Zeus Kerravala, Principal Analyst ZK Research

AI Skeptics: Get Started On Your AI Journey

Industry Voices AI & ML
Zeus Kerravala Headshot
A green slide with a headline that says, “Get Started On Your AI Journey.”  With a Juniper Networks logo center on the top and a Juniper Networks logo small bottom right.

You've an AI solution and are ready to get started on your AI journey. Where do you begin?

AI isn't a magic wand you can wave to fix all your company's problems. It takes time and effort to onboard AI and set it up for success, just as it would a new hire on your team. In this video, you can hear about some ways to integrate AI into your processes and work with it to continually optimize results. Hear real stories from IT professionals who've lived it. You’ll learn what it took for IT professionals like you to integrate AI into their day-to-day operations and the business outcomes they were able to achieve.

Show more

You’ll learn

  • How to set up AI for success

  • What it takes to integrate AI into your day-to-day operations

Who is this for?

Network Professionals Business Leaders


Zeus Kerravala Headshot
Zeus Kerravala
Principal Analyst ZK Research

Guest speakers

Sharon Mandell Headshot
Sharon Mandell
CIO, Juniper Networks
Christian Scholz Headshot
Christian Scholz
Axians Networks & Solutions


0:00 [Music]

0:06 hello everybody i'm zeus caraval

0:07 principal analyst with zk research and

0:09 i'm hosting uh this episode of ai

0:12 skeptics

0:13 in our previous conversations of the ai

0:15 skeptific series we've heard about

0:17 perspectives around how

0:19 ai or artificial intelligence will help

0:21 it practitioners

0:23 and technology channel partners

0:25 accomplish more

0:26 in today's conversation i'll be joined

0:28 by uh sharon mandel ceo of juniper

0:31 networks uh jerome say hi

0:34 hi uh thank you for having me i'm

0:37 excited to be here today

0:39 and we're also joined by juniper

0:40 network's ambassador christian scholes

0:42 of axion networks and solutions

0:44 uh and so what you say as well christian

0:48 yeah hi everybody um yeah thank you for

0:50 having me um great to be here

0:52 and it's a it's a it's a great duo to

0:54 have because they both share some

0:55 takeaways on what it's like to actually

0:57 implement the ai and work alongside it

1:00 and uh you know sharon i'll start with

1:02 you um

1:03 i you know you and i have been talking

1:05 about ai for quite some time and it's

1:07 kind of a funny topic because

1:09 i think everybody kind of conceptually

1:11 understands the value although there's a

1:13 lot of skepticism and

1:15 uh trepidation around it so

1:17 just talk a little bit about the process

1:20 you went through rolling out mist inside

1:22 juniper well sure well rolling out mist

1:25 is is

1:26 fairly straightforward um because a lot

1:29 of it comes out of the box so

1:31 um i think for a lot of people

1:33 they hear ai they think oh my god i have

1:36 to hire

1:37 you know an army of data scientists i

1:40 have to do

1:41 all of this work and

1:44 in this case the ai is built in and so

1:49 it

1:50 it it starts with

1:52 models that are trained and then it gets

1:54 to understand your network and

1:57 so it's it's not a lot

1:59 different than um

2:02 installing any other wi-fi network

2:04 except that

2:05 it's easier because we we build in the

2:08 pre-configuration and and make that that

2:11 simpler i think the other challenge is

2:13 you know people start to

2:15 wonder oh my god you know are my is my

2:18 job gonna go away as a network

2:20 administrator

2:21 and i think that um very quickly people

2:24 understand that that's not true right

2:27 because

2:28 um

2:30 the the situation was they always had

2:32 more work to do than there were people

2:35 to do it um they were solving problems

2:39 um that you know were like finding a

2:41 needle in the haystack because the scale

2:43 of the data that had to be analyzed to

2:45 solve them was so large and so really

2:48 what what introducing ai into that mix

2:52 did was it took away

2:54 the mundane problems it made some of the

2:56 very complex ones much easier

2:59 to solve and then they got to go back to

3:01 the work that they really like doing

3:03 which is architecting and designing

3:05 networks not

3:07 um troubleshooting them

3:09 yeah it's it if the analogy you used

3:11 about it sort of being in bed it's

3:12 interesting because it's not unlike an

3:14 automobile today right there's ai in

3:16 your car that helps with lane change

3:18 alert parallel park assist you know

3:20 adaptive cruise control and it's not

3:22 like we need to be data scientists to

3:23 drive today we just need to go in and it

3:26 makes us smarter better drivers

3:27 similarly it sounds like your

3:28 implementation of ai made your engineers

3:30 smarter better engineers

3:32 so uh christian uh now you've been using

3:35 this a couple of years now so why don't

3:37 you talk about your journey

3:38 yeah um i started with uh

3:40 i was around 2020 when i started with

3:43 mist

3:44 i started with adopting it in my home

3:47 so i built this new house for me and my

3:50 wife

3:50 and obviously it

3:52 always was my dream to build a smart

3:54 home a true smart home so i started with

3:56 roughly 200 sensors i'm currently at

3:59 roughly 300 to 350 sensors

4:02 and yeah as you can imagine with that

4:04 amount of sensors

4:06 you don't want to administer all of them

4:08 by hand so you basically want to to

4:11 architect what you want to achieve like

4:13 i want a certain temperature or i want a

4:15 certain air quality or

4:17 yeah whatever you feel comfortable with

4:19 and then you want the sensors to

4:21 interact with each other and the

4:23 orchestrator or the ai part to basically

4:25 control all of that

4:27 to yeah

4:28 fulfill exactly what you asked it to do

4:31 that's interesting so uh i can't wait to

4:33 see that's the next analyst summit we

4:35 should do at your house um

4:41 and uh another question for both you and

4:43 sharon i'll start with you again um how

4:45 long did it start seeing did it take to

4:47 start seeing value from ai

4:51 well you know each use case is a little

4:53 bit different right so um you know you

4:56 asked me specifically about miss that

4:59 happened very very quickly because it

5:01 came with a certain

5:03 um

5:04 set of um

5:07 features and and and problems it could

5:10 solve straight out of the box right so

5:12 so immediately we we could see

5:15 um

5:16 we could see a

5:19 ticket resolution things like that um

5:21 other solutions uh have different

5:24 requirements to get them going so

5:27 um as as we've implemented some chat bot

5:30 technologies right which people consider

5:32 ai because in theory

5:35 you know it's it it it's it's talking to

5:37 a machine and getting answers um in that

5:40 case there there was a lot more work to

5:43 do to get effectiveness and maybe we

5:45 never with with the particular

5:47 technologies we've played with so far

5:49 we've never gotten quite as far as we'd

5:51 like because

5:52 there was so much work we had to do

5:55 which required an army of people we

5:57 didn't have

5:58 to prep the data

6:00 to create a knowledge base to structure

6:02 questions properly

6:05 that

6:06 it

6:07 we never really reached that full

6:09 potential so i think you know people

6:11 have to understand that there's a range

6:14 of of ai type solutions all the way back

6:18 to the the simplest which is just

6:20 um you know predictive analytics right

6:23 where where you're using

6:25 ml models to

6:26 to draw conclusions and take next

6:28 actions and in that case

6:30 um you know you may be building it from

6:33 the ground up and then really preparing

6:36 the data is the largest job and can take

6:39 some time so i think each company has to

6:42 understand

6:43 um the value of the use case that

6:45 they're trying to solve really

6:47 understand the products they're buying

6:50 what needs to go

6:52 into

6:53 um

6:54 training the model there is it is it

6:56 trained out of the box and then is gonna

6:59 adapt and learn more about you and get

7:01 more specific about you

7:03 over time or are you building that model

7:06 from scratch because there's no way that

7:09 that problem can be solved because it's

7:11 truly uniquely about your company and

7:13 then there's going to be a different

7:14 level of effort and you ought to be

7:16 convinced that the problem you're

7:18 solving

7:19 is has that business value that that's

7:21 worth that additional effort

7:23 yeah so it sounds like there's in some

7:25 ways two types of ai there's the

7:27 embedded

7:28 uh kind of invisible ai that people

7:30 don't even know is there that helps the

7:31 network run better

7:33 right and then there's other ai that

7:35 the engineer would interact with that

7:37 does need a bit of tweaking and training

7:39 and things like that like with chat bots

7:41 that actually changes the way they work

7:42 is that a good way to think about it

7:44 yeah and again you know ai when i i'm

7:47 speaking about ai i'm not speaking just

7:49 about ai to serve the network but

7:51 ai to solve a broad range of problems

7:53 right so we use ai to make decisions in

7:56 supply chain

7:58 we use

7:59 you know the chatbot example

8:01 we have chat bots at the help desk we

8:03 have chat bots through the procurement

8:05 tools we have chat bots you know to help

8:08 our employees navigate

8:11 how to find things in in the marketing

8:13 department so

8:14 they get applied to all different kinds

8:16 of problems and each

8:18 implementation um you know watson is

8:21 different than servicenow's um chatbot

8:24 which is oracle's chatbot and what they

8:26 expect you to do and in some cases

8:29 some companies that provide those

8:31 technologies out of the cloud

8:33 they bring as part of their professional

8:35 services offerings the people who do all

8:37 of that data work because they've done

8:39 it a bunch of times before with

8:40 companies and they're not learning on

8:41 the fly so there's there's a whole range

8:44 of level of effort depending on the

8:46 nature of the solution

8:48 yeah the ios caution people do when

8:50 they're using ai to interact with

8:51 customers

8:53 make sure it works well because you can

8:55 actually have a very negative reaction

8:56 if it doesn't work well i think we've

8:58 all experienced that chat bots that

9:00 you know don't really understand what

9:01 you're saying but they've come a long

9:02 way you know in the last five years and

9:04 so i think for companies that had a bad

9:06 experience with let's say five six years

9:08 ago it's really good again i'm gonna

9:10 bounce back to you what was your you

9:12 know you set up in your own house which

9:14 is kind of cool can you just kind of

9:16 double click on the learning process

9:18 that you went through for your iot setup

9:21 yeah well at first it was just adding

9:23 all the sensors and that's basically

9:25 where all the magic starts because at

9:27 the beginning i just had a bunch of

9:28 sensors and yeah i had a pre-trained

9:31 model for this whole ai setup but it

9:34 wasn't really aware of what i wanted in

9:36 my house so it was in this constant

9:38 learning phase for about i would say six

9:41 to seven months

9:42 um

9:43 and at some point i even had to tell it

9:44 okay if there is now a snowstorm in june

9:48 for example because sometimes the

9:50 weather is just crazy for one day um

9:52 feel free to ignore that and stuff like

9:54 this because uh yeah if i'm if i

9:56 wouldn't have told it that um snow and

9:59 summer is something that it has never

10:00 experienced before so even for me that

10:02 was very weird so uh

10:04 yeah

10:06 and it was very interesting because at

10:08 at the beginning it's it almost feels

10:10 like a very

10:11 dumb piece of software

10:13 um and then the more you interact with

10:15 it the more you train it the more you

10:18 work with it every day almost like you

10:20 would work with a new co-worker the more

10:23 it learns about your let's call them my

10:26 needs like what temperature does he like

10:27 or what humidity does he like and

10:30 when do i need to open the window and

10:31 close the window when do i need to shut

10:33 the blinds because it's getting too

10:35 bright and stuff like that um

10:37 yeah now it's just working it's like

10:39 magic i mean i'm walking through my

10:41 house wherever i come into the room the

10:44 light gets turned on when i leave the

10:46 room the light gets turned off and yeah

10:48 it basically learned all of that from

10:49 the behavior that i showed it

10:52 so uh yeah yeah

10:54 that's interesting

10:55 i find sometimes those systems can

10:56 always get too smart i've got some smart

10:59 stuff

11:01 oh yeah

11:02 it shuts things off and you don't want

11:04 it to and things like that but i guess

11:05 that's that's all part of the learning

11:07 process what does it take to sort of vet

11:09 an ai solution or vendor like how do you

11:12 evaluate them

11:13 yeah so

11:14 i think first of all

11:16 you have to really understand the

11:18 problem you're trying to solve

11:21 and

11:22 then

11:22 understand

11:24 what went into the vendors thinking

11:27 about the problem they were solving and

11:31 the first thing is are they

11:33 closely enough aligned that

11:36 the models that got baked into theirs or

11:39 that they're going to apply to your

11:40 problem are actually applicable and

11:44 going to solve the problem after that

11:46 you then have to especially in the case

11:48 of sort of the prebaked

11:51 ai you need to understand the types of

11:53 data that it was trained on and whether

11:56 again that data closely enough

12:00 reflects or is broad enough

12:02 to at least start creating that value

12:05 out of the box so that it can learn and

12:07 continue from there you have to

12:09 understand

12:10 how does the vendor think about keeping

12:13 its models up to date so you know you

12:15 saw

12:16 during the pandemic certain ai solutions

12:19 that

12:20 were built based on

12:22 well-understood behaviors about how the

12:24 world worked

12:26 so you know for retail there were you

12:28 know years and years of data about

12:30 people going in and out of stores all of

12:32 a sudden it stopped well since the data

12:34 is the fuel for the model suddenly that

12:37 model didn't work anymore in answering

12:40 some of the questions that retails one

12:42 example you saw

12:43 you saw a number of others obviously

12:46 certain things in the supply chain

12:47 changed pretty radically so

12:49 you have to understand where data

12:52 discontinuity might break that

12:54 technology

12:55 and what's your likelihood for that so

12:57 it's it's it's really all about

13:00 primarily that many of the algorithms

13:02 that are the basis for ai

13:05 they're well understood statistical

13:07 analysis and so first of all you know

13:10 are they applying the right one to the

13:11 problem most likely if they've had any

13:14 success in the market they are but then

13:16 it's really about the fuel and the data

13:18 that that feeds that model

13:20 how do they deal with the drift that can

13:23 happen so those are some of the

13:25 considerations i think you know you need

13:27 to look at but

13:28 it's always first

13:31 does does the solution match my problem

13:33 and do i really deeply understand my

13:35 problem that i'm trying to solve and i

13:37 think that underscores the importance of

13:38 pre-built uh ai systems uh obviously

13:42 you know the the more

13:44 turnkey it is the

13:46 the easier it is to implement i think

13:48 you know from an engineer's standpoint

13:50 where i've seen

13:51 some hesitation uh from it pros

13:54 is that uh there's a fear that you need

13:56 to be a programmer and a data scientist

13:58 and frankly um as part of the media

14:01 right i read a lot from media the we

14:04 that was a big theme many years ago that

14:06 everybody in the world would turn into a

14:08 programmer and everybody would be have

14:10 to become a data scientist

14:11 and i think that scared some people and

14:13 that's not true i do think from a skill

14:15 set perspective though um

14:18 engineers do need to think more about

14:19 becoming a software power user right but

14:22 that's with or without ai you look at

14:24 the sophisticated network systems today

14:26 not just from yourself but even from

14:28 your competitors everything's you know

14:30 tapped into with apis you know every you

14:32 know everything's available via sdks and

14:34 things and so um you know software is

14:37 the way forward and that lets you access

14:39 a lot of these great features so like i

14:41 said you don't need to be that the data

14:42 scientist and things like that but you

14:44 do need to be comfortable with software

14:45 so

14:46 um you know and christian is that would

14:48 you like to you know maybe expand your

14:49 own insurance i do

14:51 yeah i mean it gives you a lot more

14:52 flexibility with this new skill set that

14:55 you build your like you said you're

14:56 becoming more of a designer you don't

14:58 need to be the one

15:00 um coming from a network implementation

15:02 point of view where you go to every

15:04 switch and configure every vlan on every

15:06 port you simply design okay this is the

15:08 vlan that i want here are my devices go

15:11 for it um so yeah you have the weekends

15:14 back you have all those night shifts

15:15 back so uh

15:17 i think it's a it's a great thing so

15:19 it's not threatening the jobs at least

15:21 from the way that i see it and what i've

15:23 seen with customers

15:25 is it's not threatening the job it's

15:27 giving them more air to breathe so they

15:29 can focus on the important business

15:31 things and not need to waste any time on

15:34 those yeah little

15:36 times the reality is i'm not going to

15:38 sit here and say that

15:40 the infusion of ai in technology isn't

15:43 going to get rid of some jobs because it

15:45 is but

15:46 this this is just part of technology

15:48 maturation we don't have mainframe

15:50 engineers today we don't have people

15:51 working on token ring you know things

15:53 change over time and everything right

15:56 and every engineer i talk to the one

15:58 piece of advice i give them is if you're

16:01 doing things today forget about your the

16:03 company you work for think about your

16:04 own selfishness your own needs and your

16:06 own job if you're doing things today

16:08 that aren't

16:09 additive to your resume right don't do

16:12 them find a way to automate them out of

16:14 your job and that's really the power of

16:17 what you were talking about christian

16:18 that ai can do it's like you don't have

16:19 to come in on weekends you don't have to

16:21 spend hours and hours updating vlans and

16:23 frankly nobody's going to hire you for

16:25 your next job yeah because you can do

16:27 those things right so

16:28 exactly

16:30 and when i think about um you know ai

16:32 and the network i do think one of the

16:33 things to look for though is a solution

16:36 that works across the network it is

16:39 valid because the experience that a user

16:42 or customer gets is based on how

16:44 applications work across the network not

16:47 just within specific silos and

16:49 historically we've treated networks and

16:51 styles and that's why even today you'll

16:53 see

16:54 ai solutions for wi-fi ai solutions for

16:56 sd-wan ai solutions to the data center

16:59 things like that and and while those

17:01 may

17:02 work

17:03 well they don't really give you a view

17:06 of the end-to-end network and so you

17:08 know in data science says there's an

17:10 axiom right that states that good data

17:11 leads to good insights well

17:13 silo data will lead to solid in size or

17:15 fragmented insights and i think uh you

17:18 need to have that kind of end-to-end

17:19 view so here i'm just i don't know

17:22 yeah so so zeus

17:25 i i

17:26 as kind of a cio who has to not only

17:29 integrate the network but

17:31 a broad series of applications and

17:34 experiences both for our customers and

17:37 and our employees

17:38 um

17:39 across a broad spectrum of you know

17:42 functionality

17:44 because

17:45 you know the pre-built ai solutions

17:49 they tend to be fairly narrow in focus

17:51 again because you have to curate

17:54 uh a data set

17:57 um that's

17:59 unique to that problem and so

18:02 we haven't yet seen the company

18:04 you know sort of like oracle and sap

18:07 came in and said well i'm going to

18:08 integrate everything from financials

18:11 through supply chain and then you know

18:14 work their way back to you know order

18:16 management the sales front office right

18:18 you've seen these companies come in and

18:20 and provide

18:22 application suites that that you know

18:25 cover very broad spectrums of

18:26 functionality and it took them years to

18:28 build it you haven't really seen that

18:30 use it you know across

18:33 that sort of cross-functional ai view so

18:36 you know i i joke sometimes that we're

18:38 going to have the war of the bots right

18:39 where

18:40 the bot from vendor a is gonna give you

18:42 a different answer and start fighting

18:44 with the bot from vendor b

18:46 um and i what i what i see starting to

18:49 happen is um some of the

18:52 sort of incident management solutions

18:55 that people tend to interact with right

18:57 so a person submits a problem

18:59 and then a person's on the receiving end

19:02 of that you know it might go through

19:03 some escalation path but ultimately you

19:05 get to a subject matter expert

19:07 who

19:08 you know will solve your problem

19:10 well

19:11 you know as you've seen in this world of

19:13 sre and devops it's rarely one subject

19:16 matter expert if the problem is complex

19:18 enough well now some of these solutions

19:20 are starting to treat the bots as

19:23 um

19:24 a part of the conversation um and so you

19:27 might have one or more bots combined

19:29 with five people

19:31 offering insight into

19:33 what might be the solution to the

19:35 problem

19:36 but that's still a step away

19:38 from

19:39 you know some kind of integrated

19:41 perspective or

19:43 integrated world view that i think will

19:45 will ultimately need to have

19:48 in certain areas right um

19:51 but it is because of the nature of how

19:53 these solutions are built um and how

19:55 important curated data sets are

19:58 yeah i think that's uh

20:00 the the war that both already happens in

20:02 social media

20:07 we just we don't want to come to our

20:08 corporate systems though so

20:10 um

20:11 anyways i just have one more question uh

20:13 for both of you uh and we'll do this um

20:16 you know i'd like to ask this question

20:17 because i think it's helpful for people

20:19 watching this uh what's one piece of

20:22 advice that you would give to it pros as

20:26 the 19 leaders as they embark down their

20:29 i.t journeys and that or their ai

20:31 journey they'll actually go first and i

20:32 think

20:33 it kind of parlies off what christian

20:35 said

20:36 um i think you need to be patient with

20:38 ai and you need to understand um and

20:40 this can actually help you evaluate real

20:42 ai solutions versus fake i solutions ai

20:45 washington is rampant out there and

20:46 every system used says they use ai

20:49 and a good way to understand if

20:51 something's real ai or not

20:53 is to see how it progresses and how it

20:55 learns over time as christian said at

20:57 first it may be kind of dumb you may

20:59 look at it and go well this isn't very

21:01 helpful at all but over time

21:03 it does get better and it gets smarter

21:06 and it understands your environment

21:07 better in fact i think juniper does a

21:09 nice job of highlighting the efficacy of

21:12 it and you know being actually

21:14 demonstrating you know the

21:16 the quad and quantifying the how it's

21:18 changed over time and so real ai isn't

21:21 just fancy rules based systems and if

21:23 you go with them one of those those

21:24 rules based systems um you're going to

21:26 be constantly updating it and constantly

21:29 changing the rules

21:30 um and so you have to understand that ai

21:32 is not perfect out of the gate but it

21:34 does get better over time and it will

21:35 help you more and more and so you need

21:37 to be patient with it and so sharon

21:40 what's some advice from you

21:42 the hardest part of adopting technology

21:44 is fear of change right and

21:47 and

21:48 to your point that you made earlier is

21:50 this which was

21:52 you know yeah some things are going to

21:54 change and some types of roles and work

21:56 will go away

21:58 um

22:00 it's part of the natural evolution of

22:02 technology so as you know i i say

22:05 experiment right try try different

22:08 things try different flavors see what

22:10 works for you

22:12 understand the nature of your company

22:14 and your business

22:16 and experiment um

22:18 and and some things are going to be you

22:20 know as we've discussed much more useful

22:23 straight out of the gate and some are

22:25 going to be um acquired taste because

22:27 the more the more unique

22:30 you know where

22:31 you when you apply

22:33 ai technology to something that makes

22:36 you really special and unique you're not

22:38 likely to find it out of the box right

22:40 again because of that

22:41 curated data so

22:43 you know i say kind of play with a lot

22:45 of different things and and and try

22:48 um and learn yourself from them um where

22:52 you're going to have to change and adapt

22:55 and

22:56 um

22:57 like i said just don't be afraid and get

22:59 people comfortable

23:00 um slowly and that that may go back to

23:03 your patience right i mean as is always

23:05 the case

23:06 um

23:07 technology

23:08 at some level it creates that that's

23:11 disruptive creates change that people

23:13 are uncomfortable with and i think part

23:15 of being patient with ai is being

23:17 patient with the people you're trying to

23:19 get

23:20 to use it so um a lot of experimentation

23:25 experiment and uh be patient experiment

23:27 christian uh what advice would you give

23:29 here yeah i can also echo that don't be

23:31 afraid of it um when i when i started

23:33 with this it almost felt like i'm

23:35 teaching a child or a new colleague all

23:38 of the stuff and at first it felt like

23:40 i'm doing more work

23:42 and i thought like okay if i now have to

23:44 explain everything and basically teach

23:46 everything will this really benefit me

23:48 but after some time you will see the

23:50 benefit and

23:51 the model learns and everything comes

23:53 together and yeah like i said you will

23:56 get

23:57 more

23:58 more free time to do the stuff that you

24:00 really want to do and less of the

24:02 hideous little tasks that yeah just

24:05 waste your time so be patient work with

24:07 it

24:08 um yeah make it your colleague basically

24:11 explain everything at first it's it's

24:13 yeah you need to explain everything of

24:15 course because for the ai it's brand new

24:17 what you're just telling it

24:18 depending on the type of model that you

24:20 use but uh yeah it will be

24:23 so

24:24 worth it so don't be scared

24:27 all right well uh that was a great

24:28 discussion i'm just gonna i think wrap

24:31 up by saying uh

24:33 alekko said don't be afraid of it ai has

24:36 already proven in many industries it

24:38 makes us better drivers it makes contact

24:40 center agents smarter right and i think

24:42 from an engineer's perspective it's

24:44 going to make you a better smarter

24:46 engineer it's going to let you do a lot

24:48 more fun things right and as a former it

24:51 pro i can tell you nobody here in it pro

24:54 to sit and look through router logs and

24:56 update vlans all day long to just that

24:58 just boy that's just if you get

24:59 enjoyment out of that maybe

25:01 you know um you know

25:03 you should you know seek out some other

25:04 forms of enjoyment or something so

25:06 anyways uh ai's coming uh there's really

25:09 you know it's gonna be infused

25:10 eventually in everything that we do it

25:12 already is on the consumer side so uh

25:15 you know christian sharon thanks for

25:17 joining me today i'm zeus caraval from

25:18 ck research and thanks for watching this

25:20 episode of ai skeptics

25:24 [Music]

25:29 you

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