magic.
1:28:06>> I, you know, one of the most compelling
1:28:08arguments I have for the overhype of AI
1:28:12>> in a world where everybody has access to
1:28:14these tools, whatever the
1:28:15[clears throat] tools can do, would
1:28:17largely be commoditized. What the tools
1:28:20can't do, which one could say is the
1:28:23human taste, judgment, you could say
1:28:25it's people, skills, whatever you want
1:28:26to say, is now going to be the valuable
1:28:29thing because the scarce and the hard
1:28:31becomes the most valuable through
1:28:33history and the commoditized becomes the
1:28:35least valuable. So the very nature that
1:28:37we're commoditizing, the generation of
1:28:39content or whatever you want to call it,
1:28:40code means that's actually not where the
1:28:42value will acrue as for the user. And
1:28:45actually if you think about what it
1:28:48takes to now make something that is
1:28:50objectively great if an AI can do it
1:28:54then it's not the the great thing is not
1:28:56of value.
1:28:57>> So so I think a lot I've been thinking a
1:28:59lot actually about how
1:29:01>> how do you um avoid the temptation of
1:29:04sloppification of the things you make
1:29:06the value you put into the world. It's
1:29:08very simple example that people will be
1:29:09able to relate to. If you use chat GBT
1:29:12or anthropic, you know, Claude to make
1:29:14your LinkedIn posts, let's say,
1:29:16>> they will be LinkedIn posts because
1:29:18everybody else is using them. And
1:29:19actually, a great LinkedIn post now is
1:29:21someone who doesn't use them and makes
1:29:23something that's like irreplaceably
1:29:24human,
1:29:25>> right?
1:29:25>> And deeper and more personal N of one
1:29:30lived experience.
1:29:32>> Yeah.
1:29:32>> All these things that AI can't do. And I
1:29:34think that's a compelling argument that
1:29:35actually the commodity tools produce
1:29:38commodity outcomes. So everyone has
1:29:40access to these things and what's
1:29:41changed? Like really like what
1:29:42>> the slopification we've we've got a
1:29:44bunch of slop but these people were
1:29:46halfassing their jobs before. It's just
1:29:47a halfass arcery machine and it's just
1:29:50it's it's the thing. It's what I'm
1:29:51talking about with the slot blogs. It's
1:29:53like it's it yeah people that gave you
1:29:55dog before have now got the dog
1:29:56machine to pump out dog It's
1:29:59so there's a guy called Carl Brown uh
1:30:01internet bucks. Awesome guy. Great
1:30:02software engineer. He he said I might
1:30:05have said this earlier. So, it makes the
1:30:06easy things easy, the hard things
1:30:07harder. When you know you're doing a
1:30:08really distinct small script for
1:30:10something and it can plop that out. It's
1:30:12awesome. I used Claude the other day for
1:30:14something useful. My kid loves
1:30:15Minecraft. I was trying to fix a
1:30:17broken mod cuz he loves his wither
1:30:19storm. It's awesome.
1:30:20>> And it still took me half an hour and
1:30:22kept getting things wrong. What do you
1:30:24use AI for? Generative.
1:30:25>> I really don't. I don't use it
1:30:27>> with Bloomberg terminal. I use AskB,
1:30:29which is just when it's like requesting
1:30:31the consensus analyst estimates for
1:30:32Nvidia,
1:30:33>> but otherwise you don't use it.
1:30:34>> No. So, how do you know it's bad? I've
1:30:36used it. I've put it through its paces.
1:30:38I've used it to try and do financial
1:30:39models and found one error and
1:30:41immediately be like, "Ah, I've never
1:30:42been particularly impressed." The one
1:30:44thing I will defend it on is it's really
1:30:46good for like tech support. Like I have
1:30:48this thing called Synergy in my New York
1:30:50New York place I go to. I have this
1:30:51monitor where I have a MacBook and a PC
1:30:53laptop and this thing Synergy for using
1:30:55the same mouse and keyboard.
1:30:57>> Dropping a giant
1:31:00troubleshooting log into this thing and
1:31:01going, "What's wrong?" And it going,
1:31:03"This is wrong." Yeah, super useful. Is
1:31:05that trillion dollars? No. Is that a $2
1:31:07trillion company? No. Pretty use.
1:31:08>> Better than Google though, right? Better
1:31:10than Google search.
1:31:10>> I know. I mean, yeah. Remember,
1:31:12>> do you use Google search still?
1:31:14>> I try. I have to push the crap
1:31:16out of the way. And
1:31:17>> I can't remember the last time I did a
1:31:20Google search.
1:31:20>> Christ, I find myself using Bing
1:31:22sometimes. I know. I hate saying it,
1:31:24too. But I have to scroll past the AI
1:31:26crap cuz I want the good stuff. I want
1:31:28the I want the actual links to stuff so
1:31:30that I can read the thing and go. But
1:31:33you can ask the AI to give you the
1:31:35links.
1:31:35>> Yeah. And it doesn't do a particularly
1:31:37good job. Like my
1:31:38>> So say that the other day my iPad wasn't
1:31:41turning on and it was doing this funny
1:31:42little thing on the screen. You think
1:31:43that it's better to type that into
1:31:45Google than
1:31:46>> Oh, no. I must be clear that may be the
1:31:48only LLM use case I defend. The
1:31:50troubleshooting thing is awesome for it.
1:31:52I It's the the one weakness I have. It's
1:31:54like genuinely being able to drop a log
1:31:56into it. That's awesome. Again, that is
1:31:59not what they're selling it as. They're
1:32:00not selling it as a useful little tool.
1:32:02They're selling it as the uh software as
1:32:05the thing that will change everything
1:32:07that will replace all jobs that will do
1:32:09this and that. It's not like they sold
1:32:11it as a quirky bit of software.
1:32:12>> No, you are right. They are, you know,
1:32:14telling us that it is going to replace
1:32:15everything. But funnily enough, the
1:32:17critics are saying that as well.
1:32:18>> Which one I mean I mean
1:32:19>> they are like the Jeffrey Hintons of the
1:32:21world. you know, even people that have
1:32:23left the safety team in chat who who
1:32:25I've sat here with the these are critics
1:32:27that are that are warning of the impacts
1:32:30it's going to have on the world. It's
1:32:31weird how all these critics also have
1:32:33vested interest in AI doing well though.
1:32:35Daniel, former open AI guy, AI 2027
1:32:38written with the Star Codeex guy that
1:32:40was nothing more than badly written
1:32:42science fiction that he's already had to
1:32:43walk back.
1:32:44>> You know, he could have made more money
1:32:45by staying at chat.
1:32:47>> Could he?
1:32:48>> I mean, looks like he lost
1:32:49>> if he had options early. it sticking
1:32:52around.
1:32:52>> Did he lose the options? How much do
1:32:54they
1:32:54>> You're not saying that they're they're
1:32:56being critical. They're not critical of
1:32:58the companies themselves. They're not
1:33:00critical of the stealing. They're not
1:33:01critical of the environmental damage.
1:33:03They're not critical of the fact that
1:33:04you cannot rely on the answers. They're
1:33:06critical of this big scary boogeyman out
1:33:09in the future where it's like, "Oh, I'm
1:33:12scared of when this becomes so powerful
1:33:13and everyone should talk to me about how
1:33:15scary and powerful it is." They're not
1:33:17saying, "Hey, here are the harms today.
1:33:18Here are the things we're actually
1:33:20looking at today. Here are the social
1:33:21problems of having this automated way of
1:33:25spewing out slop, of filling our feeds
1:33:27with crap, of having information that
1:33:30will pop up that is presented even with
1:33:31the little disclaimer thing of saying,
1:33:33"Yeah, sometimes this gets wrong."
1:33:34So, in the tiniest words possible, they
1:33:37don't talk about the fact that these
1:33:39things are trained on stealing millions
1:33:41of people's work. But on that last point
1:33:42where you say that it's going to get
1:33:44progressively more intelligent and when
1:33:45it does, it will be a danger.
1:33:46>> Yeah. Would you agree with the statement
1:33:49that artificial intelligence has gotten
1:33:51more intelligent
1:33:53if you measure it based on any sort of
1:33:55measure of intelligence one might use?
1:33:57>> It's got better on the tests that are
1:33:59rigged for the models. It's got better
1:34:00at tests where you can train for the
1:34:02test.
1:34:03>> Okay, so it's got better at
1:34:04>> it's got better at tests that they're
1:34:06intentionally trained for.
1:34:07>> So if you logged the rate of improvement
1:34:10on a graph, it would look something like
1:34:12this,
1:34:13>> right?
1:34:14>> You agree? in terms of what it's capable
1:34:16of doing.
1:34:17There we go. Yeah,
1:34:18>> cuz it's not it's not got new features.
1:34:21You'll notice that outside of OpenAI and
1:34:23Anthropic the VA when you remove the
1:34:25coding startups, there's basically no
1:34:27successful AI startup company.
1:34:29>> So, we agree that it's got better. It's
1:34:31got more capable
1:34:34at doing things.
1:34:35>> Yeah. Okay. Over time, AI's got more
1:34:37capable. If we imagine that trajectory
1:34:41will continue, it will get more capable.
1:34:43Then at some point it does cross you
1:34:46know this is what they say to me it
1:34:48crosses human intelligence and at such
1:34:50time
1:34:51>> will it not start to do some of the jobs
1:34:54that people are doing today
1:34:55>> outside of software engineering remove
1:34:57software because I will concede software
1:34:58engineering it's got better at that
1:35:00outside of software engineering where
1:35:02>> so the chief of staff things that admin
1:35:04>> okay so it's got better admin video
1:35:06generation photo generation
1:35:08>> text generation theoretically coding
1:35:11>> right
1:35:12>> and then I'd say agentic workflows. So
1:35:14>> what is an agentic workflow?
1:35:15>> So automated workflows where you're
1:35:17doing the same I mean a good example is
1:35:20looking at the backend data of the dire
1:35:21of a CEO
1:35:22>> summarizing
1:35:23>> looking at all of the data ingesting all
1:35:24of it going out into the internet and
1:35:25searching who Ed is
1:35:27>> looking at every interview you've ever
1:35:28done ever.
1:35:29>> Uhhuh.
1:35:30>> This is summarizing and generating
1:35:32>> making a little model on you know the
1:35:33things people want to know from Ed.
1:35:35>> Producing a report sending that to my
1:35:37inbox.
1:35:38>> Me getting a 20 30 40 50page report on
1:35:40Ed before he arrives.
1:35:41>> This is all basically the same thing. I
1:35:42think it's been doing for years though.
1:35:44It's It's not really new capabilities.
1:35:45>> Research. It's It's
1:35:48>> still the same things. They've had web
1:35:49search for years. They've had report
1:35:51generation for years.
1:35:52>> Well, we couldn't generate
1:35:54highquality videos that are like
1:35:56indistinguishable from cameras. Seed
1:35:58dance and these ones that look like
1:36:00movies.
1:36:01>> I mean, they
1:36:01>> are incredible.
1:36:02>> So, I'm saying the point I'm trying to
1:36:04make is that if we imagine that over the
1:36:05last 10 years there has been a rate of
1:36:06improvement in terms of capabilities and
1:36:08output and quality. We've seen
1:36:10hallucinations drop. We've seen the
1:36:12models get more quote unquote
1:36:14intelligent, get better at, you know, if
1:36:15you did give it an IQ test, it's getting
1:36:17higher scores than it was 10 years ago.
1:36:18We agree that there's been a upward
1:36:20motion of improvement.
1:36:21>> This is pretty much how machine learning
1:36:23goes when you feed it more data.
1:36:24>> Exactly. And you put more compute behind
1:36:25it. So if this continues,
1:36:29what does the future look like? So the
1:36:32rebuttal I was expecting to hear is that
1:36:33it won't continue. And actually,
1:36:35>> I actually don't think it I think that
1:36:37there are hard limits that we're going
1:36:38to hit. So you do believe in that
1:36:40there's a hard limit somewhere.
1:36:41>> We've kind of already hit the
1:36:42diminishing returns level because for
1:36:45example video generation which is by the
1:36:48way far less an American concern
1:36:50anymore. OpenAI shut down Sora. I think
1:36:52you can still use the API but
1:36:54nevertheless look at the look around you
1:36:56with the amount of stuff in the crew you
1:36:57need to get a shot. People think the
1:36:59movies are just shot by shot by shot and
1:37:00they just magically happen. When you've
1:37:02got my my wonderful girlfriend of first
1:37:04ads, assistant directors, you've got
1:37:06gaffers, you've got lighters, and also
1:37:08simulating light is insanely difficult.
1:37:10There are so many magical things that
1:37:12happen in creating visual images that
1:37:14yeah, you could create a one minute long
1:37:16thing that might fool someone. How do
1:37:18you practically turn that into a movie?
1:37:20Because that movie, I forget what the
1:37:21name is. There was a movie that claimed
1:37:22it aired at Can. It didn't. No one. It
1:37:26aired in the city of Can during the Can
1:37:28Film Festival. It was not at the film
1:37:29festival. When it comes to the practical
1:37:31creation of actual things at the end of
1:37:33it versus magic tricks, the actual
1:37:35practical outcomes are not there. The
1:37:36reason I keep coming back to the
1:37:37capabilities thing for the example is
1:37:39yeah, they can do better at tests, do
1:37:41better number go up. When it comes to
1:37:44can this actually do distinct tasks you
1:37:46can rely on it, you can rely on it for
1:37:48summaries. You can rely on it for
1:37:49generations. The things it was doing,
1:37:51it's getting linearlyish better at. But
1:37:54again, there's a ceiling to that. Like,
1:37:56okay, so it gets really good at
1:37:58research. What does that actually mean?
1:37:59you've already kind of got the
1:38:00automation there. What is the next step
1:38:02of that? Because training it to be more
1:38:04autonomous for example, that's not
1:38:06something that comes from training data.
1:38:07That is actually a new Gary Marcus a
1:38:10neuros symbolic. You actually need to
1:38:11build a structure around the AI to make
1:38:13it work. And even then, it doesn't fix
1:38:15the
1:38:16>> So you're saying that there will become
1:38:17a point where the rate of improvement
1:38:20will plateau.
1:38:21>> We're already there and stop.
1:38:22>> We've already hit that diminishing. Gary
1:38:24Marcus said this in 2022 as well. Do you
1:38:25know there's lots of people listening
1:38:26now that like they've had their
1:38:28workflows completely transformed by
1:38:30these tools? Have they?
1:38:32>> There'll be people. Yeah, there are.
1:38:33Yeah. The thing is, first of all, every
1:38:35single one of them, did you pay for the
1:38:37tokens? That's the thing. Did you pay
1:38:39for the tokens? And also, how many
1:38:41tokens did you burn? But putting all
1:38:42that aside, what workflows? Because if
1:38:43it's, yeah, I did a bunch of web
1:38:45scraping or web searches. I'm just not
1:38:46impressed. Did you make an entire
1:38:48movie? No, you didn't. Is it
1:38:51speeding up your coding? Yeah, I believe
1:38:52that. I've heard that from multiple
1:38:54people. But again, how much can you
1:38:56trust this?
1:38:57>> I think I'm I was getting at is, you
1:38:59know, when in the moment of any
1:39:01technological innovation, people they
1:39:04extrapolate linearly or they view it as
1:39:08a static state, i.e. they think today is
1:39:10going to look like tomorrow or they
1:39:11think it's going to get better in this
1:39:12sort of straight line. But what we end
1:39:14up seeing a lot of the time is this
1:39:15exponential improvement. All of the
1:39:17innovations we're talking about with you
1:39:18with like with compute and all that with
1:39:20fast processes, those are hardware
1:39:22breakthroughs. The hardware breakthrough
1:39:24companies don't seem to be fixing the
1:39:26LLM problems despite the all the king's
1:39:28horses, all the king's men with what
1:39:30nine 10 generations of TPUs from Google
1:39:32now. Broadcoms building stuff with open
1:39:34AI, their halapeno chip. And yet none of
1:39:37these people can just say, "Yeah, we're
1:39:38on the path to making this profitable."
1:39:40Because they can't. If we fix the
1:39:42environmental problems and the
1:39:43profitability situation, maybe I'd be
1:39:45more generous with this stuff. But they
1:39:47don't seem to be able to. And you talk
1:39:50about these improvements and
1:39:52capabilities. There's a certain point at
1:39:54which I'm saying, "Okay, can it do even
1:39:57a tenth of the stuff they're promising?"
1:39:59Sam the other week was saying it
1:40:00was going to be in like 6 months will be
1:40:02like a genie that you can ask wishes for
1:40:04from like never watched
1:40:06Aladdin. What's he talking about? Like
1:40:08also the the genie was charming. Anyway,
1:40:10long story short, the promises do not
1:40:14line up with the capabilities or the
1:40:15capability improvements. An exponential
1:40:17improvement
1:40:19in software and software performance is
1:40:22always a result of direct hardware
1:40:24improvement. We have all the gifted
1:40:26mathematicians, all the gifted software
1:40:28engineers, all the gifted hardware
1:40:30engineers. And where are we? Trillion
1:40:32plus dollars in with the future great
1:40:35financial crisis and the world's
1:40:37greatest marketing scop.
1:40:38>> I just think in the future I do think
1:40:40that all of the devices and the
1:40:41computers we use and the physical items
1:40:43in our world will be more intelligent. I
1:40:45mean sure but is that LLMs
1:40:48>> and that will be powered by the
1:40:49underlying AI infrastructure. It will be
1:40:51the more data data centers. It will be
1:40:53energy coming down.
1:40:54>> How does a GPU full data center
1:40:58translate to a Nikon camera that can I
1:41:03don't know even what you'd think think
1:41:05like because what is the thing we're
1:41:06talking about here? Because the idea
1:41:08that devices will get smarter. Sure, I
1:41:11can see that. It's a very broad
1:41:12statement. I could see it happening.
1:41:13It's really kind of happening. What does
1:41:15that have to do with the data centers?
1:41:16Cuz these data centers again are not
1:41:18being built to make your consumer
1:41:20electronics smarter. They're not being
1:41:22built for anything other than
1:41:24speculating on the ability to capture
1:41:26demand for generative AI services.
1:41:27>> But it's not just generative AI. We went
1:41:29through that earlier.
1:41:29>> Yes. No, but those data centers, they
1:41:31are being built for generative AI. They
1:41:33are not being built for anything else.
1:41:34Would you consider generative AI to be
1:41:37the fact that on Meta's earnings call
1:41:38like a couple of weeks ago, Mark
1:41:40Zuckerberg said, "The big breakthrough
1:41:41we've had, which has resulted in 15
1:41:43basis points of increased retention, I
1:41:46believe he was referring to Instagram,
1:41:48is that we now take anything you post on
1:41:50social media and we run it through an AI
1:41:53to get full context of what it is." And
1:41:55because we can see guy sat in front of
1:41:57me called Ed with blue shirt and coffee,
1:42:01we now can train the AI to serve whoever
1:42:03wants blue shirt, Ed, and with coffee to
1:42:06the right user, which means people are
1:42:08retained longer because
1:42:09>> it'sn't 15 basis points, like 0.15%.
1:42:11>> Yeah, it's cool. But it makes a
1:42:12difference at scale. It makes a big
1:42:14difference at scale.
1:42:15>> Yeah. But 10 and something billion
1:42:17dollars in and the best you've got is
1:42:180.15%. If if he could be fight I mean
1:42:22how much of a difference because
1:42:24>> there's a reason he's saying basis
1:42:26points versus dollars
1:42:28>> because think about it like this if Mark
1:42:30Zuckerberg was
1:42:31>> I take your point about scale. No, I'm
1:42:33saying the point I was making was that
1:42:35that is another application of these
1:42:38data centers because it needs a data
1:42:39center that is driving revenues, but
1:42:43also that's not out that's outside of us
1:42:45thinking about just generating
1:42:47>> and that's generative
1:42:49model. Muse was it? Oh, Muse Spark is
1:42:51their LLM. Gem is their generative ad
1:42:54model. Well, Muse then then that's them
1:42:56doing the weird thing where it's like on
1:42:58Instagram and it's like Dave the cat.
1:42:59Why is Dave the cat suffering? Like it's
1:43:01the weird popup things. Meta is
1:43:04god damn that company sucks. Like every
1:43:06time I think about how they've ruined
1:43:07that product. But that's the thing
1:43:08though, again, why can't he just say
1:43:10with his whole chest, we've made a
1:43:11couple billion. Why can't he say that?
1:43:13Because he isn't. Because there's not
1:43:14actually a way of going, I spent all
1:43:16this money. I spent 14 billion goddamn
1:43:19dollars on scale Alexander Wong and I
1:43:22made this much. They can't. It gets back
1:43:24to a very simple point of, hey, if it
1:43:27was going well, you'd tell me how well
1:43:29it was going rather than, I don't know,
1:43:31doing this weird rain dance thing where
1:43:33you're like, well, if we move all the
1:43:35pieces around in 3 years, theoretically,
1:43:37this will happen.
1:43:39I've done almost 700 interviews with
1:43:42some of the most interesting people in
1:43:43the world. And one of the things you
1:43:44learn, which is unexpected, is that
1:43:46vulnerability is the doorway to
1:43:48connection. And after sitting here for 2
1:43:50three hours with a guest, I feel a deep
1:43:53sense of connection to them. And as they
1:43:55leave, what I get them to do is to write
1:43:57a question in the diary of a CEO. We've
1:44:01taken all of the questions from the
1:44:02diary of a CEO. We have put the question
1:44:06here on this card with the name of the
1:44:09person that wrote it. So you can sit at
1:44:10home as I do with my fiance and my
1:44:13colleagues at work and other people in
1:44:14my life. Whenever we get a minute, we
1:44:16play the diio conversation cards and it
1:44:20is incredible what happens. These are
1:44:22great if you're in a romantic
1:44:23relationship and you want to connect
1:44:25your partner more. These are also great
1:44:26if you're in a team and you want to bond
1:44:28your team together. And I have to say
1:44:30they're also great for families that
1:44:31want to learn more about each other and
1:44:33that need a good excuse to spend some
1:44:35time in a digital world in the analog
1:44:38environment connecting human to human.
1:44:40It is remarkable what the right question
1:44:43at the right time can do. Go to the
1:44:46diary.com
1:44:48and you can get these conversation cards
1:44:50right now. There should be a button just
1:44:53down below here. And if it says
1:44:54subscribed, you're already subscribed.
1:44:56If it says subscriber, that means you're
1:44:58not yet. And if you're not subscribed,
1:45:00please could you do us a favor and hit
1:45:01that button? It helps the show more than
1:45:02you know. And according to the
1:45:04algorithm, you're someone that watches
1:45:06our show, but you haven't yet hit that
1:45:07button. Thank you so much. I do think
1:45:09you're accurate and right when you talk
1:45:11about the fact that there's a lot of
1:45:13like is the word for gazy?
1:45:14>> Yeah.
1:45:14>> Where like there's a lot of people that
1:45:16have spent a lot of money and they kind
1:45:17of shouldn't have spent it and they
1:45:18up and now they're thinking
1:45:20like we've spent all this invested money
1:45:21kind of like the metaverse was a bit of
1:45:23a
1:45:23>> oh my god that was a bit of a joke.
1:45:24>> That's so weird.
1:45:25>> A lot of money spent. We kind of thought
1:45:27this dream was coming of this well I
1:45:28shouldn't say dream cuz it's not a dream
1:45:30I've had but
1:45:31>> dream that they had.
1:45:31>> Yeah. This sort of virtual world and
1:45:33actually it never transpired and there's
1:45:35no sign that it will in the near term.
1:45:37AI and the dotcom boom in this regard
1:45:40are the same. NFTTS were the same,
1:45:43>> you know. So crypto, one could argue
1:45:44that a lot of the crypto industry was
1:45:46the same. It's weighing that is inflated
1:45:48by the media. The difference is the
1:45:49reason the metaverse and NFTs didn't
1:45:52escape this was there weren't stocks to
1:45:54speculate on. There weren't big
1:45:55companies that you could invest in. They
1:45:57had re record earnings in 2021. There's
1:46:00a bunch of money floating in the system
1:46:01thanks to postcoid uh the PDC that
1:46:04basically government federal money
1:46:06flowed in to the banks. There was a
1:46:07bunch of easy money zero interest free
1:46:09era money was easy to find. Then after
1:46:11that there was the hangover. Growth
1:46:12started to slow down dramatically. This
1:46:14is actually my rockcom bubble theory
1:46:16which is they don't have any hyperrowth
1:46:18ideas anymore. So suddenly they started
1:46:21buying GPUs. And when they bought GPUs
1:46:23people went they're doing AI. Oh we
1:46:26better buy the stock. And the stocks
1:46:27went on an incredible run. may like
1:46:28several hundred percent grow in the last
1:46:30few years. the stock has grown by
1:46:32hundreds of percent. Despite zero proof
1:46:35and because the media was just saying,
1:46:37"Yeah, Meta's revenues growing because
1:46:40of AI, right? Microsoft's revenue is
1:46:41grown because of AI, right? The fugazi
1:46:43you're talking about was the fact that
1:46:45everyone just gave them credit in
1:46:46advance and now we're kind of getting to
1:46:48the point where it's like, hey, you
1:46:50didn't spend that trillion dollars for
1:46:51no reason, did you? Satcha Amy Amy Hood
1:46:54just going to take him out back, send
1:46:56him to the glue factory or something?"
1:46:57Like,
1:46:57>> I do think there's overspending. I I
1:46:59want to concede that but I doic
1:47:01>> yeah no I do think there is and I think
1:47:03the reason why there's overspending Ed
1:47:06is I think there is something here
1:47:08>> and what
1:47:10>> in terms of like I think there is pra p
1:47:12p p p p p p p p p p p p p p p p p p p
1:47:12practical uses for this technology and I
1:47:14think when people realize that through
1:47:16history they go crazy because they want
1:47:18to be the person that owns the
1:47:19opportunity.
1:47:20>> I'm going to be honest I just I
1:47:21fundamentally don't agree.
1:47:22>> You don't agree with which part you
1:47:24>> I don't agree that this that the
1:47:25speculation is a result of actual
1:47:27demand. I don't believe it's suspect. I
1:47:29don't think private credit is sinking
1:47:30hundreds of billions of dollars into AI
1:47:32because of actual demand. They are doing
1:47:33it because they saw the biggest
1:47:34companies in the world building data
1:47:36centers making a ton of money from two
1:47:37companies they feed money and went I
1:47:39want some of that money.
1:47:40>> I am saying that I do think there is
1:47:42value in the underlying technology. I
1:47:44think that and so I think I'm not saying
1:47:47how much value
1:47:47>> right okay I actually I get your meaning
1:47:50that's fair.
1:47:50>> I'm not saying it's proportionate to the
1:47:52investment. All I'm saying is that do
1:47:54you know what it's like? It's like if I
1:47:56take your example, the rot economy essay
1:47:57that you wrote.
1:47:58>> Yeah.
1:47:58>> Say that you're on a desert island and
1:48:00then someone says they found a banana
1:48:02tree,
1:48:02>> right?
1:48:03>> And there's there's 10,000 people on the
1:48:06island.
1:48:06>> Okay.
1:48:07>> They are going to stam peed
1:48:10towards where they think the banana tree
1:48:11is. They are going to claw each
1:48:14other to pieces. And if if your essay
1:48:16here is right that there was desperation
1:48:17cuz they hadn't found an innovation in a
1:48:19while,
1:48:20>> maybe that explains it. Maybe there is a
1:48:21bit of value here,
1:48:22>> right?
1:48:23>> And they're stam peeding and
1:48:25killing each other and making irrational
1:48:26decisions like hungry people would.
1:48:28>> I actually think we're then we actually
1:48:30agree. That is actually my point, which
1:48:32is these three companies in Meta, their
1:48:34main business lines are running out of
1:48:36growth. There's only so much they can
1:48:37grow. And indeed, in the next three and
1:48:38a half years, analysts think that these
1:48:40two bastards, these two, OpenAI and
1:48:42Anthropic are going to spend over $400
1:48:43billion on these people alone,
1:48:46Microsoft, Google, and Amazon. And the
1:48:48crazy thing is is that's a large part of
1:48:49their future growth. And if this money
1:48:51isn't spent, their growth slows down.
1:48:53Okay,
1:48:53>> so your point about a bananas, I
1:48:55actually agree. That is the rockcom
1:48:56bubble, it's they don't have a new thing
1:48:58and they're desperate. And indeed, they
1:49:00got rewarded for buying the GPUs. They
1:49:02got when they bought these goddamn GPUs
1:49:04from Nvidia, all the markets went
1:49:07rockard overnight. They loved it. There
1:49:09were stories about how they were sending
1:49:10armored cars with the GPUs to Microsoft
1:49:13to make sure Microsoft got the GPUs. And
1:49:15so everyone saw all that money flowing
1:49:16in. Even though they never disclosed AI
1:49:18revenues, they saw the expenditures and
1:49:20they went, "Well, I want to do what
1:49:22these people are doing. I want to get a
1:49:23little of that money, don't I?"
1:49:25>> I think the area where we have a slight
1:49:27disagreement is that I think the
1:49:29underlying technology has a lot more
1:49:31promise over the long term than you do.
1:49:34So the thing I want to push back on
1:49:36there is
1:49:38to have progress with AI just on a
1:49:41taking it in a vacuum to have progress
1:49:42for these two companies to keep going
1:49:44and to keep progressing they need to
1:49:47spend tens of billions of dollars a year
1:49:49on training.
1:49:50>> The only way that that can happen is if
1:49:53these companies and venture capitalists
1:49:54and private credit firms and Nvidia
1:49:56>> keep circulating money to them. So the
1:49:58progress
1:49:59>> that we've got so far is entirely a
1:50:01result of this circular system. So it
1:50:04means that
1:50:05>> circular you talked about VCs there
1:50:06>> venture capitalists who are by the way
1:50:09the majority of the funding that open
1:50:11AAI got in the last 6 months came from
1:50:14SoftBank Nvidia and Amazon
1:50:16>> okay yeah
1:50:16>> so just the point is is you're talking
1:50:18about progress continuing progress in
1:50:21LLM can only continue as long as the
1:50:23money keeps flowing once the money keep
1:50:26once the money stops flowing the
1:50:28progress stops which
1:50:28>> but isn't that most like early like
1:50:30Spotify didn't make money for 20 years
1:50:31>> Spotify didn't lose 20.9 9 billion in
1:50:34one year. They didn't need to raise $217
1:50:36billion in the space of 6 months.
1:50:38>> Yeah. And Uber is another example.
1:50:40>> $33 billion since inception before it
1:50:42became a messy kind of profitable.
1:50:43Amazon Web Services between 2003 and
1:50:452015 when it became profitable. $29.7
1:50:48billion the scale. Yeah. That's the
1:50:50total capital expenditures and that's
1:50:52not just Amazon Web Services. That's the
1:50:53entire logistics operation normalized
1:50:55for inflation.
1:50:56>> So they all lost money for a long period
1:50:58of time is the TLDDR.
1:50:59>> Yes. But the amount of money they lost
1:51:01is
1:51:04completely
1:51:05just magnitudes different on a level
1:51:08where these three
1:51:09>> Can I argue then that the that's because
1:51:11the potential of intelligence permeates
1:51:14everything whereas Amazon at the time
1:51:16was like selling books
1:51:17>> no
1:51:17>> that was that was bringing retail online
1:51:19>> when Amazon web services grew it was
1:51:21>> oh so cloud with Amazon web services the
1:51:25reason I bring that up going to repeat
1:51:26something but it's really important 2003
1:51:28it was founded
1:51:29>> and it was founded mostly because Amazon
1:51:31as a growing online store needed
1:51:33hardcore infrastructure. 2006, I think,
1:51:36is when they turned it client-f facing.
1:51:38I may be wrong on the dates there, but
1:51:392015 was the year it became profitable.
1:51:41>> Yeah.
1:51:41>> The total capital expenditures
1:51:43normalized for inflation with $29.7
1:51:45billion across that 12-year period.
1:51:48>> Yeah.
1:51:48>> And yeah, it lost money, but
1:51:51>> if we speak cold economics here, Amazon
1:51:55didn't have to go into the they were
1:51:56unprofitable in in a way, but their
1:51:58margins actually started improving
1:51:59because AWS was a very margin heavy
1:52:01business. It was great.
1:52:02>> Yeah,
1:52:03>> these these two Google cash flow
1:52:06negative, Amazon cash flow negative.
1:52:08These businesses, the reason you liked
1:52:09software businesses was they are meant
1:52:11to be cash heavy asset light. These
1:52:16companies along with Meta have added
1:52:18more than $700 billion of new property,
1:52:21plants and equipment. So assets, data
1:52:23centers, GPUs in the last four years.
1:52:26They have gone from being these cash
1:52:28machines to these cash furnaces.
1:52:31>> You said a second ago, this can only
1:52:33continue if if investors continue to
1:52:35invest.
1:52:36>> Yes.
1:52:36>> And I was saying I I think that
1:52:38investors are used to pumping money into
1:52:40things that are burning cash. Your
1:52:42rebuttal to me sounds like well this is
1:52:44burning more cash than ever. And then so
1:52:46I would say well is the opportunity
1:52:48bigger than those other case studies you
1:52:51referenced like AWS? And one would say
1:52:54that the opportunity of intelligence
1:52:58permeates everything. So the TAM the
1:53:00total addressable market is enormous.
1:53:03Maybe the revival back to me is about
1:53:05open source and all these kind of
1:53:06>> No, no, no. I I actually know what
1:53:07you're getting at. So what you were
1:53:08describing there is the argument that
1:53:10Sachinadella or Sam would make that the
1:53:12theoretical opportunity of large
1:53:14language models and I could have bought
1:53:16that into any 24 from them when
1:53:18they were like, "Oh, we see the
1:53:20opportunity. We've gone way past the
1:53:22point at which you can rationally argue
1:53:24that LLMs need this much money. And when
1:53:27I say the money needs to keep flowing, I
1:53:28am talking these two compan Open AI just
1:53:32open AI Clammy Sam has said Wall Street
1:53:35Journal and Isaagi reported a few weeks
1:53:37ago they plan to spend $750 billion on
1:53:42compute through 2030. I think they're
1:53:44going to be dead before then, but $750
1:53:46billion.
1:53:48That is an insane amount of money. That
1:53:50is crazy
1:53:50>> and [laughter]
1:53:51a large chunk of that is training. So
1:53:53when I say progress, I mean literally to
1:53:55make the models better at stuff requires
1:53:57billions of dollars invested just in
1:53:59data
1:54:00and also tens of billions of dollars of
1:54:02taking that data. And so training
1:54:04training is actually a really
1:54:05interesting thing because when you think
1:54:07of like for Jake and Troy my trainers
1:54:10when I train with them when I lift with
1:54:11them I have a defined thing and when I
1:54:13do it and I eat right muscles get bigger
1:54:15they would. And here's the thing. When
1:54:17you train with an LLM, you're
1:54:18experimenting each and this is not
1:54:20actually a hit on the companies because
1:54:22they're still trying to work out how to
1:54:24do the thing because putting aside how I
1:54:26feel like they're trying to innovate. I
1:54:28think there are people at these
1:54:29companies that actually want to do
1:54:30something interesting. It's costing too
1:54:31much money. So once the money tap turns
1:54:34off, the money won't be there to buy the
1:54:36data or feed the data into the GPUs. Put
1:54:38aside all the thoughts I have, just the
1:54:40raw capital to get them this far has
1:54:43cost increasingly larger amounts of
1:54:45money and increasingly larger amounts of
1:54:47training money for training runs that
1:54:49sometimes can fail. GPT5 was meant to be
1:54:52this panacea for the AI industry. They
1:54:55had at least one training run that cost
1:54:56half a billion dollars and did nothing.
1:54:58And that's the thing. If we are thinking
1:55:01about progress in a in a vacuum, they
1:55:03need so much more money just to maybe
1:55:05get somewhere. There's no guarantee.
1:55:07There's never any guarantee, but there's
1:55:08a reason that Google and Amazon are cash
1:55:10flow negative now. There's a reason why
1:55:11Oracle's probably going to die as a
1:55:13result of OpenAI because Oracle's future
1:55:16depends on OpenAI spending $300 billion
1:55:18over 5 years.
1:55:19>> It's absolutely fascinating because I
1:55:21was just reading through a list of
1:55:22quotes from the big CEOs of AI companies
1:55:24to see what they would rebuttle you.
1:55:26>> Yeah.
1:55:27>> And they're all basically saying the
1:55:29same thing. They're all saying, this is
1:55:31actual an exact quote from Sundar who is
1:55:33the CEO of Google. He says the risk of
1:55:36underinvesting is dramatically greater
1:55:39than the risk of overinvesting.
1:55:42And you go down, you go through this,
1:55:44you know, Andy Jasse, CEO of Amazon,
1:55:46we're not investing approximately 200
1:55:48billion in capex in 2026 on a hunch.
1:55:51We're not going to be conservative in
1:55:53how we play this. We're investing to be
1:55:55the meaningful leader and our future
1:55:58business operating income and free cash
1:55:59flow will be much larger because of this
1:56:02investment. Then Mark Zuckerberg, CE of
1:56:04Meta, says we'll continue to invest
1:56:06aggressively in infrastructure to meet
1:56:08the demand. I'd rather risk building
1:56:10capacity before it's needed than being
1:56:12late. Makes me think of Shrek with L
1:56:15Farquad. Some of you may die, but that's
1:56:17a risk I'm willing to accept. It's like,
1:56:19you know, I'm just going to spend all
1:56:20this money. You can't fire me cuz Mark
1:56:22Zuckerberg can't be fired due to the
1:56:23unique board situation he's got going.
1:56:26So yeah, he's just going to piss the
1:56:27money away and hope he's right. And I
1:56:28know from the people who know it matter,
1:56:29he's not right. The thing is, why might
1:56:32you be wrong?
1:56:33>> I mean, this is the thing. The AI people
1:56:35who claim this is going to be the
1:56:36biggest, strongest thing in the world,
1:56:37did they ever get that? I I mean this
1:56:39like
1:56:39>> it's a good question because it's like
1:56:40they don't. And the thing is, what would
1:56:42it take for me to be wrong? A bunch of
1:56:44hardware breakthroughs to make this
1:56:45profitable. A bunch of
1:56:46>> question new mathemat because the thing
1:56:48is
1:56:48>> when it comes to being a critic or a
1:56:50skeptic,
1:56:51>> you are put on the hot seat. Not the
1:56:53people spending a trillion dollars, not
1:56:55the people promising the world. The
1:56:56person the the with a blog is
1:56:58the one who's like me. Trust me. If they
1:57:00came here, they'd be on the hot seat,
1:57:01too. Trust me.
1:57:02>> Oh, I Oh, they they won't talk to me.
1:57:05Don't know why, Steve. They don't know.
1:57:07It's cuz I call him Clammy Sammy. Um
1:57:09>> I think it's cuz my guests are quite
1:57:10quite critical that I don't think Solman
1:57:12wants to come here.
1:57:13>> Mr. Orman, go on Steve show. Do it. But
1:57:15this is the thing like of course they're
1:57:17going to say that. And also, if they
1:57:19thought they were right, I don't think
1:57:20they do anymore. If I was in their shoes
1:57:22and I thought that this was an
1:57:23existential thing, sure. But it gets
1:57:25back to the rocom bubble which is yeah
1:57:27this is the last thing they've got.
1:57:28>> But I really want to know that question.
1:57:29It was one of the questions I was really
1:57:30excited to ask you which is you have a
1:57:32different opinion. We said this at the
1:57:34top. You have a very different opinion
1:57:36from a lot of people. I would categorize
1:57:38the the two most popular opinions as
1:57:40>> uh AI is going to hurt everybody and
1:57:42it's going to be catastrophic and we
1:57:44need to stop.
1:57:44>> Yeah.
1:57:44>> The other opinion is age of abundance is
1:57:46going to be amazing. Let us crack on.
1:57:48yours is different from both of those
1:57:50which is as you said in your words it's
1:57:53a con and it's and there's no real
1:57:55underlying value in the technology and
1:57:57it's overhyped.
1:57:58>> Yes.
1:57:58>> And there's way too much spending. I
1:58:00mean a few people agree on the spending
1:58:01part but the other part. So with you
1:58:03it's one of probably the first person
1:58:05that I've spoken to that's had this
1:58:06opinion.
1:58:08>> So how what would it take for you to
1:58:10change your mind about what you believe
1:58:14here? There would need to be a hardware
1:58:16breakthrough that reduced the cost by
1:58:18like a thousand but it would have to be
1:58:19just a dramatic breakthrough that is not
1:58:22happening just to be clear because
1:58:23they've all been trying. So it's the
1:58:25cost for you that would have to change.
1:58:26>> It's the cost and it's also the data
1:58:28centers. I think the way they're
1:58:29building the data centers is reckless
1:58:30and damaging to communities. The fact
1:58:32that you have communities like in
1:58:34violent New Jersey where the residents
1:58:35like I don't want this but the planning
1:58:37boards vote for it because they're all I
1:58:39assume having chummy lunches with the
1:58:41people doing it. I think the use of gas
1:58:43turbines is disgraceful. I the
1:58:45water situation I'm not super well read
1:58:47on, so I'm not going to wait into it,
1:58:48but the use of gas turbines and behind
1:58:50the meter power is reckless and damaging
1:58:52to communities. The noise that these
1:58:54things make and also generative AI is
1:58:57this egregious pornographic
1:59:00demonstration of how unfair the world
1:59:02is. Regular people try and get a loan
1:59:04for a business, a random business. They
1:59:06want I have a good idea. They go to a
1:59:08bank, a bank of town, go
1:59:09themselves. They'll say, "I'm not g you
1:59:11going to make a store that sells stuff.
1:59:12Screw you. You want to build a data
1:59:14center? You Jensen Hang will back you.
1:59:17Jensen Hong will give you 25% residual
1:59:19value. You want to build a regular
1:59:21business that's even profitable?
1:59:23you. No, a venture capitalist won't give
1:59:25you the money. Something that's just
1:59:26growing steadily, but it's profitable.
1:59:28Screw that. No, I need 10 100x return.
1:59:31Try and get a mortgage. You have to give
1:59:33the bank a full colonic. But you want to
1:59:35get money for Jensen Hong to buy some
1:59:37GPUs? He'll give you a contract.
1:59:39Corewave is a great example. C Neocloud,
1:59:41which is just a company that builds data
1:59:43centers and puts GPUs and rent them to
1:59:45people. Nvidia, one of their first
1:59:47investors in 2023, signed a $1.3 billion
1:59:51contract to rent back their GPUs from
1:59:54Core. So that Core go to a bank and go,
1:59:56I got a customer. Yeah, it's the guy I'm
1:59:59buying the GPUs from with the debt I'm
2:00:01getting from you. If you want to buy
2:00:03GPUs, it's open season. If you want to
2:00:04live a regular life where you build a
2:00:06regular business or buy a house, highest
2:00:08interest rates ever. Screw you. Up
2:00:11yours. Yeah, you need to show us way
2:00:13more than that. I don't trust you
2:00:14regular folks. But if you're an
2:00:16unprofitable Neocloud, you get billions
2:00:19from Jensen. It doesn't matter.
2:00:21>> It's so interesting. You It's
2:00:22interesting because you are the first
2:00:24person that I've spoken to that has that
2:00:25opinion.
2:00:26>> I am prouser. Let's take another myth.
2:00:29AI will be conscious. Mhm. So
2:00:34super intelligence, artificial general
2:00:36intelligence, these are theories. Anyone
2:00:39saying this stuff will become this is
2:00:42just guessing and does not have proof.
2:00:44>> Okay.
2:00:45>> And like that's really it.
2:00:46>> Okay.
2:00:47>> Okay. Let's take another myth.
2:00:50AI systems are already blackmailing and
2:00:52escaping control. So this is a really
2:00:54specific one. Anthropic. There's
2:00:56actually two. Open AAI's GPT 3.5. I
2:01:00realize this is more than the sentence.
2:01:01I apologize.
2:01:03In their system card, and a bunch of
2:01:05media outlets covered this, saying that
2:01:07OpenAI's model blackmailed a task rabbit
2:01:10into solving a capture. What actually
2:01:12happened was a user of GPT doing the
2:01:17experiment
2:01:19got it to generate things to say to a
2:01:21task rabbit to make a task rabbit do
2:01:23stuff.
2:01:24>> A task rabbit
2:01:24>> as in a person that you rent, not even
2:01:26to do a capture. It's something you rent
2:01:28to like nail a picture up in your
2:01:30apartment. It's an insane example. This
2:01:32was covered as if these things
2:01:33blackmailed someone and and it and they
2:01:36specifically said, "Yeah, we prompted it
2:01:38to do this." And also the other note was
2:01:40that yeah, AI systems can't do
2:01:42autonomous stuff like this. Then there
2:01:43was this other one where Anthropic said,
2:01:45"Oh yeah, a model was blackmailing
2:01:47someone saying that if you don't do
2:01:49this, I'll email proof that you slept
2:01:51with someone else other than your wife."
2:01:53I think it was what actually happened
2:01:54was Anthropic explicitly trained a model
2:01:57to do this and then prompted it to
2:01:59blackmail.
2:02:00This keeps happening and the media just
2:02:03slop slot me up. I don't need no
2:02:05thoughts. Put the story in the bag. And
2:02:08it's frustrating because it scares
2:02:10people. Put aside the fact it's wrong.
2:02:12It's scary. It's scary to people. people
2:02:14living their lives who have to work
2:02:16longer hours to make less money and
2:02:18their money doesn't go far and they turn
2:02:20on the news and there's some
2:02:21being like, "Yeah, you should be
2:02:23terrified it blackmailed someone."
2:02:25>> But this is this is so counterintuitive
2:02:27of their interest to some degree and
2:02:30they've experienced it backfire.
2:02:31>> Well, they have now like it's it's
2:02:33literally backfired.
2:02:34>> It's backfired. Eric Schmidt getting
2:02:35booed at a commencement speech by 8,000
2:02:38people every time he said the word AI.
2:02:40But I mean this is this is I mean these
2:02:42serious are being attacked at home.
2:02:44>> Yeah. Which sucks. Which is
2:02:46>> terrible. I must be clear like you
2:02:48dislike the don't hurt people.
2:02:49>> Yeah. Don't don't attack people at home.
2:02:51But but the point here is that that
2:02:53narrative is backfiring in a big big way
2:02:56for them. I don't think they saw it
2:02:58coming because you have to remember you
2:02:59mentioned regulation earlier. These tech
2:03:01companies have been glazed for their
2:03:03entire existence. Travis Kick's like oh
2:03:06what? People don't like me now. And it's
2:03:07because Uber was a horribly run place
2:03:09and he was kind of a monster. Also tons
2:03:12of articles about how great Uber was at
2:03:13the time. The point I'm making is these
2:03:14companies are not used to push back.
2:03:16They thought what would happen I believe
2:03:18just guessing. They thought they do this
2:03:20scary stuff and they would just get
2:03:21floods of money and everyone would just
2:03:23be like I kneel before you. I'll do
2:03:25whatever you want. They didn't expect I
2:03:28think what has I I agree this has
2:03:30backfired on them because they were in
2:03:32articulate. They're disconnected from
2:03:34regular people. Samman drives a $5
2:03:36million car around San Francisco. So
2:03:39that that man's doing it like 9 miles an
2:03:41hour. It's hilarious. But these people
2:03:43are disconnected from everyone else. So
2:03:44they don't they don't experience real
2:03:46problems, so they can't build the
2:03:47solutions for them. And they think,
2:03:48well, if we scare people into doing what
2:03:50we want, that'll work, right? It didn't.
2:03:52They was all of this blackmail stuff was
2:03:55an attempt to make it mystic. It was a
2:03:57mysticism attempt. It was to make it
2:03:58seem like this unknowable, impossible to
2:04:00control, just this powerful thing. But
2:04:02we're the only ones. We are the o only
2:04:05us only these two angels could possibly
2:04:08control the beast we've created.
2:04:10>> This is this is quite a controversial
2:04:12statement but I think that for some
2:04:14reason I trust Dario a little bit more
2:04:17because I think he's been the most
2:04:19balanced in his writing about the risk
2:04:21profile.
2:04:22>> I
2:04:22>> whereas the others they they seem to
2:04:25kind of move with the wind.
2:04:27>> I I do you know
2:04:28>> I get what you mean. The reason I don't
2:04:30like Dario is Daario was doing the scare
2:04:32tactics thing when he worked at OpenAI
2:04:34when GPT2 came out say it's too scary to
2:04:37release. He's also gone on television
2:04:39and given AI psychosis to Axios being
2:04:42like 50% of jobs are going to go away
2:04:44because of AI.
2:04:45>> What I respect is the consistency. He's
2:04:49now being attacked by them.
2:04:50>> Good.
2:04:51>> Um but the thing is sorry I mean let me
2:04:53clarify the word attack. Darian is being
2:04:56verbally attacked by Silicon Valley and
2:04:59you know if Silicon Valley if powerful
2:05:01people in Silicon Valley are attacking
2:05:03someone.
2:05:04>> Four months ago he wasn't though. They
2:05:05were all saying he was the smartest boy
2:05:07ever.
2:05:07>> The point I want to make there as well
2:05:08is again wow you're so scared of how
2:05:10powerful this is. You're so scared of
2:05:11it. It's so scary. What are you doing
2:05:13about it? Oh nothing. Like it's just
2:05:15like what are you doing? Well we have an
2:05:16alignment team. So does every AI lab.
2:05:18Well I guess open AI cycles through
2:05:20those really quickly. Here's the thing.
2:05:22If I'm Dario Amade, I'm sitting there
2:05:23going, I'm scared of all things changing
2:05:26and I thought I had made a thing that
2:05:28would eliminate all jobs, I'd be
2:05:30terrified. I'd be walking around with
2:05:31like like a 10 ton weight on my back.
2:05:34The show, the responsibility, the fact
2:05:36he doesn't, the fact he wants to be this
2:05:38weird elder statesman that's too scared
2:05:40to hold Sam Orman's hand at an event
2:05:42just makes me believe that he's just
2:05:44saying it because it's convenient and
2:05:45he'll wind that back as he kind of
2:05:47already has whenever it's convenient for
2:05:49him. I think Open AAI and Anthropic are
2:05:51basically the same level of Bad Company.
2:05:53I think Anthropic is more cultlike. I
2:05:56think it's so weird like Jack Clark over
2:05:58there, one of the co-founders. That fell
2:06:00used to be at the register. He used to
2:06:01be one of the most critical journalists
2:06:02ever. Now he's it's like like something
2:06:04took over him because they talk of these
2:06:06things in these high fluent terms. But
2:06:08then again, maybe the people at
2:06:09anthropic buy their Maybe some of
2:06:10the people at OpenAI buy their I
2:06:12don't know. So going back to the central
2:06:13question we asked at the top here was
2:06:15what would have to be the case for you
2:06:16to look back and say do you know what I
2:06:17was wrong in 2026 and you said to me it
2:06:20would be mainly that the cost of
2:06:23production around AI drops dramatically
2:06:26>> and it would have to also do insane
2:06:29amounts of stuff it does it would have
2:06:30to be a truly autonomous
2:06:32>> it would have to continue its
2:06:33improvement in terms of capability.
2:06:34>> It would have to be a different product.
2:06:36It would have to be it would have to be
2:06:37indistinguishable from magic. And the
2:06:38reason they have these high standards is
2:06:40they set them.
2:06:41>> Okay. Fair. It's interesting as well
2:06:42because all these myths and all these
2:06:44conversations, it's about technology,
2:06:46but it's also it's an information war.
2:06:48It's literally
2:06:50narrative versus narrative. Everyone
2:06:52trying to escape the financials,
2:06:54everyone trying to actually escape what
2:06:56the models can do. And the big thing I
2:06:58always say about AI boosters is if I
2:07:00could regulate them, I'd regulate them.
2:07:02They can't speak in the future tense
2:07:03anymore. Just you got to talk about
2:07:04today, mate. You get two weeks in the
2:07:06future, Max. Because if they were
2:07:08constrained to what was happening today,
2:07:10it they would sound like insane people.
2:07:12>> Yeah. No, I think yeah, most I guess
2:07:14most technology companies would at the
2:07:15time. Like Uber would sound insane.
2:07:18Amazon was
2:07:18>> Uber was basically the difference.
2:07:20>> They were pissing money though, weren't
2:07:21they?
2:07:21>> They were pissing money away, but the
2:07:22unit economics were the same just
2:07:24subsidized. So you were still getting a
2:07:26service from A to B and paying a much
2:07:29lower cost. It wasn't like you paid Uber
2:07:32200 sorry 20 bucks a month and you could
2:07:34get 500 miles of Uber and then one day
2:07:36you started paying by the mile cuz
2:07:38that's what's happening with this.
2:07:39>> Have they they've changed their business
2:07:41model for customers like me now so that
2:07:43I have to buy credits.
2:07:45>> No. So you well kind of with
2:07:47>> they asked me the other day. So with the
2:07:49anthropics fable model with some
2:07:51accounts you have to pay for usage and
2:07:53also adoption of fable has been pretty
2:07:55low because of this because of the cost
2:07:57but with enterprises so companies over
2:07:59150 people you have to pay by the token
2:08:02now or per million token.
2:08:03>> Oh so they are moving to a token.
2:08:05>> Yeah. But when they did that everyone
2:08:06went from being like this is the most
2:08:07impressive thing ever to being like
2:08:10>> it's always we got to control these
2:08:12costs. Uber's COO said as Andrew
2:08:14McDonald I think he said that it's
2:08:16getting hard to justify cuz it's hard to
2:08:18connect spending money on tokens to
2:08:20actual useful outcomes.
2:08:22>> He said the thing like he said the
2:08:24actual thing I've been saying and it's
2:08:25so we're in an AI bubble.
2:08:27>> Yes.
2:08:27>> And when will when this AI bubble
2:08:29collapses so much of the economy is
2:08:31resting upon it.
2:08:33>> Yeah.
2:08:34>> It's going to have downstream
2:08:35consequences. So I got two questions for
2:08:36you. I guess the first question is are
2:08:38we in an AI bubble and what happens when
2:08:40the bubble pops?
2:08:41>> Yes. And it's it depends. So the big
2:08:45thing that people say is, "Oh, we'll get
2:08:47bailed out. Donald Trump scared of
2:08:48Donald Trump." Here's the problem with
2:08:50this.
2:08:52It isn't just an AI bubble. It's the
2:08:54rockcom bubble. So the AI bubble
2:08:56collapsing will probably be this company
2:08:58running out of money. Open AI.
2:09:01>> And the thing is with Open AI is they
2:09:03were meant to go public this year and
2:09:04now it's been pushed to next year a week
2:09:06and a half after I released their
2:09:07auditive financials. Wonder where that
2:09:09was. Um, but they've delayed to next
2:09:11year. Sarah Frier, the CFO, has now
2:09:12said, "Well, they'll do it earlier than
2:09:152027 or 2027." Great answer there.
2:09:18>> For anyone that doesn't understand what
2:09:19going public means, that means joining
2:09:21the stock market. And at such a time
2:09:22when you join the stock market, your
2:09:24investors can finally sell their equity
2:09:27that they got for investing in the
2:09:29company when it was private. So often
2:09:31times companies will flirt with the idea
2:09:34of we'll go public someday soon because
2:09:36investors will have a moment in their
2:09:38head where they'll get their money back
2:09:40at a return. So you kind of need to if
2:09:43you're in these guys shoes, you kind of
2:09:44need to be flirting with going public or
2:09:45investors won't want to invest.
2:09:47>> Open AAI up until this point has been a
2:09:49private company and their last funding
2:09:51round they were valued at $865 billion.
2:09:54Now when they tried to go public, New
2:09:57York Times Mike Isaac reported this.
2:09:59They tried to list well they wanted to
2:10:02go at a set a 1 trillion valuation.
2:10:05Apparently their advisor said no don't
2:10:08do that. That is very bad for a number
2:10:10of reasons. One open AI needs perpetual
2:10:12amounts of money. They raised $122
2:10:14billion this year. Most of it's crossed.
2:10:16There's some left but they are going to
2:10:18need to raise at least hundred billion a
2:10:20year just to survive. If they can't go
2:10:22public they will have to raise another
2:10:24funding round. The problem is it's going
2:10:26to be difficult to raise at even the
2:10:28same one they raise that. They're
2:10:29probably going to have to take a flat.
2:10:30So the same amount. Exactly. But they
2:10:34need money. They need money so bad.
2:10:35Amazon sent them $35 billion that was
2:10:38meant to be contingent on them going
2:10:39public early.
2:10:41>> They did that because they need the
2:10:43money. Now, OpenAI is the kind of
2:10:46catastrophe center here because
2:10:47Anthropic is likely going to beat it to
2:10:49go public. And once Anthropic goes
2:10:50public, it'll be borderline impossible
2:10:52for Open AI to do so because Anthropic,
2:10:54an unprofitable, unsustainable AI lab,
2:10:56but a better business that's growing
2:10:58faster than Open AI's. I believe they
2:11:00have a ceiling. They're eventually going
2:11:01to face predition, too. I think sometime
2:11:04in 2027, things are going to start
2:11:05running out of steam. Because the thing
2:11:07I said earlier, the only way these
2:11:08models get better is if you feed more
2:11:10money, tens of billions of dollars into
2:11:12them.
2:11:12>> So, you think OpenAI runs out of steam
2:11:14in 2027?
2:11:15>> I think they're already running out of
2:11:16steam. Yeah. But I think they run out of
2:11:17cash. You think they run out of cash?
2:11:19Yes. And the sequence of events here
2:11:21will be they they go out and try and
2:11:22raise
2:11:23>> and they have trouble raising another
2:11:25round. I think maybe Invidia props them
2:11:27up a little. Maybe Private Credit,
2:11:29Blackstone, Black Rockck and the like
2:11:30the ones and the reason that Private
2:11:32Credit is getting involved. So asset
2:11:33managers is because they're investing in
2:11:35the data centers and they know this
2:11:36company's most of the data center
2:11:38demand.
2:11:38>> Okay. So they run out of steam in 2027
2:11:40according to you.
2:11:41>> Yep. And maybe they try if they bum rush
2:11:42to go public they're going to have worse
2:11:44economics than anthropic. They're going
2:11:45to get savage. it. We work was a great
2:11:47example. Another SoftBank classic. Now,
2:11:50I think Open AI collapses, there are
2:11:52many different ways it could happen.
2:11:54There are many different ways it could
2:11:55end. But the crucial thing is is that
2:11:57there are multiple companies that are
2:11:59existentially tied to OpenAI. SoftBank,
2:12:03one of the largest companies in the
2:12:04Japanese stock market, a holding company
2:12:06with lots of investments. They have on
2:12:08paper about hundred billion worth of
2:12:10OpenAI stock. If they can't go public,
2:12:13they can't do diddly squat with that.
2:12:15And so Soft Bank's future, their ability
2:12:17to continue paying the people around
2:12:19them and existing as a business relies
2:12:21on their ability to continually
2:12:23liquidate funds to be to take the things
2:12:25they've invested in and have value from
2:12:27them either by selling the stock or
2:12:29taking loans out on the stock. If OpenAI
2:12:31can't go public, SoftBank can't do that.
2:12:33SoftBank probably won't run out of
2:12:35money, but we're going to see one of the
2:12:36largest holding companies in the world
2:12:38become much smaller. We will also see
2:12:41Amazon, Google, and Microsoft have to
2:12:43restate guidance. they will have to say
2:12:45actually we don't think we're going to
2:12:47grow as fast
2:12:48>> and what happens then
2:12:49>> well I think we enter a tech depression
2:12:51because the rockcom bubble the core of
2:12:53my theory is that they're out of
2:12:56hyperrowth ideas but the market doesn't
2:12:57think so the reason they're so
2:13:00maniacally spending is because buying AI
2:13:03GPUs allows them to kick the can further
2:13:05allows them to say we're still doing
2:13:07something we're working on AI don't
2:13:08think too hard and also their current
2:13:10businesses are still growing their
2:13:12current businesses will eventually slow
2:13:14there's only so many price increases.
2:13:15There's only so many tweaks to ads. Only
2:13:17so many tweaks to Google search. Only so
2:13:20only so many ways that Amazon can screw
2:13:22merchants. So in that tech depression,
2:13:25which you think it might be triggered in
2:13:272027, is that a cascading downstream
2:13:31economic depression? Because the stock
2:13:33market is heavily dependent on these
2:13:35companies. The stock market sees a
2:13:36pullback, investors stop investing, they
2:13:38get panicked.
2:13:40>> Yes. I think that because
2:13:41>> what's the sort of downstream
2:13:42consequence the sort of domino effect
2:13:44>> there's so much to imagine that it's
2:13:46difficult to capture everything but
2:13:48there are a few things that worry me
2:13:49first of all a ton of American money
2:13:51just regular people's money retail
2:13:53investors are in these companies and
2:13:55they bought into the magnificent 7
2:13:56thinking the number go up forever is the
2:13:58largest company on the Fortune 500 and
2:14:01NASDAQ as well and like 7 to 8% of the
2:14:04S&P 500 that company when in when the
2:14:07bottom falls out from Nvidia and we
2:14:08haven't really got into it but Nvidia is
2:14:10doing the most circular of financing,
2:14:11feeding companies money so that they can
2:14:13raise debt to buy more GPUs. I think
2:14:16Nvidia's revenue could go 50 to 70%
2:14:18down. I think that Nvidia could put
2:14:20Nvidia back in 2022 was making
2:14:22singledigit billion dollars.
2:14:23>> And what happens though, I'm thinking
2:14:24about like Jenny and Dave that are
2:14:26watching this right now and they are
2:14:27just normal people
2:14:29>> with normal jobs.
2:14:30>> People's retirements are going to
2:14:32contract severely and I don't believe
2:14:34they're going to return to those values.
2:14:36And I think that because so much of the
2:14:38value of the S&P 500 and Russell 1000
2:14:40index comes from these four companies
2:14:42and the rest of the magnificent 7. So
2:14:44Apple, Tesla, Meta as well. And the
2:14:47thing is I don't know what happens after
2:14:50that because venture capital has also
2:14:53more than half of venture capital last
2:14:54year went into AI. I think most venture
2:14:56capital investments in AI are going to
2:14:58zero because when it comes to building a
2:15:00company on top of an LLM, all of those
2:15:01are unprofitable too. And the thing is
2:15:04LLM companies have not really been
2:15:06acquired. The exception being Cursible
2:15:08by Elon Musk for the coding side, but
2:15:11you have Cognition, which is just
2:15:12another LLM company raising a $26
2:15:15billion valuation. That means that
2:15:17company has to go public cuz who's
2:15:18buying a company at $26 billion other
2:15:20than Elon Musk. And there were rumors
2:15:22that Elon Musk was trying to buy them as
2:15:23well. Is Elon Musk just going to pick
2:15:25off every like LLM company like going to
2:15:27TJ Maxx for AI? Like Jesus
2:15:29Christ.
2:15:29>> So is that a recession you're
2:15:31describing? It is a recession, but it's
2:15:33also a depression within people's
2:15:35retirements. Like I'm talking about 20,
2:15:3730, 40% off the top of these companies
2:15:39stock value.
2:15:40>> Economic contractions, recessions
2:15:41consistently lead to job losses and
2:15:43rising unemployment. When an economy
2:15:44contracts, the mechanism driving job
2:15:46losses typically follows a predictable
2:15:47sequence. Falling demand, consumers and
2:15:50businesses spend less money, causing
2:15:51revenues across most industries to drop.
2:15:53margin compression. With lower revenue
2:15:56and often fixed overhead costs like rent
2:15:58or debt, corporate profit shrink, and
2:16:00lastly, cost cutting measures to survive
2:16:01or protect profit margins, businesses
2:16:03freeze hiring, reduce hours, and resort
2:16:05to layoffs. Yes, that's that would all
2:16:08happen. But the thing is, we're talking
2:16:09about equity values dropping and we're
2:16:11talking about there not really being a
2:16:13home for that value or that money.
2:16:16[snorts] So much is riding on these
2:16:18companies, but you can't bail it out.
2:16:20You can theoretically bail out OpenAI. I
2:16:22don't think it happens. You could pump
2:16:24these dogs full of money and keep them
2:16:26alive for a bit, but at some point
2:16:27they're going to have to start. They
2:16:29have between these two companies,
2:16:30Anthropic and Open AI, you have $1.1
2:16:33trillion of commitments.
2:16:35>> Just OpenAI.
2:16:36>> Oracle is building 7.1 gawatt of data
2:16:39centers. So over $400 billion worth just
2:16:42for OpenAI. There is not a customer on
2:16:44Earth. And Oracle's revenue has been
2:16:45flat the last 15 years when you adjust
2:16:47for inflation. Without Open AI, Oracle
2:16:49dies. So you think open AAI is going to
2:16:51crash and run out of money and that's
2:16:52going to cause this domino effect across
2:16:54these other big tech companies which is
2:16:56going to impact the stock market and
2:16:57impact the broader economy.
2:16:59>> Yes. And also the tens of thousands of
2:17:01people that will be laid off from the
2:17:02tech sector. But also the venture
2:17:04capital thing is significant because
2:17:05venture capital has been having one of
2:17:08the most historic
2:17:10bad runs in history since 2018. The
2:17:14average return from venture capital
2:17:16total value put in. So the amount of
2:17:17money you get back for your dollar is
2:17:19between8 and 1.21 meaning for every
2:17:21dollar you invest you get 80 cents to
2:17:23$120
2:17:24>> paper gains.
2:17:25>> Well no that's just actual g like actual
2:17:27returns. Paper gains they'll give you
2:17:28but even then internal rate return which
2:17:30is a whole separate thing even that's
2:17:32not very happy. But long story short
2:17:34very simple venture capital is not
2:17:36making money come out. Venture capital
2:17:38is not actually providing returns.
2:17:40>> They're celebrating paper gains.
2:17:42>> They're celebrating paper gains
2:17:43>> and they're raising off paper gains.
2:17:44>> Mhm. And actually paper gains I mean
2:17:46just being able to say oh look the
2:17:47valuation of anthropic went up. So
2:17:49that's
2:17:49>> but that's that's what Google and Amazon
2:17:51were doing. Google's last quarter they
2:17:53boosted their net profits profits on
2:17:55paper by $99 billion because of the
2:17:58increased value of their SpaceX holding
2:18:00and their anthropic holding. And again
2:18:03the fact that this is happening is
2:18:05insane and the fact it's not a scandal
2:18:07is insane but we live in this culture I
2:18:09guess. But everyone is really benefiting
2:18:12right now. Oh, it's really that it's
2:18:14that great tweet. It's like when you're
2:18:15reaping, it's like, "Yeah, yeah,
2:18:17this rocks." Sewing. Ah, This
2:18:19sucks. Because right now, they're all
2:18:20like, "Yeah, all the speculative gains
2:18:22are awesome. The paper gains are
2:18:23awesome. The theoreticals of anthropic
2:18:25being worth $2 trillion. Wow. The
2:18:27articles we can write, the promises we
2:18:29can make. Then when the rubber meets the
2:18:31road, it's going to be pretty rough on
2:18:33them because the valuation of Amazon,
2:18:36Google, Microsoft, and Meta is based on
2:18:38this idea that they will grow eternally,
2:18:39that they will grow forever. If that
2:18:41changes, to quote Ed Elson from ProfitG
2:18:43Markets again, it's this. They're all
2:18:45doing Botox right now. They're sinking
2:18:46money into it to make themselves feel
2:18:48young again and the market believes
2:18:49them. When the market doesn't, we're not
2:18:51just talking about a depression. I'm
2:18:53talking about the market valuing them
2:18:54like airlines and saying, "Yeah, you're
2:18:56real big and you make money off your
2:18:58existing products, but guess what? You
2:19:00don't have new You're just going
2:19:02to be doing this forever and we're going
2:19:04to value you as such."
2:19:05>> So, if it's Jenny and Dave, should they
2:19:08do anything differently? Should they be
2:19:10conserving money? If there's a recession
2:19:11or depression coming, should they be a
2:19:12little bit more conservative? Should
2:19:13they
2:19:14>> I Yes. I actually I actually think it's
2:19:16I don't know. I don't have money in the
2:19:18market. I think it's a casino. Casino
2:19:20pumped up by the media.
2:19:21>> Should they invest in the S&P 500?
2:19:23Should they invest in Open AI?
2:19:24Unfortunately,
2:19:24>> oh god, no. I honestly I live in cash
2:19:27right now. I live in cash. Yeah. I don't
2:19:29trust the market, man. Try and
2:19:31get some gains here. I'm like I'm not
2:19:33comfortable giving financial
2:19:34>> advice, but it's like if you like it's
2:19:37like you're gambling.
2:19:38>> Okay. be conservative. Things might get
2:19:39volatile.
2:19:40>> Yeah, it really is. It's going to be act
2:19:41as you would with volatility. Take the
2:19:43gains when you've got them.
2:19:45>> Don't sell everything, but be suspicious
2:19:48of tech. Like, that's actually the
2:19:49biggest thing. It's like be suspicious
2:19:50of what they're promising. If you're
2:19:51acting based on their promises, don't
2:19:54trust the promises. Trust that they are
2:19:57going to say what will make the stock
2:19:59run rather than what's actually
2:20:01happening. and that they will find every
2:20:04dodgy way to make you think something is
2:20:07happening rather than it's actually
2:20:09happening. Annualized run rate, great
2:20:10example. Microsoft said that they had 38
2:20:13$37 billion of annualized run rate in
2:20:15AI. You hear that, you go, they made 38
2:20:18$37 billion, right? Wow, that's so much
2:20:21run rate maybe month times 12. They
2:20:24don't even define it, but it's built to
2:20:26manipulate. And they do that because we
2:20:28don't have a functional SEC and we don't
2:20:30have a media environment that actually
2:20:32where skepticism is the priority and
2:20:34where protecting the readers is
2:20:36necessary.
2:20:36>> What would they say? They would say Ed
2:20:38this technology is going to be so great
2:20:41and so transformative that we are
2:20:43investing a ton of money
2:20:45>> um in advance of the value and utility
2:20:49showing up. That's what they would say,
2:20:51>> right?
2:20:52>> And I've heard your rebuttal, but I just
2:20:53wanted to express I think that's their
2:20:55sentiment. I'm not defending them or
2:20:57anything. I'm just I'm trying to provide
2:20:58enough like balance to we see if we can
2:21:01dance between these these two
2:21:03perspectives.
2:21:05>> And a lot of people would say that
2:21:08there's going to be a blood bath because
2:21:09they can't all win big in the way that
2:21:12they're kind of describing. So,
2:21:13someone's going to have to lose. And
2:21:14>> when one of these players starts to lose
2:21:16big, I think it could, as you say, there
2:21:18could be some kind of domino effect or
2:21:19contraction.
2:21:20>> Yeah. And I think the thing that people
2:21:22want to believe is they the com bubble
2:21:24thing. It's like it worked out
2:21:25afterwards because Amazon, Oracle, they
2:21:29didn't die after the com bubble. They're
2:21:30actually fine. This isn't like that.
2:21:32They're bigger companies. They're have
2:21:34bigger promises. And even I'm not like
2:21:36Oracle I actually think could die. I RIP
2:21:39Larry. What couldn't happen to a nastier
2:21:41man? They'll probably
2:21:42>> You don't like these people, do you?
2:21:43>> No, I No. Again, I asked this question
2:21:46purely because I want an answer, not
2:21:47because I agree or disagree. But um why
2:21:50don't you like these these people? I
2:21:53don't like being misled and I don't
2:21:55think regular people like being misled
2:21:57either. And I really don't think that
2:21:58the average person can get away with
2:22:01bullshitting as much these companies do.
2:22:03And I don't think the average person
2:22:04gets anywhere near the level of
2:22:06affordance for failure and lying as
2:22:08these companies do. And I think there is
2:22:10a real economic and human cost to
2:22:12allowing these companies to run rampant
2:22:14and promise the world and never really
2:22:16get called up on it. The tepid nature of
2:22:19criticism these days is so frustrating.
2:22:21There are some really great critics out
2:22:23there that really great people, but it's
2:22:25like
2:22:27seeing these ultra rich, ultra wealthy,
2:22:29ultra powerful people lie through their
2:22:31teeth or misstate or whatever
2:22:33people want to call it, it turns my
2:22:35stomach. And I hate seeing people being
2:22:38misled. And I feel like I write at such
2:22:40length because I really want people to
2:22:42see why I've come to a conclusion. Am I
2:22:43right? Am I wrong? I think I am. Of
2:22:45course I do. But I also
2:22:48I just find it loathome. I find these
2:22:51companies don't make good products
2:22:52anymore. They don't care about their
2:22:54customers and and they treat their
2:22:56customers with contempt.
2:22:59>> If people want to go read more about
2:23:01your work, um you have a great Substack
2:23:03>> Ghost actually. It looks exactly like I
2:23:05moved off of Substack in 2024.
2:23:06>> Oh, okay. And you also have a podcast
2:23:09you do.
2:23:09>> Yeah, Better of Flame.
2:23:10>> Um I'm going to link both of them below.
2:23:12So, if anyone wants to read more, get
2:23:13more detail and and follow Ed. I think
2:23:15it's
2:23:15>> I would highly recommend. It's it is
2:23:17fascinating. And you know what? One of
2:23:19the things people um sometimes struggle
2:23:20with when they listen to podcasts is you
2:23:22get lots of different opinions. And
2:23:24weirdly, I think they think of some
2:23:25people assume podcasts are going to be
2:23:26like one person saying the same thing as
2:23:29the next person and then the next
2:23:30person. That is just not the nature of
2:23:32information in the world and opinions
2:23:33and progress and discussion. What what
2:23:35happens is people have different
2:23:36opinions. And I think my job, but also
2:23:38the listener's job is to try and pass
2:23:40through it and over time collect more of
2:23:42these reference points from different
2:23:44people and and do your own research.
2:23:47>> Yeah. whether it's on your health or
2:23:48whether it's on something like this is
2:23:49to watch endear and research and to
2:23:51learn and I would say also never believe
2:23:54one person never believe one particular
2:23:56perspective religiously you know collect
2:23:59a body of evidence and follow follow the
2:24:01evidence yourself but I love watching
2:24:03your YouTube um because it provides a
2:24:06different opinion and that challenges me
2:24:09to think beyond my current opinion about
2:24:13what might be possible so when I've
2:24:14heard you talking about how this is an
2:24:16economic bubble and I've heard you talk
2:24:18about the capex spend on with these big
2:24:20sort of frontier AI labs. It really did
2:24:23make me pause for a second and it really
2:24:25did make me consider
2:24:27that there could be a bit of fazy going
2:24:30on here.
2:24:30>> Yeah.
2:24:31>> And then it made me reflect on history
2:24:32and go, you know, through history
2:24:33there's always a bit of fazy in these
2:24:34moments and oh that's an interesting
2:24:36take on what's going to happen in 2027
2:24:382028 when there's a bit of a market
2:24:39pullback and so I highly recommend
2:24:41people go watch because you do you
2:24:42challenge me to think differently. Um,
2:24:44>> yeah.
2:24:44>> And we need some of those contrarian
2:24:46voices to to have honest discussions.
2:24:49So, thank you for doing what you do.
2:24:50Really appreciate it. And I find you to
2:24:51be a very compelling, captivating
2:24:53communicator. And I've I feel like I've
2:24:54learned a lot today. So, I appreciate
2:24:56that. We have a closing tradition.
2:24:58>> Yeah.
2:24:58>> Where the last guest leaves a question
2:24:59for the next guest not knowing who
2:25:00they're leaving it for. And the question
2:25:02left for you is given that high quality
2:25:04relationships are important for health
2:25:06and longevity, what should we be doing
2:25:08to improve our relationships and social
2:25:11connection? So this is actually
2:25:14connected to the AI bubble. So I am a
2:25:17critic. I'm a skeptic. What quote I have
2:25:20found that showing and appreciating and
2:25:24loving the people around you and
2:25:25uplifting them and me and and raising
2:25:27them up as you succeed is the way we do
2:25:29that. Your success should be everyone
2:25:30around you. It's not economic. It's
2:25:32talking about Matt Hughes for a while
2:25:34made me really happy. This whole thing
2:25:37has been at times quite grueling and
2:25:39quite negative and quite brutal. But the
2:25:41love I found and the joy I found from
2:25:44community and the people around because
2:25:46even in the in the small groups of
2:25:48haters even like Gary Marcus and sort of
2:25:50the people I talked to Edward on Grao
2:25:52Jr. Molly White, Brian Merchant, there
2:25:54are so many people who have been loving
2:25:56and caring. And I think within
2:25:58especially these very critical moments
2:26:00when you're like very much dialing in on
2:26:02how negative things are, how bad things
2:26:04are, finding the people who maybe find
2:26:08it repulsive, too. Finding the people,
2:26:10>> finding your people who can be and the
2:26:12people who will talk to you about it.
2:26:13Even like Troy and Jake, my my trainers
2:26:16who's so excited about this. um even
2:26:18talking to them about the as normal
2:26:19people knowing that there are people
2:26:21there going through their own struggles
2:26:22but also to just give you the
2:26:25perspective and also remind you that you
2:26:27are human to and focus I know this is
2:26:29kind of a all over the place point but
2:26:30it's just it's really easy to get hard
2:26:32locked on everything in life and to
2:26:35>> kind of get away from why you do things
2:26:37and focus too much on the work when the
2:26:39most important thing at times is just to
2:26:41know there are other people feeling the
2:26:42way you do and when I hear from my
2:26:44listeners and my readers a lot the most
2:26:45common thing they feel is they feel like
2:26:47they have a voice and they feel like
2:26:48someone is there for you.
2:26:50>> And I don't think it can be understated
2:26:52how much it means when you just reach
2:26:54out to someone you love and tell them
2:26:55you love them. Tell them their
2:26:56rocks. Say that their bangs. Tell
2:26:58everyone you when you like an artist or
2:27:01a writer they were a podcast like this.
2:27:02Tell them you love it. We don't
2:27:04do this enough and we need to do it
2:27:06more. Well, that's a good closing
2:27:08message. So, if you do have you have
2:27:10enjoyed the conversation today with Ed,
2:27:11please do let Ed know that you love it
2:27:13down below. Um, but please do leave your
2:27:15opinions down below and I shall read all
2:27:16of them. Ed, thank you so much. I'll
2:27:18link to your website, but also to your
2:27:20YouTube channel where people can learn
2:27:22more and I would highly recommend you do
2:27:23because it is truly fascinating and I
2:27:25think we need more voices that are
2:27:26demystifying a lot of the fugazi and the
2:27:28narrative in this moment in time and you
2:27:30are certainly one of them. I really
2:27:30enjoyed the conversation. Thank you so
2:27:32much.
2:27:32>> YouTube have this new crazy algorithm
2:27:34where they know exactly what video you
2:27:36would like to watch next based on AI and
2:27:38all of your viewing behavior. And the
2:27:40algorithm says that this video is the
2:27:43perfect video for you. It's different
2:27:45for everybody looking right now. Check
2:27:47this video out and I bet you you might
2:27:49love it.

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