In this week's TechTalk, Amit and Rinat delve into the profound impact of AI on jobs. We discuss how AI is reshaping industries, leading to automation, and job displacement, but also creating new opportunities and roles. They provide insights into how individuals can adapt to this rapidly evolving landscape, emphasizing the importance of upskilling, reskilling, and embracing AI as a tool for innovation and growth. Tune in to learn more about navigating the intersection of AI and employment in the digital age.

Hi everyone.

Welcome to Tech Talk, a podcast where Amit and I talk about all things tech.

We don't just talk about tech, we also talk about how the tech affects

our society and all the other things related to everything tech.

So today we are gonna talk about something that everyone's talking

about impact of AI on jobs.

with the last year 2023.

We've seen a surge of many, many different types of AI being available to consumers

to produce various types of content and help them augment their performance

of anything that they want to do, and that obviously opened the discussion

on how is it going to impact our jobs.

Many people are thinking that what they do on a day-to-day

basis will become obsolete.

the optimists are thinking that there will be new jobs there are various

weaknesses that with time we've started to figure out that okay, not everything

can be replaced exactly with AI as we are seeing the AI scene evolve on a

month by month and week by week basis.

So yeah, there's many aspects to explore and talk about and, so it would be

really fun topic to talk about with Amit.

So what's your thought on this, Amit?

So I think it's a very relevant topic and, thanks,

Rinat for that introduction.

I think AI is quite relevant now and it's all around us.

A lot of tools are coming up and a lot of use cases are coming

up now with the advent of AI.

We talked about generative AI a couple of episodes back.

We talked about chat GPT, we talked about AR and VR.

So there are a lot of different aspects to this whole AI thing.

there is metaverse coming in, so there's a huge potential for this.

I think the surge of AI is predominantly because of the large language

models, LLMs, as they call it.

Machine learning has been there for a very long time, and we have been

using that for image recognition and mapping different images.

But I think with LLMs, it's just taken a whole new level with with

respect to AI reaching to the masses.

So what I feel is, and I've read this as a quote, is that AI will not replace you.

People using AI will replace you.

I.

So I think that's very apt.

So there are so many AI tools in the market and we ourselves have

been playing with a lot of AI tools.

And what we are trying to see is what these AI tools can do and what

can we do with these AI tools and what, what is the pricing like, what

can we do with the fee free tier?

What can we do in the paid tier?

So I am.

I'm currently paying for a tool called Descript on which

we are recording this podcast.

And it has this facility where it'll generate the transcript and we can

edit the video using transcript.

I think Rinat is using Chat GPT paid version, so there he can use GPT four

and then he can use images, he can use websites and everything to search

the internet and create content.

And we have used AI In generating transcripts, but also, like, I've used

it for a few articles for my newsletter.

I've created an AI influencer using AI.

And we have been using AI for the last one year, and it's really amazing.

So I think it's a very relevant topic and let's discuss further

about what it impacts on a day-to-Day life and how it affects our jobs.

Yeah, very much so.

when, when we talk about AI and jobs, this reminds me of a short video I've

seen when chat GPT first came out at the end of Everyone was trying and,

whoever was in whichever profession they were seeing how would chat GPT react

or reply to that professional question.

And there was a video.

I remember a lawyer was doing a, a live in front of camera see what Chat

GPT can do and it asks it to create a contract with particular various.

legal terms, et cetera, and, some of those things as audiences who are not legally

aware, we didn't understand those terms.

But then Chat GPT wrote this contract, which actually exceeded the expectation

of the lawyer who was doing it.

And he was saying that not only he did what I asked it to do, it.

Not only it did what I asked it to do but also it added some, relevant

addendums and clause in that contract, which would be typical for that scenario.

So now that's using common sense.

it appears as though Chat GPT is using common sense and adding those things.

And then, the, the next second he was very happy saying that, oh, my

job would be really easy from now on.

then the second afterwards, you see the realisation in his face and he

realises that it's almost like he himself is not required anymore because

he was a recent graduate from law school and he was like, okay, it can

do everything I can do and better.

So where, where does it put me?

that realization is what people are having all throughout last year in

2023, as they start using chat GPT more and more, as more people become aware.

So even when you send.

A very well written email, nowadays, people might not give you the

credit or, or, the articulation that has gone into the email.

They might actually not give you that credit for that

because everyone's doing it.

It's like a inflation of intelligence is what's happening right now

and how does that affect jobs?

It is affecting jobs, but I think what's happening is because of the hype of,

oh, AI can do so many groundbreaking things, it's like day and night

difference between 2022 and 2023.

in terms of productivity.

So companies also became quite hyped and thinking, there are

so many ways we could use it.

But actually on an enterprise scenario, there are many things to think about when

you wanna implement a new technology.

especially, I, I am primarily experienced in banking and finance,

and there are regulatory requirements, there are various risks that you need

to take into account and quantify how would you manage these risks?

So for example, there has been talks that AI has been found to be biased in many

ways, gender bias, race bias, et cetera.

And what if a bank integrates AI to reply to a customer and these biases

come through in that conversation, that would be a huge reputational risk for

the bank or any organization as well as regulatory and compliance issue as

well, because it appears as though that organization is treating people unfairly

or differently based on their age, race, gender, So there are many, many risks.

as useful as it may seem to make it work on an enterprise scenario

is not just, okay, let's just buy subscription of Chat GPT, and we are set.

it's not as easy as that.

And sometimes you might even have to think about how can we dial down

the intelligence, dial down the.

bespoke responses, but always make sure it comes back with standardized

set of responses as required, which is a bit of a dumbed down version

of the solution people might think, but that is maybe what is required.

So.

just overnight transition of AI is gonna be doing my job from tomorrow is not

realistic to think And also whether it can do everything to the degree that a

human would is still very much questioned.

So these are different perspective to think about when you think

about the answer to this question.

Yeah, I think and you've raised a valid point about regulations

and regulatory requirements, but I think before we delve into job

security, I think our AI taking over.

I think I'll come back to the quote.

AI will not replace people.

People using AI will replace people.

So what that means, so it means that AI is augmenting you and it's augmenting

your productivity and augmenting anything that you're currently doing.

So suppose you take some time to write an email or suppose you take

some time to draft a, a contract.

So that now is augmented.

It means you can do the research far more quicker with AI.

Of course it's not a hundred percent accurate, but it

means you can do it quicker.

So it means you are already much better than someone who's doing it by himself,

just doing all the Google search.

so you are doing it much faster and you can create a early

draft using AI very easily.

So now, before we talk about the tools, we have to talk about these

large language models and what are the different aspects to current generative

AI which is actually generating different types of things for us.

So when we look at large language models, it is trained on text, and that text

is currently taken from the internet.

So it could be papers, it could be websites, it could be forums, it could

be video sites, et cetera, et cetera.

So, but large language models is predominantly, trained on text.

Now, this text data, it means that whatever is written on the internet,

say on Twitter or say on LinkedIn.

So it just captures it and trains itself on it.

So if there is bias on it, it means there is bias in the.

system.

So that means bias in training data translates to bias in the system itself.

So a lot of companies, what they try to do is they try to remove the bias.

They have humans to look at the training data, try to identify if

there is any bias, try to remove it.

But of course it's not possible because your dealing with billions

and billions of text lines of text.

Then we have images.

So we have mid journey, we have Dall-e, and they are trained

on different types of images.

So you have a big data set of images, and now you can generate new images,

especially for graphic designers, logo designers, website creators, et cetera.

So this is again, a.

Very different aspect now.

So suppose I want to create a thumbnail for my YouTube instead of creating

it myself I can ask AI to use certain images as input images and generate

a new image for my YouTube as a thumbnail or as a Instagram picture.

I.

The next thing is video.

So now there are tools that can generate videos for you.

So you give them a text prompt and it generates video.

And these videos are not highly realistic, but it can be highly stylized.

So suppose you want to create an animated video, but you are

not an expert in animation.

So you can create a text prompt, pay some money, and get these

videos created for you very quickly.

Without a lot of creative input.

So when the generative AI came, it was the age of democratizing creation.

So it means that anything that you want to create now or using ai, it's much

more faster, much better, and quicker.

But, but the thing is, it's not, going to replace you?

It's not at that stage yet because it is based on the training data.

So there is nothing new coming out of it because it is just

correlation from that data.

So if I ask it

yeah, just, just a little bit of anecdote as you were saying.

I just remembered something that AI falls over when it is

fed AI generated training data.

It just completely starts generating things that are not meaningful in any way.

So if you keep feeding it data that's also generated by an ai,

then it just can't work at all.

So you might think that, from YouTube to everything is gonna be filled with

AI generated content, but those content then can't generate new content anymore.

So there has to be a steady influx of human generated content always in

order for AI to work side by side.

I.

So, that, that's a very good point.

Rinat, that AI being fed on data that's created by ai, so that's synthetic data.

So and a lot of companies are already thinking about how to create synthetic

data because you will run out of training data after a certain period of time.

If you have scanned all the internet over the last couple of years and that's

all you have, then you would have to create new data to create new models.

Of course, we are generating a lot of data.

But that may not be enough to train the new models.

So companies are looking on how to generate data by themselves so

that these models can be trained.

But of course, we'll have to be very careful about what there

is, what is there in the data.

But the whole idea of talking about this is that it's not

going to create something new.

It is going to take an input of what is already there and then

create something, a mixture of it.

So suppose I want to create a test policy.

I ask the test policy, create a test policy for me.

So it's not going to create a new test policy for me.

It's going to scan the, it's already scanned the internet.

It has got some of the data, and then it's just going to blabber out

some of the things that are already there in standard test policies,

that data is read on the internet.

So that is what it is.

But how it makes me better or more productive, say to my colleague is

that now I can create a test policy in few seconds, whereas my colleague

will have to think about it and draft a test policy and and like do a lot

of research in order to create one.

But for me, I asked Chat GPT, it created a test policy.

I just edited it.

So, that's the difference.

Absolutely.

And yeah, editing something rather than creating something from scratch

is a lot quicker and You can focus on enhancing the quality of the final

output rather than just creating the very crude skeleton part.

'cause that takes a lot of time, but that doesn't really enhance

the quality that just, the basic building blocks needs to be there.

Time consuming.

And now that part is taken care of.

And now you can focus on more creative stuff how you can expand on the already

created product or content, et cetera.

And this reminds me of another interesting not study, but

phenomena that we've seen in this.

This is something I've seen in 2019 or 2020 before chat GPT or this

popularized AI systems were in play.

But we were, we've been working with AI way before that.

the different types of ai, which are pattern identifying AI and then there was

in medical sector I was working with an AI which would identify, look at MRI scans

thousands and thousands, billions of MRI scans to, to train it and then identify

whatever is wrong within those scans.

the percentage of accuracy when after the AI model was matured with training data,

It always Shifted between 88% to 93%.

And at the same time, a very experienced medical professional doctor or a surgeon,

their accuracy was also about at the same level, 90 to 95% or, or something.

There is an overlap and, but they're pretty much on par.

But where the difference came is if, medical professional worked together

with the AI generated solution, then the accuracy shot up to 98%.

So basically going back to your quote again you're not gonna be replaced by AI.

You're gonna be replaced by people using AI because.

That's the most lethal combination as well, because highest accuracy

highest level of creativity and the highest level of speed you can

also achieve in terms of generating whatever content or product or whatever

output it is that you're looking for.

So yeah, augmenting human performance is is what AI does

best at this point in time.

Obviously in future, there is talks about artificial general intelligence, although

that is a vastly different concept.

people do mistake the large language models as if it's thinking

and understanding, but it's not.

So I think we are still a bit far away from that, but you never know.

what I have noticed from using charge GPT over the

last couple of months is that it's not a hundred percent accurate and

sometimes it starts repeating itself.

So if you ask specific things, it can respond.

I.

But after you delve into it with much more questioning, then it starts failing.

And that's where you see the limitations of a system like this because it tries

to repeat the same thing and it's just trying to predict what the next word is.

it's not actually having any intelligence.

It's just trying to see that, okay, what's the next word?

What is the probability of that next word?

And I try to fit it to make sense of it, and that's how it works.

I.

Absolutely, and that's, that's another interesting thing

that happens if you ask it.

What's two plus two?

It will very confidently answer four and it'll be right, but it doesn't know what

two is and what plus is and what two is.

It just knows.

It just went through a lot of training data where it's seen two

plus two is four, but if you ask it a random but a very simple arithmetic.

An addition arithmetic, but two large random numbers, maybe 17,542

plus 37,000 something something.

It very likely will completely get it wrong because that's, it's very

unlikely that it was fed on that exact training data multiple times.

So it doesn't have anything to refer back to or, tell you what

would be the next next word for it.

So, that's how you can see where it breaks down.

I think.

after using chat, GPT and other LLMs, like Claude and Google Bard.

And a few other ones as well.

Much smaller ones I think I would come to a conclusion that even though it

sounds like it's an overnight change from what we had before and after Chat

GPT, but it's actually a really big step forward from google search engine

because in 2022, we were already asking in in the Google search

engine that, okay, how do I do this?

And then it will find the answer and it will also try and, generate

some question and answer for you from the result already.

So it was doing that already Google.

what is the job of a search engine is?

It's basically by the time we reached 2022 or 2020, we had vast amount

of information on the internet and.

We needed a way to categorize it so that we can find the

information when we need it.

The, very particular information from that sea of everything.

We just needed the answer to this one question that how do I, solve the blue

screen issue when my BIOS is not working or some something when you're working.

And then Google would find you that answer from all the things that he had indexed.

No, no.

Google will not find you the answer.

Google will give you a set of websites that it thinks has the

and Chat GPT gives you the answer because it doesn't give you a set of links.

Yes.

that's how it is to the user, right?

It's appearing like that.

But ultimately, what's happening is Google has indexed all of internet.

But Chat GPT was, it doesn't index, it was trained on, on patterns of data,

but those data is, is, is still there and it's basically bringing you up the

right data and in a way that you don't have to click five links and, read

five conversation between human beings.

It just bringing you the specific information you need.

And presenting it to you in a way that is most useful to you.

it just a, a direct one-to-one conversation.

So yeah, it is an overnight real big step forward.

But if you think about it, it's a really big step forward from

search engines to what it is now.

obviously we can talk about the image generation and all of the

other different content types after.

But in terms of text generation, you're searching for something.

Nowadays, most of the ways public are using chat GPT, if they have a

question, they say, oh, give me the answer, and it gives you the answer.

Yeah, I think it's a valid point that, okay, the search

engines were used and find you the answer and charge strain on the data.

But if you look at say an example where I want to create a travel

plan for my family to go to South Africa on a holiday in November, I.

So, so if I go to Google search and I say, okay I want to have a travel itinerary

for a trip like this in November to South Africa, it'll gimme a lot of links.

I'll have to look at the different flight options.

I'll have to look at the hotel options.

I'll have to look at what places I want to go.

I'll have to look at how many days I have to spend in each place, et cetera.

If I ask the same question in charge GPT giving all the necessary details,

charge GPT is going to gimme an itinerary day by day itinerary.

It's going to break it down.

It's going to tell me that you take a flight from here to here.

And depending upon the weather in November, it'll tell me, okay, go

to this city or that city and do this sighting and that sight seeing.

So it's already done a lot of research for me, and now I just have to do those things

like book a hotel, look at the itinerary.

And it's not like I will plan my whole trip around the itinerary, but most

of the research has already been done.

And now if you take that into a job aspect, because I think we, we have, we

are, we have digressed a lot from the job.

So in a job a lot of people they have chat.

We chat a lot, say on Slack.

Then we send a lot of emails.

So email is where you do a lot of your work because you have to

communicate about different things.

Then you use some a project management tool where you'll

have to manage different tasks.

Or a task management tool, any, any task.

It could be task of development, task of testing, task of managing

projects, resources, budget, et cetera.

And then you would also try to do some presentations.

You will have to draft some contracts, et cetera.

Now, for all these use cases what I've, I've noticed is that chat gpt is very

good at giving you an initial draft.

So suppose I want to send out an email about about a new proposal.

Okay.

And I want to tell my boss that, okay, I think that we should be proposing this

company about our products like this.

And it's something there in my head, but I don't know how to articulate it.

And I'll have to do some Google search.

I'll have to see what others are thinking about this same product and

how do you promote things, et cetera.

Now, if I ask Chat GPT and I give it context, like, okay, I'm a person

who's a manager at this company that specializes in this sector.

And I'm now trying to draft an email to my boss, and we want to write a proposal

and we want to talk about this product.

Now give me an output and Chat GPT will give you a very detailed

output because you've defined the context, you've defined the scenario.

And Chat GPT will give you a very good output.

And on that output you can edit things, tweak things replace few things that you

don't like, and then just send it and also it can, write in a particular tone.

So it can be a very formal tone, it can be a very informal tone, it can

be a very casual tone, et cetera.

So that way you have the hard work is done.

So now you just have to focus on the final output, tweaking

it and sending it to your boss.

So, imagine.

A person who's doing it from scratch and someone who's using AI to do it.

So the person doing from scratch would take about 10 to 15 minutes

or maybe 20 to 20 minutes or 30 minutes to do the research, draft

an email with you, augmenting AI.

You write the prompt, it takes you about two minutes.

Then you get the output, you tweak it, edit it 15 minutes

max, so you have half the time.

So from 30 minutes you've come down to 15 minutes, and maybe you can do it even

in five minutes if you are good at it.

So most of Chat GPT now is about prompts.

what is the quality of your prompt and how can you get the best output

in the, in the least amount of inputs?

So suppose I want to get an output for a travel itinerary or an email.

I want it to give me the best possible answer in, in one or two prompts.

I don't want to write 10 different prompts.

And asking it for 10 different outputs, and then I collate all the

information and then I create something.

I won't have one single prompt and then one quick great

output, and then just use that.

So a lot of this is actually about writing good prompts.

So you get good outputs in the shortest amount of time because

you have to pay for these tools.

These are not free tools and a lot of companies, what they're now thinking

is, can we create an LLM for ourselves?

Can we use our company data, our product data, and train an AI

just for our specific things?

Because the thing is, the moment you start talking to chat GPT,

you are sending confidential information to Chat GPT servers.

Now that means that suppose you tweak in some information about your

company, some stock prices or anything, it's gone in the training data.

Of course, they won't be able to identify exactly but that data is now

in the training data because whatever you talk to them, it's using it to

train itself and getting better at it.

So that's the downside.

Absolutely.

it's a downside.

But having known that this is what happens, you could protect yourself

from it and see the power of it.

So, the first scenario when you were mentioning that, yeah, you wanna

draft an email and all of these.

So if, imagine if the AI model had all the information.

all, all the internal company information and for large companies with a hundred k

employees, that's a lot of information in Slack, in teams, in all of these channels.

And also knowledge bases.

SharePoint is like, a slew of information for data protection, if

you can have like a internal AI model.

which has the base of understanding and responding to you like chat

GPT, but then it's been trained on all the internal data.

Then you could ask it, write

this report.

And then as a human, you would check that, that that report is actually fit for

purpose and it quantifies the exact metric that you are looking for or your boss is

looking for or your team is looking for.

So you can then tailor make it but then the, generating the base

report part is done in seconds

Yeah.

and that's what makes chat GPT or AI models so much more powerful.

In a way that it increases your productivity, but it does not

make your human judgment obsolete.

Not yet anyway.

And I think one of the things that I'm, I'm seeing even in my

own company is that a lot of effort is now being put into designing your own

LLMs or designing your own AI model.

The reason being that if you use Chat GPT you have to first pay for it.

You don't have control over the data.

You don't have control over the output, and the training data is quite vast,

and you want your training data to be very specific just for your industry.

So you don't want to want a AI model that uses the whole internet

as its training data base.

You just want the specific information needed for your sector,

for your industry, for your product, and train your model on that.

That's it.

You don't want, so suppose you're in the retail sector.

I don't want information about what do you say?

A travel company.

Okay.

I'm just giving a wrong example.

It can be related, but say defense company.

So retail has got no relation with defense.

We are not selling anything to defense companies, so I don't want that data

to be correlated or I don't want that unnecessary data or if I'm just

creating on certain types of images.

I don't want a different type of images.

So I'm, I've just given you example of email.

The other example could be logo generation.

So suppose there is a person who, who designs new logos or who designs new

new, new art for artwork, say book covers or podcast covers, et cetera.

Now that person's job can be done now much more quicker.

So you are not going to replace that person.

But imagine that person who was handling now 10 clients can handle

a hundred clients because that.

Can use AI to augment his or her productivity.

And that's just one example of logo generation.

The other example is

So j just to also intercept a little bit on this example,

'cause this is very interesting.

So as you mentioned that the person who could generate 10 artworks before

now can generate a hundred artworks.

And now the immediate thought to our audience or anyone who's thinking might

be that, okay, so now there was only ever a thousand logos to generate.

Now, before a hundred people would be doing those thousand logo designs.

Now only 10 people can do all the market, meet all the market needs, so there is

no jobs for the rest of the 90 people.

And that is gonna be a situation for the time being.

if you put it on a graph, yeah, there is a dip.

But what happens is now there is an influx more of a lot of other people who wanted

to have logo designed but couldn't, or they wanted to have their own startup

business idea, et cetera, couldn't.

But now with all of these additional tools, they are now thinking, okay, let

me start a side project as well as my job.

Everyone's doing that.

So now there is.

When there was a thousand logo to be designed before now there are

10,000 or a hundred thousand people are now asking for new logo designs.

So after that initial dip, there will definitely be a dip

where people will think, oh no 90 people are losing their job.

But very soon after, there will be so much more demand because logo generation

is now a lot easier and a lot cheaper.

As a result, a lot more people would be interested in doing that.

And then you would need a lot more people doing a hundred logos.

So overall, The amount of content being generated is increased.

The quality of content being generated is increased, but it then goes

back to the same amount of people doing it just more efficiently.

So that's how, it's in my mind, no one's losing their job.

It's just that they're becoming more efficient and the demand is increasing.

and I think that's, that's a very good point.

Like the dip, there will be a dip, but it means that there will be

a new baseline of productivity.

So initially your baseline was a hundred images, now the

baseline is a thousand images.

So there.

The baseline will be changing.

It's not that, and, and jobs will some people will lose their jobs because their

baseline productivity is not matching the current standards because the current

standard is now you should be able to do a hundred images so a thousand images,

but you're able to do only 500 images.

So that's interesting.

So this is where we go back to your quote, that some people will be losing

their jobs who did not start using AI.

So people who use AI will replace people

who don't use AI.

And I think if, if you talk about all these things, we are

just talking about generation.

this generative AI is all about generating AI images, AI text, AI videos, AI sound.

I think the next big problem for any enterprise would

be, are we violating GDPR?

Are we violating any copyrights?

Because see, imagine this this training data is being used to generate

new text, but that text is already there and it might have copyright.

So suppose I want to write a book and I write a book on something very technical,

it is going to gimme something, but that something might already been copyrighted.

And if I try to use that, someone can throw a legal case at me and

say that, okay guys, you have used copyright text for your book.

That's not valid.

So be smart.

Don't just use whatever the training data is throwing at you.

Whatever the AI model is throwing at you, be smart and edit it.

So when it come to images, you would also see that animation or cartoonish

characters are far more better.

But if you try to generate real human beings, then you

would have a lot of issues.

Like arms are missing.

the proportion is not right limbs are missing, et cetera.

So I, and I have noticed this because I've created an AI influencer on Instagram,

and I have noticed this while generating the images that some images are morphed,

some, some images are not correct.

So even though you're using AI to generate these images, it's not a hundred percent.

So you might have to do some modifications.

And then same goes for sound.

you can generate a lot of new music.

you can train your model for, say, music inspired by Taylor Swift or say Dua Lipa.

But then you have to ensure that you're not violating any copyright

laws, because even in workplace, you can do whatever you want as long as

you're not violating copyright laws and you're not violating any GDPR laws.

Because if you are using confidential information off some other people,

then that just violates all these things and, your job will be at

risk, so be smart about it as well.

Absolutely I think we've gone through many examples

of how to use AI to augment your performance and how it impacts the

society in terms of job availability.

the main point of our talk today was that, it's not completely unfounded

that there is a shift of how useful you can be in what way, to a company

to keep your job, but then in no way is it a like a done deal where this is it.

it's just an opportunity for us to upskill ourselves more, to make our

jobs more interesting and more creative and expand upon more from the basic

output that we can now easily generate.

Now let's take this as an opportunity and rise to towards our new challenge

and produce more, produce better, produce faster whatever it is that

we are producing in whichever job.

be smart about using AI and definitely start using the AI

tools and start gauging what are the different tools in the market, how

you can use it and what benefit it is.

I think a lot of tools will surprise you initially, and once you

cross the o wow, this is magical.

I think once you cross that threshold, then you'll realize the limitations.

It's only when you start using certain tools in certain ways

that you realize the limitations.

But if you don't use it, you'll never realize it.

You'll always think it's magic if you use a tool once you think it's magic.

But the more you use the tool, you think it's not magic,

there is algorithm behind it.

you can tweak the prompt to make sure that the algorithm gives

you the output that you desire.

So you can do all that, but in the end, it's not magic.

It's just mathematics.

I've used a lot of tools and I've, I've attended a lot of sessions on

different AI tools and I can say that these tools are not that intelligent.

They can do something specific things really well.

So if you are involved in something very specific, like you

have to do the same thing every day, say you're just generating

logos, your job might be at risk.

In case you start using AI tools to generate more images.

So if you do a very specific task, your job might be at risk.

specific repetitive job was at risk, even before as

well because of automation anyway.

But now with ai, it's the creative aspect as well.

So suppose you write contracts and you write only specific

contracts, then it's at risk.

But if you write specialized contracts, which changes based

on need, then it's not at risk.

Tailored made, So I I think a lot of the AI tools in the market

now are catering to personalized experience, personalized solutions,

that's where the market is heading, and it's quite an exciting future.

if we all embrace AI rather than looking at it as a threat.

if you look at it as a, as a tool to augment us, think it's a,

it's a huge benefit for all us.

I would echo what you said, Amit, that definitely use all the

different tools that are out out there.

Most of them have a free tier rather than the paid subscription.

And you can see all the different ways AI can be used

So it will open your eyes on, the limitation of it.

And I have spoken to many people about this as well, and I'm surprised,

obviously the initial hype of chat GPT was, like so many users faster

than Facebook and everyone, but then.

Not a lot of people uses tools like chat GPT as much as they could.

Right?

they've gone back to Yeah, the hype is finished.

We do use it sometimes, but a lot of the times we don't even think about.

even if you wanna reply to a very a sensitive text on WhatsApp my wife was

having to do that and I said, Hey, why don't you use chat GPT on your phone?

There is an app You can just literally copy and paste the text you received and.

get, get your response written down in the correct tone that you want to so you don't

have to overthink things and all of that.

So many ways you can use it.

Definitely would encourage everyone to explore, take some time every

week to see, like, put, put yourself down for one evening and just search.

On Google on different AI solutions.

There is a website which I don't remember the exact name, but it's

called, there is an AI for that.

And there's thousands of different AI tools all cataloged over there.

So you can see all the things that are possible.

So definitely explore, know how to prompt in a way that you get predictable

output and that way you can use it to your advantage and go a lot forward, a

lot faster and make a big difference.

So with that thought yeah we had a really nice conversation.

Please do reach out if you guys want us to go through any particular AI tool and

we would be happy to do that as well.

Thanks so much everyone.

See you next week.

Bye.

Bye.