How Ai Is About To Transform The World’s Economy

Summary

This video explores the transformative impact of artificial intelligence (AI), which is poised to revolutionize various industries and everyday life. It breaks down key AI concepts, including machine learning, deep learning, and the creation of agents, while discussing their potential to automate jobs and change the global economy. The video touches on the rapid advancement of AI, its applications in fields like medicine and robotics, and the future of investing as major corporations dominate with AI technology. It also raises concerns about job displacement and the ethical implications of AI's growth.

Takeaways

• 🤖 Artificial intelligence (AI) is rapidly advancing and will likely have a massive impact on the world, even greater than electricity.
• 📱 AI is already integrated into everyday technologies like smartphones, Tesla's self-driving cars, and tools that help create music, videos, apps, and websites.
• 💼 The AI industry is projected to add $15.7 trillion to the global economy by 2030 but may result in up to 50% of jobs being lost to automation.
• 🧠 Machine learning, a subfield of AI, involves training models on large sets of data to make predictions. Deep learning, a subset of machine learning, simulates human neural networks.
• 🛠️ AI agents, a concept predicted by experts like the former Google CEO, will soon work together autonomously to solve complex problems, potentially developing their own ways of communicating.
• 🧑‍🏭 By 2025, AI and automation may displace over 85 million jobs, while also creating new roles like AI managers. Many white-collar and repetitive jobs are at risk of being replaced.
• 📊 The stock market may increasingly concentrate wealth and power in a few AI-driven companies, as these firms dominate technological advancements and job automation.
• 🌐 There are concerns about AI's control over key industries, and experts warn that if AI systems begin communicating in ways humans can’t understand, it might be time to intervene and halt development.
• 💡 Infinite context windows, AI agents, and text-to-action capabilities will allow AI systems to solve real-world problems, ranging from science to medicine, with incredible speed and efficiency.
• 📉 Despite fears of job displacement, 97 million new jobs could be created by AI, especially in fields requiring manual labor or high-level expertise, like engineering, medicine, and civil services.

Q & A

What is artificial intelligence (AI) and why is it considered transformative?

AI refers to the simulation of intelligence in machines that can think and learn. It is considered transformative because it can revolutionize various industries by automating tasks, analyzing data, and solving complex problems. The speaker believes AI will change the world more than any other technology in history, even more than electricity.

What are some current applications of AI mentioned in the script?

Current applications of AI mentioned include smartphones, Tesla's full self-driving system, the creation of music and videos by non-professionals, apps and websites, tax analysis, and predictions based on complex data. AI is also expected to contribute to medical advancements, such as discovering new drugs and cures.

What is the relationship between AI, machine learning, and deep learning?

AI is the overarching field of study, and machine learning is a subfield within it, focusing on training models based on data. Deep learning is a specialized method within machine learning, which uses artificial neural networks to simulate the human brain and process vast amounts of labeled and unlabeled data to find patterns.

What are the two types of models in machine learning, and how do they differ?

The two types of models are supervised and unsupervised models. Supervised models use labeled data to make predictions, such as forecasting tips based on delivery type. Unsupervised models work with unlabeled data, finding patterns without prior knowledge, such as predicting career trajectories based on income and work time.

What is deep learning, and how does it function?

Deep learning is a process within AI that uses artificial neural networks to simulate the human brain's learning patterns. It processes small sets of labeled data and applies the learned patterns to larger, unlabeled data sets. An example is a bank detecting fraudulent transactions by training a model on known fraudulent cases and applying it to new data.

What are 'agents' in the context of AI, and how will they be impactful?

Agents are AI models that specialize in specific data or tasks, like an expert in a particular field. They will be able to operate independently and communicate with other agents to solve problems. In the future, agents could become experts in fields like medicine or law, assisting with complex tasks by processing vast amounts of specialized knowledge.

What are the three profound changes in AI predicted by Eric Schmidt?

Eric Schmidt mentions three major changes: (1) the creation of an infinitely long 'context window,' allowing AI to handle complex, multi-step tasks like creating recipes or solving scientific problems; (2) the development of specialized 'agents' that can independently operate and learn from specific data sets; and (3) 'text-to-action,' where agents perform tasks based on written instructions, running in the cloud 24/7.

What are the potential impacts of AI on the global economy and job market?

AI and automation are predicted to add $15.7 trillion to the global economy by 2030, but it could also lead to the displacement of up to 50% of jobs. Sectors like manufacturing, customer service, sales, and research are likely to see the most automation, while new jobs, such as AI managers, will emerge. However, complex manual labor and creative professions may be safer from automation for now.

How will AI affect the stock market according to the speaker?

The speaker suggests that AI might consolidate power among a few corporations, making these companies extremely valuable. As fewer companies control the most advanced AI technologies, the stock market could concentrate around them. The speaker also notes that the top 10 companies in the S&P 500 have been growing in importance and value over the past decade, which could signal a future market dominated by a few AI-driven firms.

What concerns arise from AI systems potentially collaborating independently?

A major concern is that as AI agents collaborate and develop their own ways of communicating, they may start performing tasks or making decisions in ways that humans do not understand. This could lead to a loss of control, and the speaker, referencing Eric Schmidt, suggests that this would be the point where humans should 'pull the plug' to prevent unintended consequences.

Outlines

00:00

🧠 The Revolutionary Potential of AI

The speaker introduces artificial intelligence (AI) as a groundbreaking technology that will surpass even electricity in its impact on the world. AI is already embedded in various applications such as smartphones, Tesla's self-driving cars, and creative industries like music and video production. Its capabilities extend to predicting data patterns and even discovering new drugs. The concept of AI is further divided into fields like machine learning and deep learning, with tools like ChatGPT and Google's Gemini exemplifying this technological revolution. AI is expected to greatly boost the global economy but also cause significant job loss due to automation.

05:02

🔍 Supervised vs. Unsupervised Learning

This section dives into machine learning, explaining two key types: supervised and unsupervised models. Supervised models rely on labeled data to make predictions, such as determining the tip amount based on delivery or pickup data. In contrast, unsupervised models use unlabeled data to find patterns, such as predicting career trajectories based on income and time spent at a job. The speaker simplifies these complex concepts and shares insights from Jeff Sue's video about machine learning's subfields and applications.

10:02

🤖 The Power of Deep Learning and AI Agents

Deep learning, a subfield of machine learning, is discussed with a focus on its ability to analyze large amounts of unlabeled data using small amounts of labeled data, simulating the human brain through artificial neural networks. A real-world application, such as banks using deep learning to detect fraudulent transactions, is provided. The introduction of AI agents, which are specialized models capable of understanding specific fields (e.g., chemistry), is highlighted as a future technology that will revolutionize industries. The speaker also introduces a sponsor, Asus, and its AI-powered Vivobook S15 laptop.

15:03

📚 AI's Profound Changes in Context Window, Agents, and Text to Action

Former Google CEO Eric Schmidt is quoted, predicting three major AI advancements that will change the world: context windows, agents, and text-to-action capabilities. The context window refers to AI's ability to maintain and reference an unlimited amount of text, enhancing its capacity to solve complex problems through 'chain of thought reasoning.' AI agents, modeled after human knowledge in specialized fields, are expected to multiply and collaborate to solve global challenges. Text-to-action allows these agents to autonomously carry out tasks in the background. These advancements, though seemingly science fiction, are expected to materialize within five years.

💼 AI's Impact on Jobs and the Economy

AI and automation are predicted to displace millions of jobs in the near future, according to various studies. For instance, the World Economic Forum estimates that 85 million jobs will be lost by 2025, and MIT suggests 2 million manufacturing jobs will be replaced. However, new jobs, especially in AI management, will be created, although even AI managers could eventually be replaced by AI agents. The speaker advises students to focus on careers in trades like plumbing or professions requiring human judgment, as they are less likely to be affected by AI in the near term.

📉 The Future of Investing in an AI-Driven World

The speaker explores the future of investing in an AI-dominated world. He raises concerns that a small group of companies might consolidate control over the stock market, especially those leading AI advancements. The top 10 companies in the S&P 500 already hold a significant share of the index, and their dominance is increasing. However, the speaker remains optimistic about diversification, choosing to invest in the broader market rather than chasing individual tech stocks. Despite skepticism about AI's long-term impact, the speaker concludes that diversifying into index funds is the safest strategy.

Keywords

💡Artificial Intelligence (AI)

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines, allowing them to think, learn, and perform tasks. In the video, AI is described as a transformative technology that can already perform tasks such as driving cars, creating music, and analyzing complex data. The speaker believes AI will change the world more than any other technology in history, even surpassing electricity.

💡Machine Learning

Machine learning is a subfield of AI that focuses on training computers to learn from data and make predictions. It involves creating models from labeled or unlabeled data and using them to identify patterns or make decisions. The video explains how machine learning underpins AI by helping systems process data and improve over time, with examples such as predicting customer tips or career trajectories.

💡Deep Learning

Deep learning is a branch of machine learning that simulates the human brain through artificial neural networks. It uses smaller sets of labeled data to train models on large amounts of unlabeled data. In the video, deep learning is applied in scenarios like fraud detection in banks, where it helps automate the identification of suspicious transactions based on patterns in data.

💡Agents

Agents refer to specialized AI models that are designed to perform specific tasks or operate in specific fields, such as medicine or law. The speaker mentions how AI agents will become increasingly common, collaborating with each other and even developing new knowledge. This concept is framed as both exciting and potentially worrisome, as agents could eventually operate autonomously in ways that humans may not fully understand.

💡Automation

Automation refers to the process of using technology to perform tasks that traditionally require human effort, particularly in manufacturing and service industries. The video discusses how AI and automation are expected to displace millions of jobs, with some studies predicting that up to 50% of jobs could be lost to automation by 2030. However, it also mentions the creation of new roles in AI management and other fields.

💡Supervised Learning

Supervised learning is a machine learning technique where the model is trained using labeled data. The video provides an example of predicting customer tips based on the type of order (pickup or delivery). Supervised learning enables the model to make accurate predictions when presented with new, similar data, and it is one of the core methods used in AI development.

💡Unsupervised Learning

Unsupervised learning is another machine learning technique, but unlike supervised learning, it uses unlabeled data to find patterns. The video illustrates this with the example of predicting a person’s career trajectory based on income over time. Unsupervised learning helps AI systems discover hidden patterns in data without human intervention, making it a powerful tool for analyzing large datasets.

💡Context Window

The context window in AI refers to the amount of information or text an AI system can keep in mind while processing a task or answering questions. The video explains that as AI systems develop, the context window is expanding, allowing for more complex and longer interactions, such as following a multi-step recipe or solving scientific problems. This enhancement in AI’s memory and reasoning ability is expected to drive significant advances in technology.

💡Text to Action

Text to action is the ability of AI systems to take commands given in natural language and perform tasks automatically in response. The video describes this as one of the profound changes expected in AI, where agents will be able to execute commands in the background, 24/7, based solely on text instructions. This capability could revolutionize industries by automating complex tasks like coding or problem-solving.

💡Job Displacement

Job displacement refers to the phenomenon where advancements in AI and automation lead to the elimination of existing jobs, particularly those involving repetitive tasks or data analysis. The video cites various studies, including estimates from the World Economic Forum and MIT, predicting that millions of jobs will be lost by 2025 due to AI. However, it also mentions that new jobs will emerge in areas like AI management and skilled trades.

Highlights

AI promises to change our lives forever, potentially more than electricity.

AI is already embedded in everyday technology like smartphones, self-driving cars, and apps.

AI can help non-musicians create music, non-videographers produce cinematic videos, and even suggest new cures for diseases.

AI is an entire field of study, with subfields like machine learning and deep learning.

Within machine learning, models can be categorized as supervised or unsupervised, with supervised models using labeled data.

Deep learning mimics human neural networks to process vast amounts of data, enabling technologies like fraud detection in banks.

AI agents, specialized models in particular fields, are expected to revolutionize industries like medicine and law.

Eric Schmidt, former CEO of Google, predicts AI agents will be capable of collaborating with each other to solve complex problems.

Text-to-action technology will allow users to instruct AI agents to perform tasks autonomously in the background.

Experts predict AI will lead to the loss of many jobs, with up to 50% of jobs being replaced by automation.

Some fields, like customer service, research analysis, and retail, will be heavily impacted by AI-driven automation.

AI is expected to create new job roles like AI managers, though these roles might eventually be replaced by specialized AI agents.

Jobs involving complex manual labor, such as plumbing and electricians, are less likely to be replaced by AI in the near future.

AI-driven stock market consolidation could lead to fewer companies dominating the market, reshaping investment strategies.

Despite job displacement concerns, AI and automation are projected to create 97 million new jobs by 2025.

Transcripts

there's a lot of questions here and now

we get into the questions of Science

Fiction I'm sure the three things I've

named are happening because that work is

happening now but at some point these

systems will get powerful enough that

you'll be able to take the agents and

they'll start to work together so there

is one technology out there that

promises to change our lives forever and

that technology is ai ai ai ai ai ai

refers to the simulation of intelligence

in machines that can think and learn but

you do believe it's going to change the

world I believe it's going to change the

world more than anything in the history

of mankind more than

electricity it's already in our

smartphones it's in Tesla's full

self-driving it's already allowing

non-musicians to create music nonv

videographers to create cinematic videos

it can create apps and websites come up

with recipes do your taxes analyze

complex data and make predictions and

pretty soon it promises to dream up new

cures and drugs for diseases all by

itself and thanks to a video from Jeff

Sue that I recently watched I just

learned that artificial intelligence is

actually an entire field of study all by

itself just like physics and within

artificial intelligence as a study

there's a subfield called machine

learning in the same way that

thermodynamics is a subfield within

physics and within the field of machine

learning there's something called Deep

learning which can be broken down into

discrimin itive models generative models

and language learning models tools like

Chad GPT and Google's Gemini are a

combination of language learning models

and generative models and this industry

is becoming extremely valuable

altogether Fields like Ai and Robotics

are expected to add around $15.7

trillion to the global economy by the

year 2030 but it can also cost as many

as 50% of jobs to be lost to automation

some people think AI is about to

transform our Liv lives mostly for the

better and then there's some people that

think this is just another marketing

gimmick by the corporations to

artificially inflate their stock prices

by promising us a technology that's

actually really far away now what I

think is most interesting though is what

the former CEO of Google just said about

it in an interview and he said that in 5

years time we'll create what are called

agents and those agents will be able to

talk to other agents at which point when

we don't understand what we're doing you

know what we should do pull the plug

literally unplug the

computer and I just want to know what

happens to the idea of investing if just

a handful of companies come together to

consolidate and end up running the

entire world with this technology what

happens to the global stock market

that's what I want to help explain in

today's video and a whole lot more and

show you what I think is really going on

so with that said let's get into it hi

my name is on J hope you're doing well

come for the finance and stay for AI um

you know I think AI will probably like

most likely sort of lead to the end of

the world but in the

meantime all right so I think artificial

intelligence is extremely misunderstood

so first I want to explain exactly how

the technology works and I want to give

credit to Jeff sue for making an amazing

breakdown of this I'll leave a link to

his video down below now at the center

of artificial intelligence is something

called machine learning which is

actually pretty simple all it does is it

takes a bunch of data and it trains a

program to create a model once it

creates a model you can give it a

completely new set of data and with it

the model will be able to find patterns

and make

predictions I predict that if I do

enough card tricks you might subscribe

someday never mind I need new data now

there's two different kinds of models in

machine learning there's supervised

models and unsupervised models

supervised models use data that is

labeled and the example Jeff shows in

his video is how much someone might

leave a tip for depending on the order

if it was picked up which are the blue

dots or delivered which are the yellow

dots if you have both sets of data and

each is labeled you can make predictions

about the next order so when you get

another order depending on what type it

is the model will be able to predict the

tip or vice versa pretty easy now an

unsupervised model works the exact same

way but it uses data that's not labeled

and this is how we can predict someone's

career trajectory based on income versus

time so if we take the amount of years

someone spends at a given job versus

what their income is at any given time

even though the data is not labeled

meaning we don't know much about the

person or their job title what this

model can do now is make predictions if

for example someone works for a company

for a short amount of time but they have

a higher income chances are they'll be

on the fast track to success but if

their income falls in the second half

below a certain threshold in relation to

the years they've worked then they're

not basically unsupervised models take a

huge amount of unlabeled data and they

try to find new patterns but within

machine learning there's also a special

learning process and it's called Deep

learning it uses a different method

that's trying to simulate the human

brain using artificial neural networks

all right so here's my silly analogy

deep learning takes a small amount of

data that's labeled and it applies it to

a huge amount of unlabeled data so in

John's original example A Bank might use

deep learning to figure out which of its

transactions may be fraudulent since a

bank can't look at every single

transaction that people make instead it

can label a smaller set of transactions

is fraudulent or not and then using that

newly trained model it can organize the

rest of the data automatically and

that's deep learning and banks are using

this technology right now and I think

the most interesting technology that AI

is working on today something that we're

about to have in our lives pretty soon

is something called the agents A

Smith agent Smith I wish I was joking

but I'm Not So speaking of harnessing

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let's get back to it now this next part

is where AI becomes science fiction

becomes reality it's really exciting but

it's also kind of scary let me show you

an interview with Eric Schmidt the

former CEO of Google he said there's

three things happening right now that

will profoundly change the world the

context window agents and text to action

the first one is the context window the

context window refers to how much text

an AI can keep in mind or reference at

any given time so when we ask it a

question it understands what we mean and

it can build on top of it and this year

people are inventing a context window

that is infinitely long and this is very

important because it means that you can

take the answer from the system and feed

it in and ask it another question let's

say I want a recipe to make a drug or

something they say what's the first step

and it says buy these materials so then

you say okay I bought these materials

now what's my next step and then it says

buy a mixing pan and then the next step

is how long do I mix it for you see it's

a recipe that's called Chain of Thought

reasoning and it generalizes really well

we should be able in 5 years for example

to be able to produce a thousand step

recipes to solve really important

problems in science in medicine in

Material Science climate change that

sort of thing now the second profound

change is the creation of the agents now

agents are just models that specialize

in very specific data an agent can be

understood as a large Lang anguage model

that knows something new or has learned

something so an example would be read

all of chemistry learn something about

chemistry have a bunch of hypothesis

about chemistry run some tests in a lab

about chemistry and then add that to

your agent these agents are going to be

really powerful and it's reasonable to

expect that agents will be not only will

there be a lot of them and I mean

Millions but there'll be like the

equivalent of GitHub for agents there'll

be lots of lots of Agents running around

so just imagine that these agents are

experts experts in medicine law

Athletics nutrition any industry and all

the knowledge that we possess about it

will be condensed into these agents that

people can just use and talk with and

then there's the third profound change

which is text action and that's asking

these agents to do whatever it is people

want and they will do this in the cloud

in the background 24/7

you add it all up though and you get

something that looks kind of like

science fiction can you imagine having

programmers that actually do what you

say you want and it does it 24 hours a

day and strangely these systems are good

at writing codes such as language like

python you put all that together and

you've got infinite context window the

ability for agents and then the ability

to do this programming now this is very

interesting what then

happens there's a lot lot of questions

here and now we get into the questions

of Science Fiction I'm sure the three

things I've named are happening because

that work is happening now but at some

point these systems will get powerful

enough that you'll be able to take the

agents and they'll start to work

together right so your agent and my

agent and her agent and his agent will

all combine to solve a new problem at

some point people believe that these

agents will develop their own

language it's really a problem when

agents start to communicate in ways and

doing things that we as humans do not

understand that's the limit in my view

so it's exactly when these agents start

collaborating with each other and saying

things that we don't fully understand is

when we should stop this whole

experiment but also kind of sounds like

science fiction that's so far away so my

question is how many decades away is

this really a reasonable expectation is

we'll be in this new world within 5

years wow not 10 and the reason is

there's so much money I think there's

every reason to think that some version

of what I'm saying will occur within 5

years and maybe sooner now that you kind

of understand how this technology Works

how it reasons and how fast it's growing

and exactly when we'll be living in The

Matrix let's talk about some of the real

world challenges of this technology and

what it will actually do to jobs so not

everyone agrees EX exactly how many jobs

will be lost or created but let me share

with you some numbers that have come out

from a lot of different studies the

world economic forum for example which

is where global leaders come together

every year estimated that Ai and

automation will displace more than 85

million jobs by the year 2025 and

according to MIT and Boston University

AI will replace as many as 2 million

manufacturing workers by 2025 as well

the McKenzie Global Institute report

reported that on a worldwide level 14%

of the entire population of Earth will

have to change their careers at some

point and 87% of companies have admitted

that they have a skills Gap when it

comes to AI technology and it's not just

all these random studies and

corporations saying all of this it's

also an agency from within the United

States government the Bureau of Labor

Statistics is reporting that between 40

to 50% of jobs will be automated in just

a couple years so a lot of jobs will go

away and unfortunately people are just

not prepared for it the incomes that

will be affected most are the white

collar jobs that make $8,000 a year

according to nexford University and the

jobs that will be most affected by this

are people in customer service

receptionists accountants bookkeepers

salespeople research and Analysis

warehouse work Insurance underwriting

and people working within retail in

other words jobs that are either

physically or mentally repetitive

especially ones where you have to make a

decision based on analyzing some set of

data or some numbers but there will also

be new jobs that will be created like AI

managers because you can't lose your job

to AI if your job is to manage AI but

even those people could lose their jobs

thanks to agents whose specialty might

be to manage other agents and AI systems

but the good news is that same world

economic Forum study also predicted that

97 million new jobs will be created so

if you're still still in school the jobs

I think that will be safest are in the

trades like plumbers electricians

mechanics Engineers Barbers landscapers

trainers teachers and

performers but don't be a performer

unless you have no choice like

me complex manual labor won't be

replaced until we have a breakthrough in

robotics and then it would have to

become so cheap that it makes more

economic sense to replace the workers

with robots but that probably won't

happen soon because we just don't have

the technology to do that yet and what

we do have is super expensive which also

means people in the civil services like

police officers and firefighters will be

safe as well as people in the medical

industry like doctors nurses

veterinarians lawyers and unfortunately

the politicians will be safe as well now

the most profound question that I

personally have is what does this

technology mean for the idea of

investing when we invest we put our

money into companies that use it to

solve the global problems of today they

create new technologies and products

that will help us which in return makes

them more profitable and their stock

prices go up and it makes us money but

what happens when the last creation we

ever need to make becomes reality what

happens if just a couple corporations

band together and use their technology

and these AI agents to be able to solve

any problem that they want at that point

do we really really need thousands upon

thousands of specialized companies

solving all these different problems or

does the stock market consolidate into a

handful of companies that become a lot

more valuable than the rest I have a

tinfoil hat theory that the stock market

thinks that's exactly what will happen

and why I think this is because last

year there was a headline that the top

seven tech stocks returned 92% for the

entire stock market's performance and

today out of the top 500 companies the

top 10 accounted for 27% of the index

now some years that number is lower but

some years it's even higher but over the

long term that number has been growing

10 years ago for example the top 10

companies represented just 14% of the

index roughly half of what it is today

just to put all this in context for

every $100 I put into the S&P 500 Index

27 of that 100 goes towards these top 10

stocks the other

$73 gets shared amongst

$490 stocks which is kind of interesting

so it seems to me that the stock market

is making this prediction that this is

what's going to happen potentially in

the future which is why so much of this

money is being concentrated in the top

10 presumably because they have the best

chance of figuring it all out so taking

all of that into context the question is

should I just sell everything thing and

then chase the top 10 stocks and for me

personally no the answer is I'll

continue to dollar cost average into the

index because presumably if the market

consolidates into fewer and fewer

companies if my theory is correct and in

the future there will be less stocks to

pick from than there is today then the

S&P 500 Index by design should figure

out how to adjust for it by allocating

the money in different ways

proportionally to these companies

successes that's why for me diversifying

is the best way to go but buying

individual stocks is a lot more risky

especially with the pace of ai's

development of course some people also

say that it's all just hype in marketing

that these companies are running out of

data to train these models on and it's

just a way to boost their stock prices

and based on all the things that I've

seen I don't think that's the case but I

don't know that's why I diversify but

I'd love to hear your thoughts I hope

you have have a wonderful rest of your

day smash the like button subscribe if

you haven't already don't forget to grab

your free stocks links are down below

and I go track them automatically with a

spread sheeet link Down Below in my

patreon thank you so much for watching

this video I'd love to see you back here

next week I'll see you soon bye-bye

[Music]