It's Not Looking Good For AI
Summary
TLDRThe video discusses the challenges faced by AI companies, including high operational costs, investor dissatisfaction, and customer confusion over AI's broad capabilities. It highlights how customers prefer specialized solutions over general-purpose AI models, leading to market confusion and competition. The video also addresses the failures of certain AI products, the issue of AI-generated code requiring external supervision, and the negative impact on job applicants flagged for using AI. The creator emphasizes the importance of these discussions and thanks supporters for enabling higher production quality without corporate sponsorships.
Takeaways
- π‘ AI companies are facing challenges due to high operational costs, including expensive hardware and maintenance for AI models.
- π Investors are skeptical as the billions invested in AI models have not yet yielded substantial profits, leading to backlash against AI companies.
- π€ Customers are confused by the broad capabilities of AI, preferring specialized products over 'one-size-fits-all' solutions.
- π± The script uses a smartphone metaphor to illustrate the market confusion caused by products that try to serve multiple purposes at once.
- π Customers tend to opt for specialized products for specific tasks rather than AI models that claim to do anything.
- π The failure of certain AI products has led to skepticism and doubt in the market, affecting the reputation of AI in the economy.
- π£οΈ AI's 'hallucinations', such as inappropriate suggestions, have raised serious concerns about the reliability of AI models.
- π¨βπ» AI-generated code often requires external supervision to ensure it works as intended, which can be more time-consuming and costly than writing the code itself.
- π The script discusses the difficulty of distinguishing between AI models that claim to perform similar tasks, leading to market fragmentation.
- π« AI models are increasingly used in the hiring process, with some companies using AI to check if job applicants' code was written with AI assistance, potentially leading to false allegations.
- π The video creator emphasizes the negative impact on new developers, who may be unfairly rejected based on AI's misjudgment of their work.
Q & A
Why are AI companies facing backlash from investors?
-AI companies are facing backlash because the billions of dollars invested in AI models are not justifying the profits generated. Investors expect returns on their investments, but AI models require expensive hardware, computational power, and maintenance costs, which are not being adequately compensated by the profits.
What is the main issue with AI products from a customer's perspective?
-From a customer's perspective, the issue with AI products is that they often try to do too many things, making it confusing for customers who generally prefer specialized products that solve a specific problem rather than a 'one size fits all' solution.
How does the smartphone metaphor relate to AI products?
-The smartphone metaphor illustrates that just as a smartphone with a built-in vaping machine would likely appeal to a very niche market, AI products that try to do everything may not attract a broad customer base. Customers often prefer products specialized in one area rather than multi-functional ones that may not fully meet their specific needs.
What are some of the challenges AI models face in the software development industry?
-AI models in software development face challenges such as producing code that often requires external supervision to ensure it works as intended. The cost and time associated with testing and ensuring code functionality are significant, and AI models are not yet adept at guaranteeing that the code they generate meets these standards.
What impact have AI product failures had on the industry?
-AI product failures, such as hallucinations in AI models and flops like Rabbit M1 and Human AI, have created skepticism and doubt among consumers and potential investors. This negative perception makes it harder for new AI companies to gain trust and succeed in the market.
Why are hallucinations in AI models concerning?
-Hallucinations in AI models are concerning because they can lead to dangerous or absurd recommendations, such as suggesting harmful actions or giving nonsensical advice. These incidents undermine trust in AI and can have serious consequences for users who rely on AI for accurate and safe guidance.
How has AI affected the job application process for software developers?
-AI has affected the job application process by introducing AI code checkers that flag assignments as being AI-generated. This has led to applicants being rejected unfairly if the AI incorrectly identifies their work as AI-generated, making it difficult for them to secure a job.
What is the potential downside of using AI to check code in job applications?
-The downside is that AI code checkers may misidentify code written by applicants as being AI-generated, leading to unfair rejections. This could harm the applicant's career prospects and create an environment of distrust between job seekers and employers.
Why do customers prefer specialized products over AI models that can do everything?
-Customers prefer specialized products because they are often more reliable and easier to use for specific tasks. A product designed to do one thing well is usually more appealing than an AI model that tries to do everything but may not excel in any one area.
What commitment has the content creator made regarding sponsorships?
-The content creator has committed to not accepting any sponsorships or brand deals. They want their audience to hear their genuine opinions, without any influence from companies, to ensure that the content remains unbiased and authentic.
Outlines
π AI Companies Facing Economic Challenges
This paragraph discusses the current difficulties faced by AI companies, including a lack of profitability despite significant investment in hardware and maintenance. It highlights the operational costs of AI models and the discrepancy between the billions of dollars invested and the returns received by investors. The video aims to explore these issues while emphasizing that the content is based on opinions rather than facts, due to legal reasons.
π€ Customer Confusion and Market Segmentation in AI
The second paragraph delves into the customer perspective on AI, where the versatility of AI products is causing confusion in the market. It uses the smartphone metaphor to illustrate how attempting to cater to multiple markets simultaneously can dilute a product's appeal. The paragraph argues that customers prefer specialized solutions over a 'one-size-fits-all' approach, which is currently hindering AI's success in the market due to a lack of clear, specialized value propositions.
Mindmap
Keywords
π‘AI models
π‘Operational cost
π‘Investors
π‘Customer confusion
π‘Market segment
π‘Product specialization
π‘Competition
π‘Product flops
π‘Hallucinations attribute
π‘Software engineering
π‘Interview process
Highlights
AI companies are facing challenges due to high operational costs and the inability to justify the profits from AI models.
Investors are experiencing backlash from not receiving adequate returns on their investments in AI.
Customers are confused by the broad capabilities of AI, preferring specialized products over general AI solutions.
AI's 'one size fits all' approach is not appealing to customers who seek specific solutions.
Market competition is causing confusion for customers trying to choose between various AI products.
AI product failures, such as Rabbit M1 and human AI, are creating skepticism in the market.
AI models' hallucinations, such as inappropriate suggestions, are raising serious concerns among consumers.
AI-generated code often requires external supervision to ensure its quality and functionality.
The process of ensuring code works as intended is more resource-intensive than writing the code itself.
AI models are not adept at verifying the functionality of the code they generate.
AI has infiltrated the interview process, with some companies using AI to check applicants' code for AI assistance.
Applicants are facing allegations of using AI to write code, which can negatively impact their job prospects.
The video creator emphasizes the importance of distinguishing personal opinions from facts for legal reasons.
AI models require significant computational power and maintenance, leading to high operational costs.
The creator discusses the smartphone metaphor to illustrate the confusion in the market regarding AI capabilities.
The creator highlights the need for specialized AI solutions rather than generalist AI models.
The video creator has hired a video editor thanks to the support from patrons and channel members.
The creator is committed to not accepting sponsorships or brand deals to maintain content integrity.
Transcripts
with the recent events involving Ai and
AI companies it seems like AI companies
have been Landing themselves in a lot of
hot water recently whether it's Wall
Street Turning its back on AI or
software developers reporting that their
workloads have been increased by 77%
with using AI models it seems like the
future is looking Bleak for AI companies
so in this video I'm going to be
discussing some points and discussing
some opinions about Ai and just a small
disclaimer for the video These are
opinions not facts
I have to say these things these days
for legal reasons but anyway let's jump
in so the first thing is the operational
cost of AI models and we know that AI
models require a lot of expensive
Hardware a lot of computational power
and a lot of maintenance cost to
maintain the operations of AI models to
make sure that it is operating at its
highest capacity for customers
now the billions of dollars that have
been poured into AI models and its
operations is not justifying the profits
that is generating back for the
investors which is how a usual company
survives in the economy they raise a lot
of money they develop the product and
they sell it and the profits that it
generates it's given back to the
investors so which is why AI companies
have been facing a lot of backlash from
investors with them not being able to
generate the profits from AI products so
this is something that is affecting AI
companies progresses in the latest
economy the second thing is about the
prospect of AI and its customer side of
it so for customers they usually pay for
a product that is specialized in one
thing and it's good at doing one thing
and the prospect of AI being like it can
do anything that the user can ask it is
why customers are confused with buying
AI products so I want to explain this
with a smartphone metaphor let's say
that you use a smartphone and you vape
on The Daily so you hear about a company
that is creating a smartphone that has a
waving machine built into it now on
paper it would seem like the company is
targeting two big markets people who
vape and people who use smartphones that
looks good on paper but in actuality
what happens is the company ends up
targeting the people who are looking for
both a smartphone and a vaping machine
built into one at the same time and are
willing to pay for it and what happens
in these scenarios is that the companies
are not able to generate profits for the
product that they created and this is
what the problem with AI is their
Prospect of AI models capabilities of
doing anything that the user can throw
at it is what puts the customers in a
confusing
State and it goes back to the market
segment of people wanting to have a
solution that can do a specific thing
over something that can do everything
that the user can ask it let's say that
you are a businessman and you are in a
requirement of a product that can
summarize documents for you so you go
into the market and you find out about
this AI model that can do anything for
you you would rather buy a specialized
product product that only specializes in
summarizing documents for you rather
than buying an AI model that can do
everything you ask of it and which is
why the prospect of AI is failing in in
this latest economy because customers
don't want to buy a one siiz fits all
solution they want specialized solutions
that they are willing to pay for so it's
less clutter for them and the
competition is another aspect of it
where let's say that you are uh in the
market and you are looking at open an AI
model that can summarize documents for
you and at the same time you also look
at claw or barred AI capability to do
the same thing now how will you
distinguish between the two of them and
let's say that you are in a requirement
of paraphrasing sentences for a document
so there are like five companies for it
so this creates a lot of confusion for
customers and and the market ends up
dividing itself between customers who
went with one company and with customers
who went to another company for the same
solution this is normal business
competition but the prospect of AI is to
be blamed here which claims to do
anything that the user can ask it to the
third thing is the flops that we have
been seeing regarding AI products so
whether it's rabbit M1 being a flop or
human AI flopping or some software
related AI models not being able to
generate code that is usable for uh
software developers without an external
supervision this all creates a negative
impact about AI in the economy where a
new company wanting to do something with
AI will be met with a lot of skepticism
and with a lot of
doubts which is a result of these
products failing in the current economy
okay the hallucinations attribute of AI
models are the biggest factor in it
where somebody asking for mental support
gets told that they should just jump off
the Golden Gate Bridge or uh some AI
model uh suggesting that people should
eat rocks every day so these are some
things that gets taken very seriously in
the consumer's mind where they question
the capabilities of AI and on the
software development side the code that
the AI chatbots generate have been
proved to be needing external
supervision to make sure that the code
is good enough to be used now an aspect
that has to be kept in mind about
software engineering is that making sure
that the code works as intended is lot
more expensive and time-taking than
writing the code in the first place the
code that we write it requires a lot of
testing environments and it requires a
lot of time and effort to make sure that
the code that we have delivered is
working as intended and AI models are
good at writing code but they are not
good at making sure that the code works
as intended because if we talk about
just specific modules even if we talk
about a simple example let's say a crud
form that simple crud form can function
very differently in the same product on
two different stages this is what needs
discussion over the external
investigation that needs to take place
when we take the code from AI chart Bots
and decide to use it in our projects now
I created a video before where I talked
about how how you should be careful with
using AI models for coding the video
should be right there so make sure to
check that out later and the last thing
that I would like to discuss about AI is
how AI models have infiltrated the
interview process so how it used to work
was the applicant would apply to the
company for a job position the company
would email them a task or an assignment
that they have to complete within a time
frame the applicant would complete the
assignment and submit it back to the
organization and it would be checked if
it is good enough and based on the
assign ment that the applicant submitted
the interview would go further now what
I heard from my Discord Community lately
is that the students are now getting
rejected in the assignment phase itself
because the management of the company is
passing their assignments into an AI
code Checker whether it's checked that
the code was written with the help of AI
or not and if the AI miscalculates or
mis Flags the assignment being like this
code was written with the help of AI
then the applicant gets put with an
allegation that they used AI to write
code and in my opinion this is an
allegation that you cannot walk out of
you cannot convince the management at
all that you did not use AI to write the
code because most of the code that the
AI is trained on is mediocre level or
beginner level code and the type of code
that beginers right it matches those
patterns and it's very likely that the
AI would say yes this code was written
with the help of AI now this is really
depressing scenario for the software
developers who are trying to enter the
industry this is something that we need
to discuss because the applicants have a
lot to lose on this part getting flagged
with using AI to write code is an
allegation that they cannot walk out of
and it basically ruins their chances of
ever Landing a job in the company so we
can only find out what will happen in
the future as time passes and uh yeah
there is a lot of uh problems with AI
obviously but we will have to see how
that works out so this is it for the
video um so I would like to take a
moment to thank all of my patrons and
channel members who have decided to
support the channel um as a result of
that I have been able to hire a video
editor finally so you should see the
production quality of my videos go up so
I can only thank you for that and if you
decide to support the channel even
further please do because I have made
this commitment that I will not accept
any sort of sponsorship or brand deals
on this channel I want you guys to hear
what I have got to say not any sort of
control speech uh from any company or
whatever so thanks thank you for
listening and I will see you in the next
one thank you
Browse More Related Video
Our Terrible Future And Open Source | Prime Reacts
Project Orion (GPT-5 Strawberry) Imminent, Already Shown To FEDS!
The Secret List of (soon to be) Extinct Jobs βββsincerely, AI
Training Your Own AI Model Is Not As Hard As You (Probably) Think
This Could Be a MASSIVE AI Business...and Now It's Yours π€
Is the AI bubble popping?
5.0 / 5 (0 votes)