January 28, 2025 • 16 mins
In this episode, host Vera Shafiq delves into the critical issue of using AI tools in marketing while safeguarding proprietary and sensitive data. Highlighting popular AI tools like Copilot, ChatGPT, Gemini, and Perplexity, Vera addresses the concerns surrounding data privacy and security. The episode also explores DeepSeek, a new AI model from China, discussing its potential and inherent risks. Vera shares best practices for utilizing AI tools safely, emphasizing the importance of anonymizing data, and introducing solutions like private AI models and enterprise AI systems with strict data governance controls. The episode concludes with a look into synthetic data as a method to train AI models without compromising privacy.
00:00 Introduction to the Podcast
00:29 The Importance of AI and Data Privacy
01:51 DeepSeek: The New AI Model from China
05:39 AI Data Landscape and Best Practices
07:00 Using AI Tools Safely
11:38 Advanced AI Solutions for Enterprises
15:01 Conclusion and Key Takeaways
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:15):
Hey everyone.
Welcome to the podcast.
This is the podcast where wehave real and relevant
discussions on business andmarketing in the franchise space
and talk all things marketingand AI.
And I'm your host Veera Shafiq.
Today we're diving into asubject that's top of mind for a
lot of marketing professionals,CMOs, and it's really how do we
(00:36):
use AI tools safely while beingmindful of our proprietary and
sensitive data?
So, you know, as we know, we'veall been using AI tools such as
Copilot, ChatGPT, Gemini,Perplexity on the list goes on
and on and on.
and really it's been giving usthe opportunity to drive
efficiency and insight andresearch and do our jobs a lot
(01:00):
better.
But the nagging question at theback of a lot of our minds is
what about the data privacy?
What about when I start to sharemy company's proprietary data
with the AI models?
For example, you know, I'muploading performance data,
(01:21):
marketing performance data, orI'm uploading some graphs or
some charts, even somecompetitive stuff.
Alright, what is safe to uploadand what should I be worried
about?
These are the kinds of questionsthat I'm hearing and I myself
have had.
Um, so just researching andreally understanding how these
(01:42):
platforms work has given me amuch better clarity on what I
should and should not be usingAI for.
On top of that, very topicalthis week is the subject of
DeepSeek, which is the new AImodel that came out of China and
just came out of nowhere as italmost seemed as if it came out
(02:04):
of nowhere.
It's a Chinese artificialintelligence startup that
recently gained significantattention for its open source AI
models.
And it has particularly ofinterest a model named R1, which
is supposed to rival.
A.
I.
Systems.
Such as Open A.
I.
S.
Chat.
G P.
T.
Offering comparable performancein areas such as reasoning
(02:27):
mathematics encoding.
So deep sea has achieved theseadvancements.
With a significantly lowerinvestment and development
costs.
And it's really kind of beenbrought to the attention in the
media that, you know, thiscompany has spent 5 million
developing its AI model,whereas.
(02:48):
Open AI spent a hundred milliondollars to do something of the
same caliber.
So, questions around thesecurity of DeepSeek also come
up.
And, you know, we wonder howsafe is it to use DeepSeek if we
wanted to test that out, andespecially if we're using
proprietary data, so today we'llbreak down exactly where and how
(03:08):
you should be sharing yourproprietary data and where you
should exercise caution.
So let's start with DeepSeeksince it's not It's very topical
right now.
It's the talk of the town.
DeepSeek has focused itsfoundational AI technologies and
committed to open sourcing onall of its models.
Regarding safety, there areseveral considerations.
(03:32):
So, censorship and bias is oneof them.
Their models have been observed,and this is all over the
internet, and if you read all ofthe kind of experience that
people have had testing it out,I also tested it out and noticed
similar kind of, um, attributes,is that, um, The DeepSeek models
have been observed to exhibitcensorship, particularly on
(03:52):
topics sensitive to the Chinesegovernment.
So, for instance, some of theexamples that I've heard about
is that, you know, AI, the AImay refuse to discuss subjects
like the Tiananmen Squareprotests or human rights in
China.
So, it is censored.
And then there are the securityconcerns.
So regarding, security, I wasreading an article in the
(04:14):
Guardian and it says that thecompany recently faced a
significant cyber attack leadingto a temporary suspension of new
user registrations.
So while deep seeks.
Has already addressed the issue.
It does highlight that there arepotential security, security
vulnerabilities, uh, with deepseek and then data privacy as a
(04:35):
Chinese company.
And, you know, we all know whatis going on with Tik TOK and the
whole, I guess, fear that theChinese, uh, you know, holding
the art data somehow, there is.
The fear that DeepSeek may besubject to local data
regulations, which could raiseconcerns about data privacy and
potential government access toour information.
(04:57):
So, I'm not going to sway oneway or the other, but I
personally, I'm going to remainwary of.
Deep seeks, content limitations,recent security incidents, and
the data privacy considerations.
And right now for me, I don'tfeel the need to use deep seek.
I am sure that might change inthe future, but right now I'm
going to stick with thefoundational models that are,
(05:19):
presented to us through open AI.
Anthropic, we have met as modelsand Google's models.
We have models coming out theyin yang.
So I'm going to stay away fromdeep seek for now.
That's, that's my personalchoice, but again, these are the
considerations that we do needto start to think about as these
AI models start to come out.
(05:39):
So let's talk about the AI datalandscape for a little bit
before we get into bestpractices.
Let's talk about how AI toolsinteract with your data.
So many of the publiclyavailable AI models, including
chat, GPT, and Collect and storeuser inputs to improve their
model performance and fortraining purposes.
(06:00):
And if you look at the privacystatements for all of these
models, you'll see that theystate that very clearly.
Chat GPT plus subscription has atoggle where you can toggle off
using your data for training.
So that's something that you cando, um, for franchise
corporations, specifically thetopic of, whether this data is
(06:22):
being used and how it's beingused can raise significant
concerns around specific thingslike confidential brand strategy
and marketing data.
We could be using competitivepositioning and pricing models
in our queries and our prompts.
Then there's the topic oflocalized franchisee specific
data.
(06:42):
And then the big one, which isCRM data and PII.
many brands in the franchiseindustry have been highly
scrutinized in verticals, suchas health and finance, and they
are certainly on the front lineswhen it comes to being really
accountable and, and highlyresponsible for their customer
data.
So let's talk a little bit abouthow to use these tools and where
(07:06):
we should kind of hold back andmaybe think about using a
different solution.
So tools such as chat GPT.
Perplexity, Claude, Gemini, theyare great for, handling publicly
available insights.
So if we want to do research onindustry trends or general
marketing strategies or marketresearch, this is all publicly
(07:28):
accessible data that can beshared with the models without
risk.
We can.
You know, go back and forth withthe models on all of this stuff
that is already publiclyaccessible.
The same thing applies toanonymized data sets.
So if we want to feed themodels, customer data,
operational data, uh, we can useanonymization techniques.
(07:51):
To anonymize the data,definitely make sure we're
removing all PII.
So no emails, phone numbers,addresses, names, all of that
good stuff.
But we can use techniques likedata masking or tokenization, or
we can aggregate data so thatit's not individual level data
to remove that PII and usedanonymized data sets.
(08:14):
So that's one way that you canget away with uploading your
data to things like chat GPTWithout worrying about it.
Another thing you can do, sowhen you're looking for market
research, competitive analysis,things of that nature, industry
shifts, you can definitely startto use, you can use chat, GBT
(08:34):
for that for audience behaviors.
Just make sure that you're notinputting proprietary insights
when crafting your prompts.
So just stay away from very,very highly sensitive
proprietary secret sauce type ofstuff.
and then finally, contentcreation and optimization.
This is a big one.
We know we can do this.
All day long till the cows comehome with no worries because we
(08:58):
can use GPT and co pilot togenerate copy and creative and
SEO strategies.
And this is all above board.
No worries about that.
obviously we need to be carefulin terms of copyrighted or
plagiarism and things like that.
But at the end of the day, thecopy and creative that, AI
generates is essentially uniqueand we can use that and not be
(09:22):
worried about, getting into anytrouble there.
And that there is an asteriskbehind that.
We do, need to be careful, withcreative and especially when
we're telling it to write thingsin the style of someone else or
in the style of a photographeror an artist, we need to be a
little bit careful there, butfor general purposes, marketing
content creation andoptimization is fine to use.
(09:44):
A chat GPT or a Claude, etcetera for.
So where do we need to exercisecaution?
Well, I would say, you know,back to that proprietary
marketing and brand data, anyinternal marketing strategies or
proprietary budgets orperformance data, or anything
that includes passwords to anyof your systems should
(10:04):
definitely not be shared with AImodels such as chat GPT, because
that does not ever guaranteefull data privacy.
So.
Just be careful about,explicitly going in and giving
chat GPT, carte blanche,everything that you have, which
is proprietary.
As I mentioned earlier, you needto clean that data, aggregate
(10:26):
that data, or anonymize thatdata before uploading it.
customer data and CRM insights.
So we talked about this.
Never input raw customer datainto AI tools unless you're
using an enterprise AI solutionwith strict privacy controls.
And we'll talk about that in alittle bit.
Uh, localized franchisee levelinsights.
So when we're talking aboutindividual franchise location
(10:50):
data, whether that be marketingperformance, financials,
anything proprietary, and again,anything that's a secret sauce
should be secured withininternal systems and not be
exposed to public AI tools.
And then finally, I would saypredictive modeling for business
growth.
So if we want to create our ownproprietary.
(11:11):
Predictive models, that's fine,but we need to build those on
internal secured frameworks, noton public AI platforms, because
obviously that's a secret sauce,right?
We're trying to create a verycustom predictive model for our
business and to grow ourbusiness.
So we don't really want to sharethat with, a chat GPT or, you
know, a model that's.
(11:32):
I guess, unless the voter is, ispotentially using that data to
train other data on.
So, if you are looking toleverage AI at scale.
Without compromising yourproprietary data.
There are some solutions.
I would say the most advancedsolution is using private or on
prem on premises.
(11:53):
That is AI models.
this is probably the mostexpensive and involved solution.
So.
Instead of using publiclyavailable AI, some franchise
corporations are investing inprivate AI models deployed on
premises or within secure cloudenvironments.
So some of these types ofplatforms include IBM Watson for
enterprise AI and Google VertexAI.
(12:15):
So these are where you have yourmodels downloaded on premises,
they're not in the cloud, andnow you are actually creating
your own proprietary models.
within your own company fourwalls or within a secure cloud
environment.
the second level would be, anenterprise AI.
(12:38):
System that has data governancecontrols.
And this includes things likeMicrosoft co pilot, Salesforce,
Einstein, Adobe Sensei, theseare enterprise grade AI with
strict data governance, and theyensure that your proprietary
data remains confidential.
So for example, Microsoft copilot enterprise stays within
(12:59):
the Microsoft 365 suite.
So all of your data, which sitsin Microsoft 365 stays.
In the four walls of yourenterprise.
And there's no leakage of thatdata outside into the public
domain.
AI powered marketing automation.
There are platforms for thatright now that have their own AI
(13:21):
models, which are also containedwithin your own datasets so that
you don't have to worry aboutany data leakage.
Things like HubSpot, Marketo andSprinkler are now, incorporating
AI powered marketing automation.
that keeps your proprietary datawithin secured systems.
So you could be free and safe touse those.
And then finally, somethingthat, which is a little bit
(13:44):
different and it's usingsynthetic data for AI training.
So I think synthetic data issomething that we're going to
start hearing a lot more about.
and it's something that we canuse to artificially generate
data sets.
That mimic the real data, butwithout exposing actual business
insights.
So this really ensures securitywhile maintaining the model
(14:05):
accuracy.
So for example, if I want toupload, or I want to use a set
of data.
For my franchise to predictcustomer purchasing behavior,
for example.
So due to privacy concerns,using actual customer
transactional data is notfeasible.
So instead, what we can do is wecan generate synthetic data that
(14:28):
reflects the patterns and trendsof the original data set, and
then the synthetic data can beused to train the AI model.
And therefore, the predictionsthat will come out of the AI
model will be accurate, but theywill not compromise our customer
privacy.
So, I think synthetic data isanother, option for us to kind
of get over that hurdle ofsharing our actual proprietary
(14:51):
data with, a public AI model,such as ChatGPT.
So definitely look intosynthetic data if that's
something that you areinterested in doing.
So yes, we really summed up alot of different things here,
but I think the key takeaway is,make sure you do your research,
know that it's not safe to sharevery proprietary and sensitive
(15:16):
data with things like chat GPT.
I think a lot of us are able touse these platforms quite
successfully without having toshare proprietary data.
We definitely want to make surewe're not including names,
company names, or people's namesor anything like that when
sharing, Insights with theseplatforms.
But I think the next level of AIimplementation is really to get
(15:41):
onto one of these enterpriselevel platforms and start to,
develop your own data sets,create your own.
And, um, get a little bit moresophisticated and advanced with
your use of AI.
Well, that's it for today'sepisode.
Thank you for listening.
If you enjoyed what you heard,please feel free to give me a
review and, tune in to nextweek's episode.
(16:03):
Have a great week, everyone.
Hey everyone.
Welcome to the podcast.
This is the podcast where wehave real and relevant
discussions on business andmarketing in the franchise space
and talk all things marketingand AI.
And I'm your host Veera Shafiq.
Today we're diving into asubject that's top of mind for a
lot of marketing professionals,CMOs, and it's really how do we
(00:36):
use AI tools safely while beingmindful of our proprietary and
sensitive data?
So, you know, as we know, we'veall been using AI tools such as
Copilot, ChatGPT, Gemini,Perplexity on the list goes on
and on and on.
and really it's been giving usthe opportunity to drive
efficiency and insight andresearch and do our jobs a lot
(01:00):
better.
But the nagging question at theback of a lot of our minds is
what about the data privacy?
What about when I start to sharemy company's proprietary data
with the AI models?
For example, you know, I'muploading performance data,
(01:21):
marketing performance data, orI'm uploading some graphs or
some charts, even somecompetitive stuff.
Alright, what is safe to uploadand what should I be worried
about?
These are the kinds of questionsthat I'm hearing and I myself
have had.
Um, so just researching andreally understanding how these
(01:42):
platforms work has given me amuch better clarity on what I
should and should not be usingAI for.
On top of that, very topicalthis week is the subject of
DeepSeek, which is the new AImodel that came out of China and
just came out of nowhere as italmost seemed as if it came out
(02:04):
of nowhere.
It's a Chinese artificialintelligence startup that
recently gained significantattention for its open source AI
models.
And it has particularly ofinterest a model named R1, which
is supposed to rival.
A.
I.
Systems.
Such as Open A.
I.
S.
Chat.
G P.
T.
Offering comparable performancein areas such as reasoning
(02:27):
mathematics encoding.
So deep sea has achieved theseadvancements.
With a significantly lowerinvestment and development
costs.
And it's really kind of beenbrought to the attention in the
media that, you know, thiscompany has spent 5 million
developing its AI model,whereas.
(02:48):
Open AI spent a hundred milliondollars to do something of the
same caliber.
So, questions around thesecurity of DeepSeek also come
up.
And, you know, we wonder howsafe is it to use DeepSeek if we
wanted to test that out, andespecially if we're using
proprietary data, so today we'llbreak down exactly where and how
(03:08):
you should be sharing yourproprietary data and where you
should exercise caution.
So let's start with DeepSeeksince it's not It's very topical
right now.
It's the talk of the town.
DeepSeek has focused itsfoundational AI technologies and
committed to open sourcing onall of its models.
Regarding safety, there areseveral considerations.
(03:32):
So, censorship and bias is oneof them.
Their models have been observed,and this is all over the
internet, and if you read all ofthe kind of experience that
people have had testing it out,I also tested it out and noticed
similar kind of, um, attributes,is that, um, The DeepSeek models
have been observed to exhibitcensorship, particularly on
(03:52):
topics sensitive to the Chinesegovernment.
So, for instance, some of theexamples that I've heard about
is that, you know, AI, the AImay refuse to discuss subjects
like the Tiananmen Squareprotests or human rights in
China.
So, it is censored.
And then there are the securityconcerns.
So regarding, security, I wasreading an article in the
(04:14):
Guardian and it says that thecompany recently faced a
significant cyber attack leadingto a temporary suspension of new
user registrations.
So while deep seeks.
Has already addressed the issue.
It does highlight that there arepotential security, security
vulnerabilities, uh, with deepseek and then data privacy as a
(04:35):
Chinese company.
And, you know, we all know whatis going on with Tik TOK and the
whole, I guess, fear that theChinese, uh, you know, holding
the art data somehow, there is.
The fear that DeepSeek may besubject to local data
regulations, which could raiseconcerns about data privacy and
potential government access toour information.
(04:57):
So, I'm not going to sway oneway or the other, but I
personally, I'm going to remainwary of.
Deep seeks, content limitations,recent security incidents, and
the data privacy considerations.
And right now for me, I don'tfeel the need to use deep seek.
I am sure that might change inthe future, but right now I'm
going to stick with thefoundational models that are,
(05:19):
presented to us through open AI.
Anthropic, we have met as modelsand Google's models.
We have models coming out theyin yang.
So I'm going to stay away fromdeep seek for now.
That's, that's my personalchoice, but again, these are the
considerations that we do needto start to think about as these
AI models start to come out.
(05:39):
So let's talk about the AI datalandscape for a little bit
before we get into bestpractices.
Let's talk about how AI toolsinteract with your data.
So many of the publiclyavailable AI models, including
chat, GPT, and Collect and storeuser inputs to improve their
model performance and fortraining purposes.
(06:00):
And if you look at the privacystatements for all of these
models, you'll see that theystate that very clearly.
Chat GPT plus subscription has atoggle where you can toggle off
using your data for training.
So that's something that you cando, um, for franchise
corporations, specifically thetopic of, whether this data is
(06:22):
being used and how it's beingused can raise significant
concerns around specific thingslike confidential brand strategy
and marketing data.
We could be using competitivepositioning and pricing models
in our queries and our prompts.
Then there's the topic oflocalized franchisee specific
data.
(06:42):
And then the big one, which isCRM data and PII.
many brands in the franchiseindustry have been highly
scrutinized in verticals, suchas health and finance, and they
are certainly on the front lineswhen it comes to being really
accountable and, and highlyresponsible for their customer
data.
So let's talk a little bit abouthow to use these tools and where
(07:06):
we should kind of hold back andmaybe think about using a
different solution.
So tools such as chat GPT.
Perplexity, Claude, Gemini, theyare great for, handling publicly
available insights.
So if we want to do research onindustry trends or general
marketing strategies or marketresearch, this is all publicly
(07:28):
accessible data that can beshared with the models without
risk.
We can.
You know, go back and forth withthe models on all of this stuff
that is already publiclyaccessible.
The same thing applies toanonymized data sets.
So if we want to feed themodels, customer data,
operational data, uh, we can useanonymization techniques.
(07:51):
To anonymize the data,definitely make sure we're
removing all PII.
So no emails, phone numbers,addresses, names, all of that
good stuff.
But we can use techniques likedata masking or tokenization, or
we can aggregate data so thatit's not individual level data
to remove that PII and usedanonymized data sets.
(08:14):
So that's one way that you canget away with uploading your
data to things like chat GPTWithout worrying about it.
Another thing you can do, sowhen you're looking for market
research, competitive analysis,things of that nature, industry
shifts, you can definitely startto use, you can use chat, GBT
(08:34):
for that for audience behaviors.
Just make sure that you're notinputting proprietary insights
when crafting your prompts.
So just stay away from very,very highly sensitive
proprietary secret sauce type ofstuff.
and then finally, contentcreation and optimization.
This is a big one.
We know we can do this.
All day long till the cows comehome with no worries because we
(08:58):
can use GPT and co pilot togenerate copy and creative and
SEO strategies.
And this is all above board.
No worries about that.
obviously we need to be carefulin terms of copyrighted or
plagiarism and things like that.
But at the end of the day, thecopy and creative that, AI
generates is essentially uniqueand we can use that and not be
(09:22):
worried about, getting into anytrouble there.
And that there is an asteriskbehind that.
We do, need to be careful, withcreative and especially when
we're telling it to write thingsin the style of someone else or
in the style of a photographeror an artist, we need to be a
little bit careful there, butfor general purposes, marketing
content creation andoptimization is fine to use.
(09:44):
A chat GPT or a Claude, etcetera for.
So where do we need to exercisecaution?
Well, I would say, you know,back to that proprietary
marketing and brand data, anyinternal marketing strategies or
proprietary budgets orperformance data, or anything
that includes passwords to anyof your systems should
(10:04):
definitely not be shared with AImodels such as chat GPT, because
that does not ever guaranteefull data privacy.
So.
Just be careful about,explicitly going in and giving
chat GPT, carte blanche,everything that you have, which
is proprietary.
As I mentioned earlier, you needto clean that data, aggregate
(10:26):
that data, or anonymize thatdata before uploading it.
customer data and CRM insights.
So we talked about this.
Never input raw customer datainto AI tools unless you're
using an enterprise AI solutionwith strict privacy controls.
And we'll talk about that in alittle bit.
Uh, localized franchisee levelinsights.
So when we're talking aboutindividual franchise location
(10:50):
data, whether that be marketingperformance, financials,
anything proprietary, and again,anything that's a secret sauce
should be secured withininternal systems and not be
exposed to public AI tools.
And then finally, I would saypredictive modeling for business
growth.
So if we want to create our ownproprietary.
(11:11):
Predictive models, that's fine,but we need to build those on
internal secured frameworks, noton public AI platforms, because
obviously that's a secret sauce,right?
We're trying to create a verycustom predictive model for our
business and to grow ourbusiness.
So we don't really want to sharethat with, a chat GPT or, you
know, a model that's.
(11:32):
I guess, unless the voter is, ispotentially using that data to
train other data on.
So, if you are looking toleverage AI at scale.
Without compromising yourproprietary data.
There are some solutions.
I would say the most advancedsolution is using private or on
prem on premises.
(11:53):
That is AI models.
this is probably the mostexpensive and involved solution.
So.
Instead of using publiclyavailable AI, some franchise
corporations are investing inprivate AI models deployed on
premises or within secure cloudenvironments.
So some of these types ofplatforms include IBM Watson for
enterprise AI and Google VertexAI.
(12:15):
So these are where you have yourmodels downloaded on premises,
they're not in the cloud, andnow you are actually creating
your own proprietary models.
within your own company fourwalls or within a secure cloud
environment.
the second level would be, anenterprise AI.
(12:38):
System that has data governancecontrols.
And this includes things likeMicrosoft co pilot, Salesforce,
Einstein, Adobe Sensei, theseare enterprise grade AI with
strict data governance, and theyensure that your proprietary
data remains confidential.
So for example, Microsoft copilot enterprise stays within
(12:59):
the Microsoft 365 suite.
So all of your data, which sitsin Microsoft 365 stays.
In the four walls of yourenterprise.
And there's no leakage of thatdata outside into the public
domain.
AI powered marketing automation.
There are platforms for thatright now that have their own AI
(13:21):
models, which are also containedwithin your own datasets so that
you don't have to worry aboutany data leakage.
Things like HubSpot, Marketo andSprinkler are now, incorporating
AI powered marketing automation.
that keeps your proprietary datawithin secured systems.
So you could be free and safe touse those.
And then finally, somethingthat, which is a little bit
(13:44):
different and it's usingsynthetic data for AI training.
So I think synthetic data issomething that we're going to
start hearing a lot more about.
and it's something that we canuse to artificially generate
data sets.
That mimic the real data, butwithout exposing actual business
insights.
So this really ensures securitywhile maintaining the model
(14:05):
accuracy.
So for example, if I want toupload, or I want to use a set
of data.
For my franchise to predictcustomer purchasing behavior,
for example.
So due to privacy concerns,using actual customer
transactional data is notfeasible.
So instead, what we can do is wecan generate synthetic data that
(14:28):
reflects the patterns and trendsof the original data set, and
then the synthetic data can beused to train the AI model.
And therefore, the predictionsthat will come out of the AI
model will be accurate, but theywill not compromise our customer
privacy.
So, I think synthetic data isanother, option for us to kind
of get over that hurdle ofsharing our actual proprietary
(14:51):
data with, a public AI model,such as ChatGPT.
So definitely look intosynthetic data if that's
something that you areinterested in doing.
So yes, we really summed up alot of different things here,
but I think the key takeaway is,make sure you do your research,
know that it's not safe to sharevery proprietary and sensitive
(15:16):
data with things like chat GPT.
I think a lot of us are able touse these platforms quite
successfully without having toshare proprietary data.
We definitely want to make surewe're not including names,
company names, or people's namesor anything like that when
sharing, Insights with theseplatforms.
But I think the next level of AIimplementation is really to get
(15:41):
onto one of these enterpriselevel platforms and start to,
develop your own data sets,create your own.
And, um, get a little bit moresophisticated and advanced with
your use of AI.
Well, that's it for today'sepisode.
Thank you for listening.
If you enjoyed what you heard,please feel free to give me a
review and, tune in to nextweek's episode.
(16:03):
Have a great week, everyone.
Popular Podcasts
On Purpose with Jay Shetty
I’m Jay Shetty host of On Purpose the worlds #1 Mental Health podcast and I’m so grateful you found us. I started this podcast 5 years ago to invite you into conversations and workshops that are designed to help make you happier, healthier and more healed. I believe that when you (yes you) feel seen, heard and understood you’re able to deal with relationship struggles, work challenges and life’s ups and downs with more ease and grace. I interview experts, celebrities, thought leaders and athletes so that we can grow our mindset, build better habits and uncover a side of them we’ve never seen before. New episodes every Monday and Friday. Your support means the world to me and I don’t take it for granted — click the follow button and leave a review to help us spread the love with On Purpose. I can’t wait for you to listen to your first or 500th episode!
The Breakfast Club
The World's Most Dangerous Morning Show, The Breakfast Club, With DJ Envy And Charlamagne Tha God!
The Joe Rogan Experience
The official podcast of comedian Joe Rogan.