The Future of AI Is Amazing

Summary

The speaker enthusiastically discusses the transformative potential of recent advancements in AI, specifically large language models and foundation models. They explain how these models have made significant breakthroughs in areas like creativity, natural language processing, and problem-solving, at a fraction of the cost and time compared to human efforts. The speaker argues that this economic dislocation, similar to previous revolutions like the microchip and internet, will drive a new wave of innovative companies and potentially lead to economically viable artificial general intelligence. The talk invites collaboration between venture capitalists, the tech community, and policymakers to capitalize on these promising developments.

Takeaways

• 🧠 AI has been around for decades and has made significant progress in solving various problems, but its widespread adoption has been limited due to economic factors.
• 🚀 The emergence of large language models (LLMs) and foundation models has brought a significant economic shift, making AI solutions orders of magnitude cheaper and faster than human alternatives.
• 💻 LLMs excel in domains like creativity, natural language understanding, and task assistance, areas traditionally considered uniquely human.
• 💰 The drastic reduction in marginal costs enabled by LLMs is akin to previous platform shifts brought about by the microchip (compute) and the internet (distribution).
• 🏭 While job displacement is a concern, history suggests that if demand is elastic, these technological advancements can drive overall economic growth.
• 🦸‍♀️ The speaker envisions a wave of iconic companies emerging from this AI revolution, similar to the rise of tech giants during the internet era.
• 🌐 LLMs have the potential to revolutionize various domains, including social interactions, creativity, productivity, and even embodied artificial general intelligence (AGI).
• 🤝 Realizing the full potential of this AI wave will require collaboration and partnership across the venture capital, tech, and government sectors.
• ⚡ The speaker expresses excitement about the rapid pace of change and economic opportunities presented by this AI wave.
• 🔮 There is optimism that LLMs can solve problems previously thought unsolvable, paving the way for new applications and business models.

Q & A

What is the main topic discussed in the script?

The main topic discussed is the excitement around the recent wave of large AI models (also called foundation models or state-of-the-art models) and their potential to create a platform shift in the industry, similar to the shifts brought about by the microchip and the internet.

What is the key difference between traditional AI and the current wave of large AI models?

The key difference is the economics. Traditional AI solutions lacked broad market appeal, required significant investment for correctness, and often competed with the human brain's efficiency. However, the current wave of large AI models is more cost-effective, faster, and in some cases, outperforms humans in tasks like creativity, communication, and reasoning.

Can you provide an example illustrating the cost advantage of large AI models?

The script provides the example of creating a Pixar-style image. Using a large AI model, the inference cost is about one-hundredth of a penny, and it takes about a second. In contrast, hiring a graphic artist could cost around $100 per hour or more, making the AI solution orders of magnitude cheaper and faster.

How does the script compare the economic impact of large AI models to previous technological shifts?

The script compares the potential impact of large AI models to the microchip revolution, which brought the marginal cost of compute to near zero, and the internet revolution, which brought the marginal cost of distribution to near zero. It suggests that large AI models could bring the marginal cost of creation to near zero, potentially ushering in a new platform shift.

What are some potential areas where large AI models could have a significant impact?

The script mentions several potential areas, including creativity (generating images, music, voice imitations), natural language reasoning (conversational AI, virtual companions, therapists), and serving as co-pilots for various online tasks and activities.

How does the script address concerns about job dislocation due to AI?

The script argues that, similar to previous technological shifts, if the demand for AI-powered services is elastic (i.e., the demand increases as costs decrease), the total throughput and use of AI could increase, potentially expanding growth and creating new opportunities rather than just displacing jobs.

What future developments does the script envision for large AI models?

The script suggests that there is now a real line of sight to embodied AGI, meaning economically viable artificial general intelligence (AGI) systems that can solve problems that were previously unsolvable due to high costs.

How does the script characterize the potential impact of large AI models on the industry?

The script suggests that large AI models could lead to the fastest growing companies we've seen in the history of the internet, including by the way the internet itself, and that we should get ready for a new wave of iconic companies driven by this technological shift.

What role does the script suggest for various stakeholders in relation to large AI models?

The script mentions the need for partnerships and collaboration among the venture capital community, the tech community, and policymakers in Washington, D.C., suggesting that a concerted effort from these stakeholders will be required to fully realize the potential of large AI models.

How does the script describe the historical context and evolution of AI?

The script provides a brief historical overview, mentioning that AI has been around for about 70 years, and has steadily solved various problems that were initially thought to be challenging for computers, such as expert systems for medical diagnosis, chess, image detection, and robotics. However, the script suggests that the current wave of large AI models represents a significant departure from traditional AI in terms of economics and capabilities.

Outlines

00:00

🤖 AI's Transformative Potential and Past Achievements

This paragraph introduces the topic of artificial intelligence (AI) and its historical developments. It discusses AI's successes over the past 70 years, highlighting its ability to solve problems previously thought to be challenging for computers, such as medical diagnosis, chess, image recognition, and robotics. The speaker emphasizes that AI has added significant value to large companies through applications like search engines and personalization. However, a conundrum existed in the investment community regarding why AI had not yet led to a platform shift akin to the mobile or internet revolutions, despite its impressive capabilities.

05:01

🌊 The Emergence of Large Language Models and Economic Disruption

This paragraph discusses the recent wave of large language models (LLMs) or foundation models, which are software capable of generating text, images, or conversations based on input. The speaker highlights how these models have entered domains previously unexplored by AI, such as creativity, natural language reasoning, and acting as co-pilots for various tasks. Unlike traditional AI solutions, these models have favorable economics due to their broad market appeal, reduced emphasis on absolute correctness, and software-based nature. The speaker provides examples demonstrating the significant cost and time advantages of using LLMs compared to human experts, indicating potential for massive economic disruption akin to the microchip and internet revolutions.

10:01

🚀 The Future of AI and Call for Collaboration

In this concluding paragraph, the speaker expresses excitement about the potential value creation by AI, particularly in domains like social interaction, creativity, productivity, and embodied artificial general intelligence (AGI). The speaker acknowledges the significant challenges and the need for collaboration among venture capitalists, the tech community, and policymakers in Washington, D.C. to navigate this transformative shift successfully.

Keywords

💡AI (Artificial Intelligence)

Artificial Intelligence refers to the development of computer systems capable of performing tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. The video discusses the long history and evolution of AI, its successes in solving complex problems, and its potential for transformative economic impact, particularly with the emergence of large language models (LLMs) and foundation models.

💡Large Language Models (LLMs)

Large Language Models, also known as Foundation Models, are advanced AI systems trained on massive amounts of data to understand and generate human-like text. The speaker highlights LLMs as a game-changing development, capable of tasks like natural language processing, creativity, reasoning, and acting as intelligent assistants or co-pilots across various domains. LLMs are presented as a significant economic driver due to their low marginal costs and broad applicability.

💡Economics

Economics plays a crucial role in the video's narrative. The speaker argues that while AI has achieved remarkable technological feats, its widespread adoption and impact have been limited due to unfavorable economics, such as niche market applications, hardware requirements, and competition with inexpensive human labor. However, LLMs are presented as a paradigm shift, offering orders of magnitude lower costs and faster execution compared to human alternatives, thereby enabling new economic opportunities and potential platform shifts akin to the microchip and internet revolutions.

💡Marginal Cost

Marginal cost refers to the cost of producing one additional unit of a good or service. The video emphasizes that LLMs have brought the marginal cost of creation and reasoning to near zero, making them significantly cheaper and faster than human alternatives for tasks like image creation, language understanding, and reasoning. This dramatic reduction in marginal cost is positioned as a driving force behind the potential economic disruption and platform shift enabled by LLMs.

💡Creativity

Creativity, traditionally considered a uniquely human trait, is presented as an area where LLMs excel. The speaker highlights LLMs' abilities to create images, music, poetry, and engage in creative tasks better than humans. This newfound computational creativity, combined with the low marginal cost of LLMs, is portrayed as a significant economic opportunity and a departure from traditional AI limitations.

💡Platform Shift

The video draws parallels between the potential impact of LLMs and previous platform shifts, such as the microchip revolution and the internet revolution. A platform shift refers to a technological advancement that fundamentally alters the economic landscape, enabling new business models, industries, and companies to emerge. The speaker argues that the economics of LLMs have the potential to catalyze a similar platform shift, ushering in a new wave of innovative companies and reshaping various sectors.

💡Embodied AGI

Embodied AGI (Artificial General Intelligence) refers to the concept of creating economically viable, embodied AI systems with broad capabilities akin to human intelligence. The speaker suggests that LLMs provide a line of sight to achieving embodied AGI, meaning AI systems that can solve real-world problems in a cost-effective manner, unlike previous attempts that were too expensive or impractical.

💡Venture Capital

Venture capital, the investment of money into early-stage, high-potential companies, is mentioned as a driving force behind the development and commercialization of LLM technologies. The speaker, likely a venture capitalist, expresses excitement about the rapid growth of LLM-powered companies, suggesting that venture capitalists see immense potential in this space.

💡Co-pilot

The term 'co-pilot' is used in the video to describe the role of LLMs as intelligent assistants capable of aiding humans in various tasks and domains. LLMs are portrayed as versatile co-pilots that can augment human abilities, understand context, and provide valuable assistance across a wide range of activities, from writing and analysis to coding and problem-solving.

💡Job Dislocation

Job dislocation refers to the potential displacement of human workers due to technological advancements like LLMs. The speaker acknowledges this concern but draws parallels to previous technological revolutions, suggesting that if demand for LLM-enabled services is elastic (i.e., grows with increased accessibility and lower costs), the overall economic growth could offset job losses by creating new opportunities and industries.

Highlights

AI has been around for over 70 years, solving many problems previously thought impossible for computers, such as expert systems for medical diagnosis, beating humans at chess, image detection, and robotics.

Despite AI's success, there hasn't been a platform shift like mobile or the internet because the economics were not favorable due to niche markets, the need for absolute correctness in some use cases, hardware requirements, and competition with the efficient and cheap human brain.

The emergence of large language models or foundation models is a game-changer because they can handle tasks like creativity, natural language reasoning, and serving as co-pilots for various online tasks, which traditional AI could not.

These models are much cheaper and faster than humans for tasks like image creation, language understanding, and reasoning, with costs as low as one-hundredth of a penny and near-instantaneous processing.

When marginal costs have dropped significantly in the past, such as with the microchip (compute) and the internet (distribution), it led to platform shifts and the creation of iconic companies like IBM, HP, Amazon, Google, and Salesforce.

These large models bring the marginal cost of creation to near-zero, suggesting a potential platform shift and the rise of new iconic companies.

The demand for these models' capabilities is expected to be elastic, leading to an expansion of growth rather than job dislocation, similar to the effects of the microchip and internet revolutions.

There are glimpses of the potential impacts, such as changes in the social order, real monetization of creativity, productivity improvements through co-pilot capabilities, and the possibility of economically viable embodied artificial general intelligence (AGI).

The speaker emphasizes the need for partnership between the venture capital community, the tech community, and policymakers in Washington, D.C., to harness the potential of this transformative technology.

Transcripts

okay so I'm going to be uh covering this

AI stuff everybody's talking about so

I'm just going to spend about 10 minutes

to talk about why we're so excited about

it and then um Senator Todd young is

going to be up here and we're going to

have a discussion so I probably took my

first AI course in the late '90s the

stuff has actually been around with us

for a very long time uh and during that

you know 70 years um It's You Know by

every metric has been a huge success

it's been up and to the

right we've solved a number of problems

we didn't think computers were good at

solving so for example expert systems in

the 50s and 60s we used for medical

diagnosis we got very good at beating

Russians at chess in the 80s and 90s

we're good at image detection we're good

at robotics like we've solved a lot of

problems that originally we thought you

know computers were just like large

calculators on top of just solving these

problems for decades a lot of the

solutions are actually better than than

than humans like we're better than

humans at handwriting detection we're

better than humans at at uh identifying

objects and

images and and with all of this magic

we've actually been able to add a lot of

value to large companies right every

time you go to Google and you get a

search this is using AI anytime you get

some personalization this is AI right so

this stuff is like magic right it's been

around for a long time it's solved all

these problems

so there's been this huge conundrum in

the investment community and the

conundrum is the following if this stuff

is so magic and it solves all of these

problems why haven't we seen a platform

shift in the same way we saw a shift

with mobile or with the internet like

why hasn't this happened and we've

actually done a lot of research with

this as a firm and the answer is is that

even though the capabilities have been

fantastic like I talked about the

economics just haven't been there in the

same way there's a number of reasons for

this I won't be exhaustive but I'll just

cover a few of them so one of them a lot

of the solutions just tend to apply to

Niche markets there's not a lot of broad

Market appeal the second one is probably

the most important in Nuance which is a

lot of the use cases that we apply it to

correctness is really important like

robotics but getting something

absolutely correct is very very hard and

requires a tremendous amount of

investment so a number of the solutions

require hardware and finally you know

the the competition for AI it's not it's

not another computer it's actually a

human brain and you know maybe it'll be

better maybe it's not as good the human

Rin is incredibly efficient and it's

incredibly cheap and one of the best

examples of this is is autonomous

vehicles or Robo taxi so when I joined

Stanford to do my PhD in 2003 Sebastian

thrud had just won the DARPA Grand

Challenge right so he had driven a van

autonomously across the desert and won

this and we were like great news

exciting like autonomous vehicle is a

solved problem back in 2003 now if we go

20 years later we've invested 75 billion

dollars as an industry and while we do

have autonomous vehicles on the road and

they're great and they're solving real

problems the unit economics are still

worse than say Uber and lift because

they're competing against the human

brain so while this is very important

technology to date it's really remained

in the realm of large companies right

that can sync these types of

Investments so the AI learnings of the

last couple of decades is not that

technology can't be built or even that

we can't monetize it we're actually good

at all of that is that this is very hard

for startups to build businesses

around so the reason that we're so

excited and the industry is changing so

quickly is this wave is very very

different on exactly this

issue

economics so when I talk about kind of

this wave I'm talking about the

emergence of what we call large models

or Foundation models or state-of-the-art

models these are pieces of software that

you put in text or you put in an image

and outc comes something out can come an

image or text or a conversation right

there're these kind of like very very

smart pieces of software that you can

ask questions and they provide answers

too and they've already entered a number

of problem domains that we just haven't

cracked in computers and certainly in AI

right the AI has not been able to do

this before so for example creativity

you know these models are better than

humans at creating images or creating

music or creating um you know voice

imitations it actually turns out they're

great at natural language reasoning as

well right they're great

conversationalists they're great friends

they're great romantic Partners they're

great therapists um and they also have

now serving this a thing which we call

co-pilot which is this catchall phrase

that they're pretty good at like mean

online tasks and by mean I mean average

right so if it's something that you do a

lot of it can kind of get the hang of it

and do it as

well now remember when I said like

traditional AI the economics didn't work

and there was a set of reasons so those

reasons just don't apply to this set of

tasks right like these markets are

enormous like whatever video games and

movies alone are like a half trillion

dollar market

in many of these use cases correctness

just isn't an issue right I mean what

like there's no formal notion of

correctness of creating like a fantasy

image or like creating like a sonnet or

something like that the use cases are

primarily software and and the last

point is the one that like I couldn't

have predicted and it's the most

surprising that it turns out that for

these tasks the one that we think of as

very human like you know communication

and social interaction and creativity

the computers are far cheaper and far

better than than than humans

are I want to give you a very specific

example it may be silly but it actually

generalizes so let's just say that I

Martine wanted to create a a picture of

me as a a a Pixar character so if I had

one of these AI models do it the actual

inference cost the cost of doing that is

about one/ 100th of a penny and it takes

about a second and it and I mean this is

what we did here and this is the quality

that you get if you were to compare that

to hiring a graphic artist a graphic

artist is what let's say 100 bucks in an

hour it's it actually is much

more I've gone down this road before so

the AI you know it's just not a little

bit better it's not like 20% better it's

for orders of magnitude cheaper and

faster this isn't limited to images this

is also the true for like any sort of

language understanding so like take a

complex legal document I can take a

complex legal document I can feed it

into an llm and I can ask questions if

you compare the analog would be me like

whatever like working with my my lawyer

so like the lawyer would have to read it

would have to understand it you know I

don't know how much you know you know

the average lawyer cost is but like it's

you know 500 tends to be pretty standard

so again to use an llm is four to five

orders of actitude cheaper and

faster and it's exactly because of this

that we as Venture investors and we on

the private Market side are so exciting

because we're seeing the fastest growing

companies we've seen in the history of

the internet including by the way the

internet itself and this is by measured

for revenue or the number of users Etc

right it's exactly economic dislocations

that create new startups not just new

technology so if you take a step back

historically when marginal costs have

dropped this

much this is what creates platform

shifts and has changed the industry

entirely it's happened twice you know

that pretty concretely I want to walk

through both of those so the first one

was compute so in the creation of the

microchip it brought the marginal cost

of compute to zero like so before you

had the microchip calculations were done

by hand right so it was like people

doing logarithm tables you know in in

large rooms and then ANC was introduced

which was forwarders of magnitude faster

and then you had the computer Revolution

and here came you know IBM and HP and

everything

else the internet brought down the

marginal cost of distribution to zero

right so before you like whatever You'

send you know a box or you'd send a

letter and then the price per bit

dropped and you could send it over the

Internet by the way OS forward is a

magnitude Improvement and this ushered

in kind of the internet Revolution right

this is kind of Amazon and Google and

Salesforce so it's pretty clear if you

just take the fundamental economic

analysis that these large models bring

the marginal cost of creation to zero

like creating that image and language

understanding like reasoning over those

documents and these are very very broad

areas that they can be applied

to now whenever we talk about economics

we always kind of talk about Job

dislocation as well it's very very

important especially with an economic

dislocation of this size we can learn

from the last two Epoch both the

microchip and the internet in that if

demand is elastic so for example the

demand for compute seems kind of

unlimited and the demand for

distribution seems kind of un limited

that even though the costs drop the

total throughput the total use increases

by a lot because it becomes more

accessible and so rather than removing

drops or removing value these tends to

EXP expand growth like the internet

almost certainly expanded growth uh in

the United States and so we think the

same thing is going to happen here as

well so get ready for a new wave of

iconic companies it's almost certainly

going to happen it's not just the

technology which is solving problems

that have never been solved before but

the economic case is absolutely there um

you know when this happened with the

internet like we didn't really know what

was going to be on the other side of it

like we couldn't have predicted Google

and we couldn't have predicted Yahoo but

we knew it was going to be something and

it's kind of one of those moments but we

have some glimpses right we know like

the social order is changing we know

like this is a very real use case that's

monetizing today and people are

using we absolutely think that the

creativity itself is going to

change you know productivity these kind

of mean workers like this is happening

as well and if you really want some

prognostication for this is all going I

mean for the first time I say there is

actually real line of sight to embodied

AGI and by embodied AGI means something

that's economically viable so you don't

have a bunch of robots that can't work

because they're too EXP expensive like

actually like solving problems that we

to have solved um and so like with that

listen there's a lot to do this is going

to be a major value driver I think it

requires a ton of partnership from the

VC Community from The Tech Community and

certainly from DC so we appreciate all

of you being here today and and your

patience with my

talk