The Future of AI Is Amazing
Summary
TLDRThe 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
🤖 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.
🌊 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.
🚀 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.
Mindmap
Keywords
💡AI (Artificial Intelligence)
💡Large Language Models (LLMs)
💡Economics
💡Marginal Cost
💡Creativity
💡Platform Shift
💡Embodied AGI
💡Venture Capital
💡Co-pilot
💡Job Dislocation
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
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