The AI opportunity: Sequoia Capital's AI Ascent 2024 opening remarks

Summary

The transcript discusses the rapid evolution of AI, highlighting its three distinct capabilities: creation, reasoning, and humanlike interaction. It draws an analogy with the cloud transition, suggesting AI's potential to replace services with software, impacting business models significantly. The script emphasizes AI's role in enhancing productivity and quality of life, predicting a future where AI becomes an integral part of daily operations, potentially leading to the rise of one-person companies. The speakers express excitement about AI's transformative power in various sectors, including customer support and legal services.

Takeaways

• 🚀 AI is transitioning from a concept to practical applications, with three distinct capabilities: creation, reasoning, and humanlike interaction.
• 📈 The AI industry has rapidly grown, with generative AI alone estimated to have around $3 billion in revenues, indicating a significant market shift.
• 🤖 AI is expected to replace services with software, potentially impacting hundreds of billions of dollars in revenue and creating new business models.
• 🌐 The advancement of AI is likened to the cloud and mobile transitions,预示着巨大的经济增长和市场机会.
• 🔄 AI is progressing through stages of human-tool interaction to human-machine assistant collaboration, and finally to human-machine network systems.
• 💡 AI's role in society is primarily seen as a productivity revolution, with the potential to reduce costs and increase efficiency in critical areas such as education and healthcare.
• 📊 Investment in AI is currently skewed towards foundational models, with less focus on application development, indicating a need for more practical applications.
• 🔧 AI applications are still in the early stages, with user retention and expectations needing to be addressed for wider adoption.
• 🧠 There is a growing focus on improving AI's reasoning and planning capabilities, moving beyond pattern recognition to more complex cognitive tasks.
• 🛡️ Ensuring reliability and trust in AI applications, especially in high-stakes industries, is becoming increasingly important with new tools and techniques being developed.
• 🌟 The future of AI envisions a shift from individual AI tools to complex, interconnected networks that can optimize and improve entire business processes.

Q & A

What was the primary objective of the AI Ascent event?

The primary objective of the AI Ascent event was to learn about the current state of AI and to meet people who can be helpful in the journey of understanding and utilizing AI technology.

How has the perception of AI evolved over the past year?

Over the past year, the perception of AI has evolved from viewing it as a magic box capable of doing amazing things to recognizing its distinct capabilities, such as creation, reasoning, and humanlike interaction, and understanding its potential for practical applications.

What are the three distinct capabilities that AI brings to various applications?

The three distinct capabilities that AI brings to various applications are the ability to create (generative AI), the ability to reason (one-shot or multi-step agentic type reasoning), and the ability to interact in a humanlike capacity.

How does the AI industry's growth compare to the cloud transition in the past?

The AI industry's growth is analogous to the cloud transition, where the cloud software market grew massively from $6 billion to $400 billion in revenue over 15 years, indicating a significant shift in the technology landscape and business models.

What is the significance of AI's ability to reason?

The significance of AI's ability to reason is that it allows software to perform tasks that were previously not possible, such as complex problem-solving and decision-making, essentially covering both the creative (right brain) and analytical (left brain) aspects of human cognition.

How has AI already impacted the customer support industry?

AI has already significantly impacted the customer support industry by automating customer service inquiries, equivalent to the work of hundreds of full-time agents, thereby improving efficiency and reducing costs.

What is the current state of funding in the generative AI layer cake model?

In the generative AI layer cake model, funding has been uneven, with more capital going towards the foundation models and less towards the application layer, indicating a focus on developing foundational technologies before building applications.

What are some challenges faced by AI applications in terms of user retention?

Some challenges faced by AI applications in terms of user retention include the gap between user expectations and the reality of the AI's capabilities, leading to disappointment when the AI does not perform tasks as reliably or effectively as expected.

What is the prediction for AI applications in 2024?

The prediction for AI applications in 2024 is that they will transition from being co-pilots or helpers to becoming more like coworkers, capable of taking the human out of the loop entirely in domains such as software engineering and customer service.

How does the concept of a productivity revolution relate to AI?

The concept of a productivity revolution relates to AI in that it is expected to significantly reduce the cost of producing goods and services, enable more efficient processes, and allow humans to do more with less, ultimately leading to improvements in critical areas of society.

What is the long-term future of AI in terms of company building?

The long-term future of AI in terms of company building is that AI will enable the rise of the one-person company, where individuals can tackle more problems and achieve greater productivity by leveraging AI systems that function like neural networks, optimizing and managing various aspects of the business.

Outlines

00:00

🚀 Introduction to AI Ascent and its Impact

The speaker, Pack Rady, introduces himself and his team at SEOA, setting the stage for the AI Ascent event. He emphasizes the objective of learning and meeting influential people in the AI field. The speaker reflects on the past year's journey through the hype cycle of AI, highlighting the transition from inflated expectations to the plateau of productivity. He identifies three distinct AI capabilities: creation (generative AI), reasoning, and humanlike interaction, which have significant implications for business models. The analogy of the cloud transition is used to illustrate the potential of AI to replace services with software, suggesting a massive growth opportunity ahead.

05:00

🌐 Historical Context and AI's Future

The speaker provides a historical overview of technological waves, from the 1960s to the present, emphasizing how each wave built upon the previous one. He discusses the evolution from silicon-based transistors to cloud computing and mobile devices, and how AI, although an old concept, is now becoming practical and transformative due to recent technological advancements. The speaker asserts that we are at the beginning of a significant value creation opportunity with AI, comparing it to the cloud and mobile transitions, and predicts that the next couple of decades will be dominated by AI.

10:03

🤖 AI's Current State and Diverse Applications

The speaker, Sonia, discusses the current state of AI, noting its rapid development and integration into various fields. She highlights the impact of AI in customer service, legal services, and software engineering, emphasizing the shift from theoretical applications to practical, market-ready solutions. Sonia also touches on the increasing quality of life due to AI advancements, such as virtual AI avatars. She reflects on the funding environment, noting a trend of more investment in foundational models rather than applications. Despite the impressive growth and user numbers, she points out that there is still a gap between expectations and reality, indicating a need for further development and improvement in AI applications.

15:04

💡 Predictions for AI's Evolution and Challenges

The speaker delves into predictions for AI's future, emphasizing the transition from AI as a co-pilot to a full-fledged coworker. He anticipates AI taking on higher-level cognitive tasks and becoming more reliable in critical applications. The speaker also discusses the importance of AI prototypes moving into production, highlighting the need for focus on latency, cost, model ownership, and data privacy. He concludes by acknowledging the pressure and high expectations for AI applications as they transition into real-world use.

20:06

📈 AI as a Productivity Revolution

The speaker, Constantine, positions AI primarily as a productivity revolution, drawing parallels with historical technological revolutions. He discusses the progression from human tools to machine networks, using the example of the sickle to the mechanical reaper to the modern combined harvester. Constantine envisions a future where AI systems work together in complex networks, leading to significant cost reduction and increased productivity. He predicts that AI will help drive down costs in crucial areas such as education, healthcare, and housing, and enable the concept of a 'one-person company,' where individuals can achieve more through AI-augmented capabilities.

25:07

🌟 The Future of AI in Business and Society

In the final paragraph, the speaker discusses the broader implications of AI for business and society. He envisions a future where AI not only integrates into specific processes but becomes a foundational layer for entire companies to function like neural networks. The speaker predicts the rise of the 'one-person company,' where individuals can leverage AI to tackle more problems and create a better society. He concludes by emphasizing the role of the audience in shaping this future, encouraging the group to explore how they can use AI to abstract away complexity and build powerful solutions for the future.

Keywords

💡AI Ascent

AI Ascent refers to the conference or event where the discussions in the transcript are taking place. It is a gathering focused on the advancements and applications of artificial intelligence, aiming to bring together experts and enthusiasts to share insights and explore opportunities in the AI field.

💡Generative AI

Generative AI refers to the subset of artificial intelligence that has the capability to create new content such as images, text, video, and audio. This capability represents a significant leap from traditional software, which has typically been focused on processing existing data rather than creating new content.

💡Reasoning

In the context of AI, reasoning refers to the ability of an artificial intelligence system to think logically and make decisions in a manner similar to human cognition. This includes complex problem-solving, planning, and executing tasks, which is a departure from simple data processing and marks a significant advancement in AI capabilities.

💡Hype Cycle

The Hype Cycle is a model that describes the typical pattern of expectations surrounding a new technology as it emerges and gains public attention. It includes stages such as the 'peak of inflated expectations,' the 'trough of disillusionment,' the 'slope of enlightenment,' and finally the 'plateau of productivity.' The concept is used to illustrate the progression of AI from being seen as a 'magic box' to a more mature and practical technology.

💡Cloud Transition

The Cloud Transition refers to the shift in the technology landscape where businesses and individuals began to adopt cloud computing services over traditional, locally-hosted software solutions. This transition has led to significant changes in how technology is consumed, managed, and developed, with a focus on accessibility, scalability, and cost-effectiveness.

💡Product Market Fit

Product Market Fit is a term used in business to describe a situation where a product satisfies a strong market demand, and is well-received by its target audience. It's a critical point for any new product or service, indicating that it has found the right combination of features and value to appeal to its intended market.

💡Enterprise Applications

Enterprise Applications refer to software solutions that are designed to meet the needs of businesses, often by automating, optimizing, or managing business processes. These applications are typically used by organizations to improve efficiency, productivity, and decision-making.

💡Reliability

In the context of AI, reliability refers to the consistency and dependability of an AI system's performance. It is a critical factor for AI applications, especially in high-stakes environments like healthcare or defense, where the consequences of errors can be significant.

💡Inference

Inference in AI refers to the process of using a trained model to make predictions or decisions based on new data. It is the act of deriving conclusions from the information that the AI system has learned during its training phase.

💡One-Person Company

The concept of a 'One-Person Company' refers to a business model where a single individual is able to run and manage an entire company, often leveraging advanced technologies to automate tasks, scale operations, and increase productivity.

Highlights

AI Ascent conference aims to learn and connect with people in the AI field.

The past year has seen AI move from hype to practical applications, with a focus on generative AI, reasoning, and human-like interaction.

AI's three distinct capabilities include creation (generative AI), reasoning, and human-like interaction, which can be integrated into various applications.

The AI industry has experienced a shift from the peak of inflated expectations to the plateau of productivity.

The cloud transition analogy suggests AI's potential to replace services with software, with a starting point in the tens of trillions.

AI is expected to be the theme of the next 10 to 20 years, marking a significant value creation opportunity.

Chat GPT's release marked a whirlwind of change in the AI field, with rapid advancements and shifting landscapes.

AI has found product-market fit in customer support and legal services, automating jobs and changing work dynamics.

AI is not only about revolutionizing work but also about increasing the quality of life through various applications.

Generative AI is estimated to have generated around $3 billion in revenues in its first year, indicating a strong market presence.

The funding environment for AI has been uneven, with more investment in foundational models than in application development.

AI's potential for cost reduction and increased productivity could have significant economic implications.

AI's evolution from tools to machine networks is expected to transform various sectors, including software development and writing.

The future of AI involves generalization within computing, moving from storing pixels to understanding and generating concepts.

AI's advancements are expected to drive down costs in critical areas such as education, healthcare, and housing.

The rise of AI could lead to the one-person company, enabling individuals to tackle more problems and create a better society.

The conference emphasizes the importance of community and collaboration in shaping the future of AI and its impact on society.

Transcripts

my name is pack Rady I'm one of the

members of team seoa I'm here with my

partners Sonia and Constantine who will

be your MC's for the day and along with

all of our partners at seoa we would

like to welcome you to AI

Ascent there's a lot going on in the

world of AI we have an objective to

learn a few things while we're here

today we have an objective to meet a few

people who can be helpful on our journey

while we're here today and hopefully

we'll have a little bit of fun so just

to frame the

opportunity what is it well a year ago

it felt like this magic box that could

do wonderful amazing things I think over

the last 12 months we've sort of been

through this contracted form of the hype

cycle we had the peak of inflated

expectations we had the trough of

disillusionment we're crawling back out

into the plateau of productivity and I

think we've realized that what what llms

what AI really brings to us today are

three distinct capabilities that can be

woven into a wide variety of magical

applications the first is the ability to

create hence the name generative AI you

can create images you can create text

you can create video you can create

audio you can create all sorts of things

not something software has been able to

do before so that's pretty cool the

second is the ability to reason could be

one shot could be multi-step agentic

type reasoning but again not something

software's been able to do

before because it can create because it

can reason we've sort of got the right

brain and the left bra covered which

means that software can also for the

first time interact in a humanlike

capacity and this is huge because this

has profound business model implications

that we're going to mention on the next

slide so what a lot of times we try to

Reason by analogy when we see something

new and in this case the best analogy

that we can come up with which is

imperfect for a million reasons but

still useful is the cloud transition

over the last 20 years or so that was a

major tectonic shift in the technology

landscape that led to new business

models new applications new ways for

people to interact with technology and

if we go back to some of the early days

of that cloud transition this is Circa

about

2010 the entire Pi the entire Global Tam

for software was about 350 billion of

which this tiny slice just $6 billion

doar is cloud software fast forward to

last year the Tam has grown from about

350 to 650 but that slice has become 400

billion of Revenue that's a 40% ker over

15 years that's massive growth now if

we're going to Reason by

analogy Cloud was replacing software

with software because of what I

mentioned about the ability to interact

in a humanlike

capability one of the big opportunities

for AI is to replace services with

software and if that's the T that we're

going after the starting point is not

hundreds of billions the starting point

is possibly tens of trillions

and so you can really

dream about what this has a chance to

become and we would posit and this is a

hypothesis as everything we say today

will be we would posit that we are

standing at the precipice of the single

greatest value creation opportunity

mankind has ever

known why now one of the benefits of

being part of SEO is that we have this

long history and we've gotten to sort of

study the different waves of technology

and understand how they interact and

understand how lead us to the present

moment we're going to take a quick trip

down memory lane so

1960s our partner Don Valentine who

founded SEO was actually the guy who ran

the goto market for Fairchild

semiconductor which gave Silicon Valley

its name with silicon based transistors

we got to see that happen we got to see

the

1970s when systems were built on top of

those chips we got to see the 1980s when

they were connected up by by networks

with PCS as the endpoint and the Advent

of package software we got to see the

1990s when those Networks Works went

public facing in the form of the

internet change the way we communicate

change the way we consume we got to see

the 2000s when the internet matured to

the point where it could support

sophisticated applications which became

known as the cloud and we got to see the

2010s where all those apps showed up in

our pocket in the form of mobile devices

and change the way we work and so why do

we bother going through this little

build well the point here is that each

one of these waves is additive with what

came before and the idea of AI is

nothing new it dates back to the 1940s I

think neural Nets first became an idea

in the

1940s but the ingredients required to

take AI from idea from dream into

production into reality to actually

solve real world problems in a unique

and compelling way that you can build a

durable business around the ingredients

required to do that did not exist until

the past couple of years we finally have

compute that is cheap and plent we have

networks that are fast and efficient and

reliable seven of the 8 billion people

on the planet have a supercomputer in

their pockets and thanks in part to

covid everything has been forced online

and the data required to fuel all of

these delightful experiences is readily

available and so now is the moment for

AI to become the theme of the next 10

probably 20 years and so we we we have

as strong conviction as you could

possibly have in a hypothesis that is

not yet proven that the next couple of

decades are going to be the going to be

the time of

AI what shape would that opportunity

take again we're going to analogize to

the cloud transition and the mobile

transition these logos on the left side

of the page those are most of the

companies born as a result of those

transitions that got to a billion

dollars plus of Revenue the list is not

exhaustive but this is probably 80% or

so of the companies formed in those

transitions that got to a billion plus

of Revenue not valuation Revenue the

most interesting thing about this slide

is the right side and it's not what's

there it's what isn't there the

landscape is wide open the opportunity

set is

massive we think if we were standing

here 10 or 15 years from

today that right side is going to have

40 or 50 logos in it chances are it's

going to be a bunch of the logos of

companies that are in this room this is

the opportunity this is why we're

excited and with that I will hand it off

to

Sonia

[Applause]

than wow what a year chat GPT came out a

year and a half ago I think it's been a

whirlwind for everybody here it probably

feels like just about all of us have

been going non-stop with the ground

shifting under our feet constantly so

let's take a pause zoom out and take

stock on what's happened so far

last year we were talking about how AI

was going to revolutionize all these

different fields and provide amazing

productivity gains a year later it's

starting to come into

Focus who here has seen this tweet from

Sebastian at Clara show

fans um it's pretty incredible Clara is

now using open aai to handle two-thirds

of customer service inquiries they've

automated the equivalent of 700

full-time agents jobs we think you know

there are tens of millions of call

center agents globally and one of the

most most exciting areas where we've

already seen AI find product Market fit

is in this customer support

Market Legal Services a year ago the law

was considered one of the least Tech

forward Industries one of the least

likely to take risks uh now we have

companies like Harvey that are

automating away a lot of the work that

lawyers do from day-to-day grunt work

and drudgery all the way to more

advanced

analysis or software engineering I'm

sure a bunch of people in this room have

seen some of the demos floating around

on Twitter recently it's remarkable that

we've gone from a year ago AI

theoretically writing our code uh to

entirely self-contained AI software

engineers and I think it's really

exciting the future is going to have a

lot more

software and AI isn't all about

revolutionizing work it's already

increasing our quality of life now the

other day I was in a zoom with Pat and I

noticed that he looked a little bit

suspicious uh didn't speak the entire

time and having reflected on it more I'm

pretty sure that he actually sent in his

virtual AI Avatar um was actually

hitting the gym which would explain a

lot hi this is Pat Grady this is

definitely me I'm definitely here and

not at the gym right

now and it even gets the facial

scrunches right this is courtesy of hen

it's it's pretty amazing um this this is

how far Technologies come in a year it's

it's just it's scary to think about um

it's scary and exciting to think about

how this all plays out in the coming

decade um all getting a

two years ago uh when we thought that

generative AI might usher in the next

great technology shift we didn't know

what to expect would real companies come

out of it would real Revenue materialize

I think the sheer scale of user poll and

revenue momentum has surprised just

about everybody uh generative AI we

think is now clocking in around $3

billion doll of revenues in Aggregate

and that's before you count all the

incremental revenue generated by the

Fang companies and the cloud providers

in AI

to put 3 billion in context it took the

SAS Market nearly a decade to reach that

level of Revenue generative AI got there

it's first year out the gate so the rate

and the magnitude of the C change make

it very clear to us that generative AI

is here to

stay and the customer pull in AI isn't

restricted to one or two apps it's

everywhere I'm sure everyone's aware of

how many users chat GPT has but when you

look at the revenue and the usage

numbers for a lot of AI apps both

consumer companies and Enterprise

companies startups and incumbents uh

many AI products are actually striking a

cord with customers and starting to find

product Market fit across Industries and

so we find the diversity of use cases

that are starting to hit really

exciting the number one thing that has

surprised me at least about the funding

environment over the last year has been

how uneven the share of funding has been

if you think of generative AI as a layer

cake where you have Foundation models on

the bottom uh you have developer tools

and infro above and then you have

applications on top a year ago we had

expected that there would be a Cambrian

explosion in the application layer due

to the new enabling technology in the

foundation layer instead we've actually

found that new company formation in

capital has formed in an inverse pattern

more and more Foundation models are

popping up and raising very large

funding rounds while the application

layer feels like it is just getting

going our partner David is right here uh

and posed a thought-provoking question

last year with his article ai's $200

billion question if you look at the

amount that at the amount of money that

companies are pouring into gpus right

now we spent about $50 billion doar on

Nvidia gpus just last year and

everybody's assuming if you build it

they will come AI is a field of dreams

but so far remember on the previous

slide we've identified about3 billion

dollars or so of AI Revenue plus change

from the cloud providers we've put 50

billion into the ground plus Energy Plus

data center costs and more we've gotten

three out and to me that means the math

isn't mathing yet uh the amount of money

it takes to build this stuff has vastly

exceeded the amount of money coming out

so far so we got some very real problems

to fix

still and even though the usage and uh

even though the revenue and the user

numbers in AI look incredible the usage

data says that we're still really early

and so if you look at for example the

ratio of daily to monthly active users

or if you look at one month retention

generative AI apps are still falling far

short of their mobile peers to me that

is both a problem and an opportunity

it's an opportunity because AI right now

is a once a week once a month kind of

tinkery phenomenon for the most part for

people but we have the opportunity to

use AI to create apps that people want

to use every single day of their

lives when we interview users one of the

biggest reasons they don't stick on AI

apps is the gap between expectations and

reality so that magical Twitter demo

becomes a disappointment when you see

that the model just isn't smart enough

to reliably do the thing that you asked

it to do the good thing is with that $50

billion plus of GPU spend last year we

now have smarter and smarter base models

to build on and just in the last month

we've seen Sora we've seen Claud 3 we

saw grock over the weekend and so as the

level of intelligence of the Baseline

Rises we should expect ai's product

Market fit to accelerate so unlike in

some markets where the future of the

market is very unclear uh the good thing

about AI is you can draw a very clear

line to how those apps will get

predictably better and

better let's remember that success takes

time we said this at last year's aent

and we'll say it again if you look at

the iPhone some of the first uh some

first apps in the V1 of the App Store

were the beer drinking app or the light

saer app or the flip cup app or the the

flashlight kind of the fun lightweight

demonstrations of new technology those

eventually became either native apps uh

aka the flashlight Etc or utilities and

gimmicks

um the iPhone came out in 2007 the App

Store came out in 2008 it wasn't until

2010 that you saw Instagram and door

Dash uh 2013 so it took time for

companies to discover and harness the

net new capabilities of the iPhone in

creative ways that we couldn't just

imagine yet we think the same thing is

playing out in

AI we think we're already seeing a peak

into what some of those next legendary

companies might be here are a few of the

ones that have captured our attention

recently but I think it's much broer

than the the set of use cases on this

page as I mentioned we think customer

support is one of the first handful of

use cases that's really hitting product

Market fit in the Enterprise as I

mentioned with the Clara story I don't

think that's an exception it's the rule

I think that is the rule AI friendship

has been one of the most surprising

applications for many of us I think took

a few months of thinking for us to wrap

our uh our heads around but I think the

user and the usage metrics in this

category imply very strong user love um

and then horizontal Enterprise knowledge

we'll hear more from glean and dust

later today we think that Enterprise

knowledge is finally starting to be

become

unlocked so here are some predictions

for what we'll see over the coming year

prediction number one 2024 is the year

that we see real applications take us

from co-pilots that are kind of helpers

on the side and suggest things to you

and help you to agents that can actually

take the human out of the loop entirely

AI that feels more more like a coworker

than a tool we're seeing this start to

work in domains like software

engineering um customer service and

we'll hear more about this topic today I

think both Andrew in and Harrison Chase

are playing this PE on

it prediction number two one of the

biggest knocks against llms is that they

seem to be paring the statistical

patterns in text and aren't actually

taking the time to reason and plan

through the tasks at hand that's

starting to change with a lot of new

research um like inference time compute

and game gameplay style value iteration

like what happens when you give the

model the time to actually think through

what to do we think that this is the uh

this is a major research thrust for many

of the foundation model companies and we

expect it to result in AI That's more

capable of higher level cognitive tasks

like cogn like uh planning and reasoning

over the next year and we'll hear more

about this later today from noan Brown

of open

AI prediction number three we are seeing

an evolution from fun consumer apps or

prosumer apps where we don't really care

if the AI says something wrong or crazy

occasionally uh to real Enterprise

applications where the stakes are really

high like hospitals and defense the good

thing is that there's different tools

and techniques emerging to help bring

these llms sometimes into the 59

reliability range from rhf to prompt

chaining to Vector databases and I'm

sure that's something that you guys can

compare notes on later today I think a

lot of folks in this room are doing

really interesting things to make llms

more reliable in

production and finally 2024 is the year

that we expect to see a lot of AI

prototypes and experiments go into

production and what happens when you do

that that means latency matters that

means cost matters that means you care

about model ownership you care about

data ownership and it means we expect

the balance of compute to begin shifting

from pre-training over to inference so

2024 is a big year there's a lot of

pressure and expectations built into

some of these applications as they

transition in production and it's really

important that we get it

right with that I'll transition to

Constantine who will help us dream about

AI over an even longer time

Horizon thank you Sonia and thank you

everyone for being here today Pat just

set up the so what why is this so

important why are we all in the room and

Sonia just walked us through the what

now where are we in the state of AI this

section is going to be about what's next

we're going to take a step back and

think through what this means in the

broader concept of technology and

Society at

large so there are many types of

Technology Revolution there are

communication revolutions like telefony

there are Transportation revolutions

like the locomotive there are

productivity revolutions like the

mechanization of food

Harvest we believe that AI is primarily

a productivity Revolution and these

revolutions follow a pattern it starts

with a human with a tool that

transitions into a human with a machine

assistant and eventually that moves into

a human with a machine Network the two

predictions that we're going to talk

about in this section both relate to

this concept of humans working with

machine networks let's look at a

historical example the sickle has been

around as a tool for the human for over

10,000 years the mechanical reaper which

is a human and a machine assistant was

invented in 1831 a single machine system

uh being used by a human Today We Live

in an era where we have a combined

Harvester combined Harvester is tens of

thousands of machine systems working

together as a complex

Network we're starting to use language

in AI to describe this language like

individual machine participants in the

system might be called an agent we're

talking about this quite a bit today uh

the way the topology and the way that

the information is transferred between

these agents we're starting to talk

about as reasoning for example in

essence we're creating very complicated

layers of abstraction Above The

Primitives of

AI I'll talk about two examples today

two examples that we're experiencing

right in front of us in knowledge work

the first is software so software

started off as a very manual Pro process

here's a love who wrote logical

programming uh with pen and paper was

able to do these computations but

without the assistant of a

machine we've been living in an era

where we have significant machine

assistance for computation uh not just

the computer but the integrated

development environment and increasingly

more and more Technologies to accelerate

development of software we're entering a

new era in which these systems are

working together in a complex machine

Network what you see is a series of

processes that are working together in

order to produce uh complex Engineering

Systems and what you would see here is

agents working together to produce codee

not one at a time but actually in unison

and Harmony the same pattern is being

applied in writing very commonly writing

was a human process human and a tool

over time this has progressed to human

and a machine assistant and now we have

a human that's actually leveraging not

one but a network of assistants I'll

tell you in my own personal workflow now

anytime I call an AI assistant I'm not

just calling gp4 I'm calling Mist large

I'm calling Claud 3 I'm having them work

together and also uh against each other

to have better answers this is the

future that we're we're seeing right in

front of us so what what does this type

of revolution mean for everyone in this

room and frankly everyone outside of

this room in cold hard economic terms

what this

means is significant cost reduction so

this chart is the number of workers

needed at an S&P 500 company to generate

1 million of Revenue it's going down

rapidly we're entering an era where this

will continue to decline what does that

mean faster and fewer the good news is

it's not so that we can do less it's so

that we can do more and we'll get to

that in the next set of predictions also

fortunate is all the areas where we've

had this type of prog progress in the

past have been deflationary I'll call

out computer software and accessories

the process of computer software because

we're constantly building on each other

has actually gone down in cost over time

uh televisions are also here but some of

the most important things to our

society education college tuition

Medical Care housing they've gone up far

faster than inflation and it's perhaps a

very happy coincidence that artificial

intelligence is poised to help drive

down costs in these and many other

crucial

areas so that's the first conclusion

about the long-term future of artificial

intelligence as a massive cost driver a

productivity Revolution that's going to

be able to help us do more with less in

some of the most critical areas of our

society the second is related to what is

it really doing

one year ago on the stage we had Jensen

hang make a powerful prediction he said

that in the future pixels are not going

to be rendered they're going to be

generated any given image even

information will be generated what did

he mean by this well as everyone in this

room knows historically images have been

stored as rope memory uh so let's think

about the letter a asky character number

97 okay that is stored as a matrix of

pixels either the presence or absence if

we use a very simple black and white

presence or absence of those pixels well

we're entering a period in which we

already are representing Concepts like

the letter A not as Road storage not as

a presence or absence of pixels but as a

concept a multi-dimensional point I mean

the the image to think about here is the

concept of an a which is generalizable

to Any Given format for that letter A so

many different type faces in this

multi-dimensional space we're sitting at

the center and where do we go from here

well the powerful thing is the computers

are now starting to understand not just

this multi-dimensional point not just

how to take it and render it and

generate that image like Jensen was

talking about we are now at the point

where we're going to be able to

contextualize that understanding the

computer's going to understand the a be

able to render it understand it's an

alphabet understand it's an English

alphabet and understand what that means

in the broader context of this rendering

computer's going to look at the word

multi-dimensional and not even think

about the a but rather understand the

full context of why that's being brought

up and amazingly this future is how we

think how humans think no longer are we

going to be storing uh the wrote pixels

in a computer memory that's not how we

think I wasn't taught about the letter A

as the presence or absence of a of a

pixel on a page instead we're going to

be thinking about that as a concept

powerfully this is how we' thought about

it philosophically for thousands of

years here's my fellow Greek Plato 2,500

years ago who said this idea of a

platonic form is what we all ascribe to

or all striving for that you have this

concept in this case of a letter A or

this concept of software engineering

that we actually are able to build a

model around so what now we've talked

about the second pattern this idea that

we're going to have generalization in

inside Computing itself what does that

mean for each of us well it's going to

mean a lot for company building uh today

we're already integrating this into

specific processes and kpis Sonia just

mentioned how Clara is using this in

order to accelerate their kpis around

customer support they know that they

have certain kpis that they can drive

towards and they can have a system

that's actually retrieving information

generating great customer

experiences tomorrow and this is already

happening alongside new user interfaces

that might be a different interface for

how the support is actually being

communicated and this is what I'm

personally incredibly excited about is

because of this future in which concepts

are rendered because of this future in

which everything is generated eventually

the entire company might start working

like a neural network let me break that

down in a specific

example this is a caricature as with

everything in this presentation it's in

reality everything is continuous these

are all discreet this is a caricature of

the customer support process you have

customer service that has certain kpis

these are driven by Text to Voice

language generation customer

personalization and the like this feeds

into sub patterns sub trees that you're

optimizing and eventually yourx going to

have a fully connected graph here yourx

going to have feedback from the language

generation to the end kpi for the

servicing of the customers this is is

going to be at some point a layer of

abstraction where customer support is

managed optimized and improved by the

neural network now let's think about

unique customers another part of the

important job of building a business

well again you have Primitives of

artificial intelligence from language

generation to a growth engine to add

customization and optimization this will

all feed into each other once again the

powerful conclusion here is eventually

these layers of abstraction will be

become interoperable to the point where

the entire company is able to function

like a neural network here comes the

rise of the oneperson

company the one person company is going

to enable us not to do less but to do

more more problems can be tackled by

more people to create a better Society

so what's next the reality is the people

in the room here are going to decide

what's next you are the ones who are

building this future we personally are

very excited about the future because we

think that AI is positioned to help

drive down costs and increase

productivity in some of the most crucial

areas in our society better education

healthier populations more productive

populations and that's the purpose of

convening this group today you all are

going to be able to talk about how are

we able to take our Technologies

abstract away complexity mundane details

and actually build something that's much

more powerful for the future I'll hand

it off to Sonia to introduce our first

speaker thank

you