AI Boom Vs. Internet Boom

Summary

在这段视频中，讨论了当前人工智能（AI）与网络1.0时代之间的共同主题。与互联网时代相比，AI更像是微处理器或大型计算机时代的延续。互联网是一个连接众多现有计算机的网络，而AI则是一个信息处理系统，它处理数据并产生结果。AI的发展可能更类似于计算机行业的早期，从大型机到个人电脑再到智能手机，计算机的形态和规模经历了巨大的变化。AI的未来可能包含各种形状、大小和能力的模型，它们将基于不同的数据进行训练，并在不同的规模上运行，具有不同的隐私和安全政策。此外，AI的易用性可能导致与以往不同的用户锁定情况，因为AI可以像与人交谈一样简单。这段讨论提出了关于AI发展和行业格局的有趣见解。

Takeaways

• 🤖 **AI与互联网的类比**：AI更像是计算机或微处理器，而不是网络。AI处理数据，进行信息处理，与互联网的连接性质不同。
• 🌐 **网络效应**：互联网行业动态主要围绕建立网络或在网络之上构建应用程序，而AI则存在一些网络效应，但并不占主导地位。
• 💡 **AI的新特性**：AI和大型语言模型被视为一种新型计算机——基于概率的计算机，与以往确定性计算机（冯·诺依曼机）有本质区别。
• 🚀 **技术的进化**：从大型机到个人电脑再到智能手机，计算机的体积和成本不断减小，预示着AI模型也将呈现出多样化的形态和规模。
• 📈 **行业金字塔**：计算机行业形成了一个庞大的金字塔结构，从超级计算机到嵌入式系统，AI行业可能会发展出各种规模和能力的模型。
• 🔍 **AI的易用性**：AI因其高易用性而与众不同，用户可以轻松与之交互，这可能会影响其市场锁定效应和用户的选择自由度。
• 🌟 **AI的多样性**：AI模型将基于不同类型的数据进行训练，运行在不同的规模上，具有不同的隐私和安全政策。
• 📚 **历史经验**：从计算机行业的早期发展中汲取教训，而不仅仅是互联网的早期阶段。
• ⚙️ **技术发展周期**：技术发展通常会经历兴衰周期，包括过度兴奋和随后的萧条，AI也可能经历类似的周期。
• 🏭 **产业动态**：AI产业的展开可能会类似于计算机产业，从少数大型模型到广泛分布的各种规模的模型。
• 🧐 **开放性问题**：AI的锁定效应尚不明确，用户可能会根据特定任务的需求自由选择不同规模和价格的AI模型。

Q & A

为什么说将当前AI的发展与Web 1.0时代进行类比并不完全恰当？

因为互联网是一个连接众多现有计算机的网络，而AI更像是一个计算机系统，特别是一个信息处理系统。AI的核心在于数据处理，与互联网时代的网络效应和行业动态有所不同。

在讨论AI时，为什么将AI比作微处理器而不是网络业务？

AI更像是微处理器或原始计算机，因为它是一个系统，数据输入、处理和输出，与网络业务的网络效应和正反馈循环不同，AI更注重数据处理和信息处理。

AI与以前的计算机（如冯·诺依曼机）有何不同？

AI和大型语言模型被视为一种新型计算机，基于概率的计算机，神经网络计算机，与以往确定性、严格按照程序运行的冯·诺依曼机不同，AI能够更好地与人类互动并理解世界。

为什么说AI的能力是可以组合的，并且可以构建出更复杂的东西？

AI的能力是可以组合的，因为它基于模块化的设计，允许从小型、简单的组件构建出更大型、更复杂的系统，这与以往确定性计算机的局限性形成对比。

在AI领域，我们是否可能会看到类似于互联网初期的泡沫和泡沫破裂现象？

是的，因为技术发展往往伴随着过度兴奋和随后的沮丧，AI领域也可能会有泡沫和泡沫破裂的现象，但具体情况会有所不同，因为AI和网络业务的行业动态不同。

计算机行业是如何从大型机时代演变到个人电脑和智能手机的？

计算机行业经历了从大型、昂贵的IBM大型机，到成本更低的小型计算机，再到个人电脑和智能手机的过程。随着技术的进步，计算机变得更小、更便宜，最终普及到各个领域和消费者手中。

AI行业未来可能会如何发展，是只有几个大型模型还是会有多种不同规模的模型？

AI行业很可能会发展成一个包含各种形状、大小和能力的模型的生态系统，类似于计算机行业的金字塔结构，从超级计算机集群到嵌入式系统，基于不同的数据、规模、隐私和安全政策。

为什么说AI是迄今为止最容易使用的计算机？

AI能够理解和生成自然语言，使得与AI的交互就像与人交谈一样简单，这与以往需要专业知识和技能才能使用的计算机系统形成鲜明对比。

在AI时代，用户是否可能面临与以往计算机时代相同的锁定效应？

AI的易用性可能会减少传统的锁定效应，但是否会出现新的锁定形式，例如对特定AI模型的依赖，仍然是一个开放的问题。

为什么说AI的发展可能不会遵循互联网初期的发展模式？

AI作为一个信息处理系统，其发展模式更可能借鉴计算机行业早期的发展，特别是微处理器的发展，而不是互联网的网络效应和行业动态。

在AI领域，网络效应是否还像在互联网时代那样重要？

虽然AI领域也存在网络效应，但它不像互联网业务那样占据主导地位。AI的核心在于其数据处理和学习能力，这些能力带来了新的价值和可能性。

AI的发展是否会像计算机行业那样，最终普及到几乎所有设备中？

是的，随着技术的进步和成本的降低，AI技术很可能会像计算机芯片一样被集成到各种设备中，从而实现广泛的应用和普及。

Outlines

00:00

🤖 AI与互联网1.0的共同主题

在第一段中，Nathan和Ben讨论了当前人工智能（AI）与互联网1.0时代之间的最强大共同主题。他们提到，由于Nathan在Netscape的早期角色，以及Ben在互联网初期的经历，他们经常被问及AI和互联网之间的类比。Nathan认为，尽管互联网的兴起是一个重大的技术事件，但将AI的兴起与互联网的兴起进行比较并不完全恰当。他解释说，互联网是一个连接许多现有计算机的网络，而AI更像是一个信息处理系统，一个新型的计算机。AI的核心是处理数据，而不是建立网络。Nathan认为，AI更类似于微处理器的发展，而不是互联网的发展。他还强调了AI作为新型计算机的能力，特别是大型语言模型，它们与以往确定性计算机不同，具有概率性，并且能够更好地与人类互动和理解世界。

05:03

📈 计算机行业的发展历程

第二段中，讨论了计算机行业的发展历程，以及它对AI未来发展的启示。Nathan提到了早期计算机的巨大规模和成本，以及IBM创始人Thomas Watson的著名论断，即世界只需要五台计算机。随着时间的推移，计算机变得更小、更便宜，最终普及到个人和企业。Nathan预测，AI模型也将呈现出类似的多样性，从大型的“上帝模型”到小型的嵌入式系统，每种模型都有其特定的用途和用户。他还指出，与以往计算机不同，AI非常容易使用，因为它可以理解自然语言，这可能会影响用户对AI模型的依赖和选择。

10:04

🌐 AI的未来发展和用户选择

在第三段中，讨论了AI的未来发展，特别是用户将如何根据自己的需求选择不同规模和能力的AI模型。Nathan提出了一个观点，即AI的发展可能会导致一个包含各种规模和能力的模型的生态系统，而不是只有几个主导模型。他还提出了一个有趣的问题，即用户在使用AI时是否会像使用以前的计算机系统那样面临锁定效应，或者他们将能够自由地根据特定任务的需求选择最合适的模型。这段讨论强调了AI技术的易用性，以及它可能如何改变人们对技术的依赖和选择。

Keywords

💡人工智能（AI）

人工智能（AI）是指由计算机系统模拟的人类智能，包括学习、推理、自我修正和感知等能力。在视频中，AI被比作一种新型的计算机，与传统的冯·诺依曼架构计算机不同，AI更侧重于处理不确定性和概率性问题，能够更好地理解和交互。AI的发展被认为将带来新的产业变革，类似于微处理器的发展对计算机行业的影响。

💡网络效应（Network Effects）

网络效应指的是一个产品或服务的价值随着用户数量的增加而增加的现象。在视频中，网络效应被用来描述互联网行业的特点，如社交媒体网络的竞争和用户增长。然而，视频提出AI更像是微处理器，其价值并不完全依赖于网络效应，而是作为信息处理系统的能力。

💡冯·诺依曼架构（Von Neumann architecture）

冯·诺依曼架构是一种计算机组织结构，由处理器、存储器和输入/输出设备组成，其中存储器被用来存储程序和数据。视频中提到，传统的计算机是基于这种架构的确定性计算机，而AI则被视为一种新型的、基于概率的计算机，它能够处理更加复杂的任务。

💡微处理器（Microprocessor）

微处理器是一种集成电路，它集成了中央处理单元（CPU）的功能，是现代计算机和智能设备的核心部件。视频中将AI比作微处理器，强调AI作为一种新型计算机的潜力，它将推动技术发展并改变行业格局。

💡确定性计算机（Deterministic computer）

确定性计算机是指在给定相同输入的情况下，总是产生相同输出的计算机。视频中提到，传统的计算机是确定性的，它们严格按照编程执行任务，而AI则不同，它能够处理不确定性，提供更加灵活和人性化的交互。

💡神经网络（Neural Network）

神经网络是一种模仿人脑神经连接方式的算法，它是AI中用于处理复杂数据和模式识别的关键技术。视频中提到，AI和大型语言模型被视为一种基于神经网络的新型计算机，它们能够理解和处理语言、图像等复杂信息。

💡主框架（Mainframe）

主框架是一种大型、昂贵的计算机系统，通常用于处理大量数据和复杂的计算任务。视频中通过主框架的发展历史来类比AI的未来，指出最初计算机的体积和成本都非常大，但随着技术进步，计算机变得越来越小、越来越便宜，并普及到各个领域。

💡迷你计算机（Minicomputer）

迷你计算机是相对于大型主框架计算机而言的较小型计算机，它们在20世纪中叶开始流行，成本较低，适用于中型企业。视频中提到迷你计算机作为计算机发展史的一部分，展示了从大型机到个人电脑的演变过程。

💡个人电脑（PC）

个人电脑是指设计用于单个用户使用的计算机，它们在20世纪70年代末和80年代初开始普及。视频中通过PC的发展来说明计算机如何变得更加小型化和普及，最终成为家庭和工作中不可或缺的工具。

💡智能手机（Smartphone）

智能手机是一种集成了移动电话功能的便携式计算机设备，具有互联网接入、高级操作系统和多种应用程序。视频中提到智能手机作为计算机小型化和普及的另一个例子，展示了技术如何渗透到日常生活中。

💡嵌入式系统（Embedded Systems）

嵌入式系统是一种专门为特定的控制功能而设计的计算机系统，通常嵌入在设备或产品中。视频中提到，随着技术的发展，嵌入式系统变得越来越普遍，从汽车到家用电器都包含了这种小型化的计算机系统。

Highlights

AI的发展与网络1.0时代的相似之处被讨论，但最终认为两者之间存在根本性差异。

互联网是一个连接许多现有计算机的网络，而AI更像是一种信息处理系统，即一种新型计算机。

AI的行业动态、竞争动态和创业动态与互联网时代不同，更类似于微处理器的发展。

AI的网络效应与互联网时代相比较弱，AI更像是一个芯片或计算机，具有数据处理的功能。

传统计算机是确定性的，每次执行相同的任务，而AI计算机是概率性的，结果可能不同。

AI能够理解语言和图像，这是以往确定性计算机无法解决的问题。

AI的发展可能更接近于计算机行业的早期阶段，而不是互联网的早期阶段。

技术发展中不可避免的会有过度兴奋和随后的低迷，即所谓的“泡沫和破裂”。

计算机行业的发展从大型机到微型计算机，再到个人电脑和智能手机，呈现出多样化的趋势。

AI的未来可能包含各种形状、大小和能力的模型，形成整个生态系统。

AI模型的多样性将基于不同种类的数据训练，运行在不同的规模上。

AI的易用性是其一大特点，它可以通过自然语言与人类交流。

AI的“锁定效应”与传统计算机不同，用户可能更自由地选择所需的AI模型。

AI的发展可能带来与以往不同的行业格局，包括不同的公司和多样化的AI应用。

AI的普及将导致几乎所有设备都嵌入芯片，并最终连接到互联网。

计算机行业的发展历史对理解AI的未来发展提供了宝贵的视角。

AI的未来发展可能由多个因素决定，包括成本、选择速度和特定任务的需求。

Transcripts

[Music]

Nathan uh Nathan OD asks uh what are the

strongest common themes between the

current state of AI and web 1.0 and so

let me start there let me give you a

theory Ben and see what you think um so

I get this question you know because of

my role and you know Ben you you with me

at Netscape you know we we get this

question a lot because of our role early

on with the with the internet so there's

a you know the internet boom was like a

major major event in technology and it's

still within a lot of you know people's

memories um uh and so uh you know the

sort of you people you know people like

to reason from analogy so it's like okay

the AI boom must be like the internet

boom starting an AI company must be like

starting an internet company um and so

you know what what is this like and we

actually got a bunch of questions like

that you know that are kind of analogy

questions like that um I actually think

and you know and then Ben you know you

and I were there for the internet boom

so we you know we live through that and

the bust and the boom and the bust um so

um I actually think that the analogy

doesn't really work um for the most part

it works in certain ways but it doesn't

really work for the most part and the

reason is because the the

internet the internet was a network um

whereas AI is a computer

yep okay yeah so so so people understand

what we're saying like the PC

boom or or the PC Boomer even I would

say the microprocessor like my best

analogy is to the micro processor yeah

or or even to like the original

computers like back to the main frame

era um and and the reason it's because

yeah look what the internet did was the

internet you know obviously was a nwor

nwor but the network connected together

many existing computers and then of

course people built many other new kinds

of computers to connect to the internet

but fundamentally the internet was a

network and then and and and that's

important because most of most of the

sort of Industry Dynamics competitive

Dynamics startup Dynamics around the

internet had to do with basically

building either building networks or

building applications that run on top of

networks and this you know the internet

generation of startups was very consumed

by Network effects and you know all all

these so positive feedback loops that

you get when you connect a lot of people

together and you know things like met

you know so so-called metast law which

is sort of the value of a network you

know expands you know kind of the way it

expands as you add more people to it um

and then you know there were all these

fights you know these fights you know

all the social networks or whatever

fighting to try to get network of facts

and try to steal each other's users uh

because of the network of facts and so

it was kind of you know it's dominated

by by by Network effects um which is

what you'd expect from from from a

network business um AI like there there

are some networks effects in AI that we

can talk about but um it's it's more

like a microprocessor it's more like a

chip it's more like a computer

um in that it's a system that basically

right it it data comes in data gets

processed data comes out things happen

um that's a computer it's an information

processing system it's a computer it's a

new it's a new kind of computer it's a

you know we like to say the the the sort

of computers up until now have been what

are called Von noyman machines which is

to say they're deterministic computers

which is they're like you know hyper

literal and they do exactly the same

thing every time and if they make a

mistake it's it's the programmer's fault

uh but they're very limited in their

ability to interact with people and

understand the world

um you know we we think of AI and large

language models as a new kind of

computer a probabilistic computer a

neural network based computer um that

you know by the way is not very accurate

and is you know doesn't give you the

same result every time and in fact might

actually argue with you and tell you

that it doesn't want to answer your

question yeah yeah which makes it very

different in nature than the old

computers um and it makes it get kind of

composability you know the ability to

build things big things out of little

things more

complex right but but the capabilities

are new and different and and valuable

and important because it can understand

language and images and you know all

these do all these things that you you

see when you use domains we could never

solve with deterministic computers we

can now go after right right right yeah

exactly and so I think I think Ben I

think the analogy and I think the

Lessons Learned are much more likely to

be drawn from the early days of the

computer industry or from the early days

of the microprocessor than the early

days of the internet does that does that

sound right I think so yeah I definitely

think so and that doesn't mean there's

no like um boom and bust and all that

because that's just the nature of

Technology you know people get too

excited and then they get too

depressed so there will be some of that

I'm sure uh there will be over buildout

you know potentially of eventually of

chips and power and that kind of thing

um you know we start with the shortage

but but I agree like I think networks

are fundamentally different in the

nature of how they evolved on computers

um and and the kind of just the adoption

curve and all those kinds of things will

be

different yeah so then and this kind of

goes to where how I think the industry

is going to unfold and so this is kind

of my best theory for kind of what

happens from here this kind of this you

know this this giant question of like

you know is the industry going to be a

few God models or you know a very large

number of of models of different sizes

and so forth so the computer like

famously the you know the the original

computers like the original IBM

mainframes you know the big computers um

you know they they were very very large

and expensive um and there were only a

few of them um and the prevailing view

actually for a long time was that's all

there would ever be um and there was

this famous statement by Thomas Watson

Senor who was the creator of IBM you

know which was the dominant company for

the first like you know 50 years of the

of the computer industry um and uh he

said he said he said I I believe this

actually true he said I don't I don't

know I don't know that the world will

ever need more than five computers um

and I think the reason for that it was

literally it was like the government's

going to have two and then there's like

three big insurance companies and then

that's it yeah um who else would need to

do all that maath exactly who yeah who

else would need to who else needs to

keep track of huge amounts of numbers

who who else needs that level of you

know calculation capability it's just

not a relevant you know it's just not

not not a relevant concept and by the

way they were like big and expensive and

so who else can afford them right and

who else can afford all the headcount

required to manage them and maintain

them I mean this in the days I mean

these things were big these things were

so big that You' have an entire building

that got built around a computer right

um and they'd have like they'd famously

have all these guys in white lab coats

literally like taking care of the

computer uh because everything had to be

kept super clean or the computer would

stop working um and so you know it was

this thing where you know today we have

the idea of an AI God model which is

like a big foundation model then you

know then we have the idea of like a god

Mainframe like there there would just be

a few a few of these things and by the

way if you watch old science fiction it

almost always has this sort of conceit

it's like okay there's a big

supercomputer and it either is like

doing the right thing or doing the wrong

thing and if it's doing the wrong thing

you know that's that's often the plot of

the of the science fiction movies is you

have to go in and try to you know figure

out how to fix it or defeat it so sort

of this this idea of like a single top

down thing of course and and that held

for a long time like that held for you

know the first few decades and then you

know even when computers computers

started to get smaller so then you had

so-called mini computers was the next

phase and so that was a computer that

you know didn't cost $50 million instead

it costs you know 500 $500,000 but even

still $500,000 is a lot of money people

aren't putting many computers in their

homes and so it's like midsize companies

can can buy many computers but certainly

people can't and then of course with the

PC they shrunk down to like $2,500 and

then with the smartphone they shrunk

down to

$500 um and then you know sitting here

today obviously you have computers of

every shape size description all the way

down to you know computers that cost a

penny you know you've got a computer in

your thermostat that you know basically

controls the temperature in the room and

it you know probably cost a penny and

it's probably some embedded arm ship

with firmware on it um and there's you

know many billions of those all around

the world you buy a new car today it has

something new cars today have something

on the order of 200 computers in them

maybe maybe more at this point um and so

you you just basically assume with the

chip today sitting here today you just

kind of assume that everything has a

chip in it you assume that everything by

the way draws electricity or has a

battery because it needs to power the

chip and then increasingly you assume

that everything's on the internet

because basically all computers are

assumed to be on the Internet or or they

will be um and so so and so as a

consequence what you have is the

computer industry today is this massive

pyramid and you still have a small

number of like these supercomputer

clusters or these giant mainframes that

are like the god model you know the god

the god the god main frames and then

you've got you know a larger number of

minicomputers you've got a larger number

of PCS you've got a much larger number

of smartphones and then you've got a

giant number of embedded systems um and

it turns out like the computer industry

is all of those things um and you know

what what is a what you know what what

size of computer do you want is based on

what exactly are you trying to do and

who are you and what do you need and so

if if that analogy holds it basically

means actually we are going to have ai

models of every conceivable shape size

description capability um right based on

trained on lots of different kinds of

data at running at very different kinds

of scale very different privacy policies

different you know security policies you

know you're just you're just going to

have like enormous variability um and

variety um and it's going to be an

entire ecosystem and not just a couple

of companies yeah let me see what you

think of that well I think that's right

and I also think that the the other

thing that's interesting about this era

of computing if you look at prior areas

of computing from the Mainframe to the

smartphone um a huge source of lock in

was you know basically

the difficulty of using them so you know

nobody ever got fired for buying IBM

because like you know you had people

trained on them you know people knew how

to use uh the operating system like it

was you know it was just kind of like a

safe Choice due to the massive

complexity of like dealing with a

computer and then even with a smartphone

like the re you know why is the Apple

computer um smartphone so dominant um

you know what makes it so powerful it's

well because like switching off of it is

so expensive and complicated and so

forth it's an interesting question with

AI because AI is the easiest computer to

use by far it speaks English it's like

talking to a person um and so like what

is the lock in there and so are you

completely free to use the size price

Choice speed that you need for your

particular task or are you locked into

the god model

um

and you know I think it's still a bit of

an open question uh but it's it's pretty

interesting and that that that thing

could be very different than prior

Generations

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