SPEAKER_00: i came to california and eric and i went for a walk around the mountain group campus and you know he spent an hour walking around he said you have 10 more interviews after this and he says well we really i'd really like you but you know this is a job which requires you to sell advertising in europe and you've never sold advertising before i'm like so should that does that mean

SPEAKER_01: i should just pack my my suitcase and go back or take my bag and go back to london because it's not my job for me he's like yeah and he was very very sort of thoughtful he said listen the business model of google is going to evolve multiple times in the future so i'm not sure we

SPEAKER_02: need to go find ourselves the best ad sales executive we need to find ourselves a good executive who can roll with the punches and adapt this week in startups is brought to you by

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SPEAKER_05: legal needs learn more at link squares.com twist all right everybody welcome back to this week in

SPEAKER_06: startups it's all star summer we're getting all the amazing entrepreneurs ceos investors in the industry because it's the summer they got a little bit of time i might be able to get them on the schedule and then

SPEAKER_07: you get the benefit today no different i'm super excited to have nikesh aurora on the program he's the ceo of palo alpa networks but before that you know him because he was the president of softbank and uh was going to be the heir apparent uh masayoshi sam we'll get into that and before that

SPEAKER_13: he was one of the earliest uh google executives and was part of that early google uh cadre that included

SPEAKER_14: cheryl sandberg and tim armstrong and marissa and just all those incredible executives who went on to do great things so the cash welcome to the program thank you jason thank you for having me

SPEAKER_06: yeah yeah so i i wanted to start with you know google because what an amazing time to go to google and how did you wind up getting the job at google how did you become aware of google i know you immigrated to the united states i we had talked about that at some point and you came here with nothing um so i'm kind of curious how you got here with nothing and then wound up at google feels like the american

SPEAKER_19: dream yeah there was a bunch of stops before that jason so i actually went to school in boston went to business school uh did a little bit of the buy side investment thing which didn't quite work out for

SPEAKER_21: me well i liked i i did fine i just didn't want to sit in a room you say you went to school in boston

SPEAKER_25: that means you went to harvard yeah no i went to northeastern oh okay see you see people who go to harvard and we're like i don't want to say i went to harvard because people are going to just hate me

SPEAKER_26: well i can't say i went to harvard because i didn't go there exactly i didn't go to school in boston i went to northeastern i went to boston college yeah so but you immigrated yeah yes i did and

SPEAKER_01: i worked at fidelity and i worked at putnam and then i decided to move to london germany and i worked at t-mobile at that point in time uh what time period is this this is 1999 to 2004 this is when t-mobile bought t-mobile usa it used to be called voice stream yeah i was part of the team at t-mobile

David Friedberg: we did that and this is right as the dot-com market fell apart uh so you got to witness that

SPEAKER_35: and then the down market of those 2002 to 2006. i wrote a sell note in 1999 which i still have

SPEAKER_01: which i said i cannot understand how to value stocks anymore it's november 99 and i sold everything and i

SPEAKER_42: quit my job as a buy side analyst did you do the same thing in 2021 by chance or was it i i wasn't i

David Friedberg: wasn't an investor in 2021 yes so that takes a lot of courage um and fortitude to be in the middle of a roaring party and saying you know what this party's not sustainable it makes no sense the

SPEAKER_50: cops are going to be here any minute and they're going to bust this party and it's going to be over

SPEAKER_52: and the lights are coming on what what what gave you the um fortitude to kind of write the memo i'm curious well it must be not popular at the time yeah in hindsight it's wonderful i was just enjoying

SPEAKER_55: the party had a good buzz going and i said i've had enough let me go out and go home and i didn't see

SPEAKER_19: the cops getting there so now it looks like i was so smart the cops showed up right after i left

SPEAKER_57: this is a key thing i have this discussion with my wife all the time i'm like you know it'd be a great

SPEAKER_59: time to leave this party right now and she's like this party's amazing i'm like exactly that's right

David Friedberg: this is the peak and knowing the peak of the party and when it might be a good time to get some rest to

SPEAKER_14: get some sleep before it kind of gets dark uh is a key part of doing this so uh yeah yes and i was

SPEAKER_62: and then i was at t-mobile in germany and i did that for four or five years and i was 34 years old and

SPEAKER_00: i had this moment where i said wait so i'm flying every week from london to germany i'm working with people who are 10 to 15 years on average older than me i'm doing marketing in germany and there's got to be something else i should be doing so i decided to leave that job and i was having lunch with a friend of mine who says listen this headhunter called me about this tech company from the us was looking for somebody to run europe uh my job's too big maybe you might be some this might be something

SPEAKER_64: you were interested in yeah so my mac company was of course google and i said okay i'll take a phone

SPEAKER_00: call or i'll take an interview and i did that and uh it was october 2004 right about when google went public they had not hired a single senior person ever from the outside all the 12 vice presidents

SPEAKER_01: were internally promoted and uh i think uh larry and sergey were probably on their second trip to europe ever in the history of google they happened to be passing through london so and one thing led to another i ended up getting an interview with sergey walking through the british museum wow then we

SPEAKER_52: walked around what was that like what was sergey like at that time in 2004 i mean i just got in public

SPEAKER_30: it's no different than than he is today you know it's bubbly curious running around larry was busy

SPEAKER_71: touring the the museum with his uh part of his family and sergey was the chosen one to make to the

SPEAKER_01: interview and we walked around we talked about uh we walked into the rosetta stone and and uh i just

SPEAKER_00: spent five minutes in a museum shop before that i'm not a museum goer but lo and behold there's too many

SPEAKER_72: rosetta stones there so i'd read a little bit about it so i started talking about it so what do you think

SPEAKER_01: about translation do you think google translations translate is going to work so you're talking about that and that led to me coming to martineau spending two days there and long story short i ended up as

David Friedberg: head of google europe what was his pitch to you as to the ambition of google and where it was going

SPEAKER_07: because it obviously has become a much bigger enterprise than the enterprise that you joined 19 years ago so so what was his pitch to you of like what they were going to do

SPEAKER_79: i think his very simplistic pitch he just basically said listen the world is getting more and more

SPEAKER_55: connected there's more and more information around us it's going to be hard to parse through it without any sort of something helping you out and the thing that's going to help you out is google

SPEAKER_01: and you didn't have to be a genius to figure out that you know even in that short period of time what is it like four million people connected to the internet in 1998 in 2004 probably tens of millions

SPEAKER_55: it wasn't hard to believe that over time and this number could keep rising you know none of us

SPEAKER_65: are smart enough to say that three and a half billion people will be connected but it's going to be more than that and don't forget i was out of a job right and you were a marketing executive and

SPEAKER_59: they had a very unique idea around advertising and connecting marketers with customers so what was your

SPEAKER_84: first gig there was it working with the with the ad networks so um you know i came to california and

SPEAKER_00: eric and i went for a walk around the mountain group campus and you know he spent an hour walking around he said you have 10 more interviews after this and he says well we really i'd really like you but you know this is a job which requires you to sell advertising in europe and you've never sold

SPEAKER_01: advertising before i'm like so should that does that mean i should just pack my my suitcase and go back or take my bag and go back to london because this is not my job for me he's like yeah and he was very very sort of thoughtful he said listen the business model of google is going to evolve multiple times in the future so i'm not sure we need to go find ourselves the best ad sales executive we need to find ourselves a good executive who can roll the punches and adapt um so very insightful by eric very

SPEAKER_19: no eric's very inflightful for the most part and strategic yeah yeah and then uh you know i ended up my

SPEAKER_01: first gig was to run europe uh and and yes it was primarily selling ads but we had like nine offices and one real office eight regis offices and in five years i opened 26 physical locations for google we hired 4 000 people and we went from i think 800 million in revenue 4 billion wild what a run yeah

David Friedberg: and you see you're joking saying regis offices you mean like the pre we were they had regis office shares which we could rent an office with a lock on the door with old corporate furniture in it like

SPEAKER_19: really like the most dismal offices you could ever be in a serviced office which traded at 140th evaluation of uh possibly we work yes are you still using your personal phone number for your startup

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SPEAKER_07: you were responsible in some ways for maintaining google's very unique culture in europe or did they say

David Friedberg: create another culture because this idea of like hiring really smart people letting them loose

SPEAKER_07: and uh you know interviewing 10 20 people i mean it's a very unique culture especially coming out of corporate german culture that you were in so maybe you can contrast the cultures yeah well i don't know

SPEAKER_19: if i fully got into the corporate german culture at all but i'd say it was definitely unique even for

SPEAKER_01: a western culture for somebody to interview you know tens of people for one job and then eventually you had to wait with bated breath for about a week because all these packages went to larry and larry

SPEAKER_00: would read through them i think i'm pretty sure i moved to the us in 2009 i know in 2008 i'm in a petition larry and said larry i've been keeping track of all the recommendations we send you to people to hire and the ones you reject and i have a 90 plus correlation with the ones you're going to reject so

SPEAKER_01: can i please have authority to move and hire them and he let me have the authority to hire people oh wow the caveat that he would have the right to reject an employee even if i had hired him or her if he chose to do so he never exercised that but there was always that hanging

SPEAKER_112: over one's head saying larry could decide he doesn't like this person and that didn't follow

SPEAKER_114: his hiring process but no i think that was i honestly think that that was one of the amazing things

SPEAKER_00: that one of the many amazing things that google did is to maintain this constant debate and dialogue

David Friedberg: and making sure we're hiring good people what you deconstructed his algorithm and you got to 90 correlation what was his algorithm what did you figure out he was doing was it as simple as just

SPEAKER_117: hiring very smart people was he hiring people with chips on their shoulder was hiring smart people with

SPEAKER_119: chips on their shoulder what was he looking for i think he had the point of view that listen there's

SPEAKER_02: lots of people we could be hiring a lot of people want to work at google he just has to make sure that we don't end up hiring small false positives uh what does it mean yeah well you know you want to

SPEAKER_19: make sure that there's a set of people who have looked at it from every angle like you know there

SPEAKER_72: were simple things like you just can't say this person is smart he'd say okay tell me the three questions you asked them and what is their answers to to tell me that they were smart so it took away all the biases it took away friends you know people you know from a different job area perception they're

SPEAKER_55: really good there we can hire them here he'd say write down everything you talk to them about within reason and that way i can make my own judgment whether this person qualifies or doesn't qualify so

SPEAKER_01: it just took take out a lot of the reasons why people sometimes make mistakes in hiring and you know larry always held the view which i like fully subscribe to that if you get a bad leader it

SPEAKER_00: can have a multiplicative negative impact to the rest of your organization so you got to be very careful when you hire senior people because they can they can drag the whole team down yes they they're

David Friedberg: because of their position in the company they're going to have an outsized impact and so false positive being hey this person interviewed really well they seem smart they seem like a leader they seem like somebody who could um you know motivate people to do great work here or or draw in other talent but it was a show it was performative maybe or somebody maybe hired them because they're friends and they went to

SPEAKER_55: college together but also he also was wanting to make sure that we didn't get under the pressure

SPEAKER_01: that we have to fill this job quickly and you end up hiring somebody who's 70 of the what you need but now you're there they're there for the next two three years and now they're performing at 70 and now you've basically taken the what could have been a great performance of part of the organization

SPEAKER_131: and impacted it because you were rushing to solve a short-term problem

SPEAKER_132: so how many people work at palo alto networks about 14 000. okay so now you have 14 000 people

David Friedberg: at palo alto networks yes and what what did you take from larry's algorithm and then what's in

SPEAKER_13: your algorithm nikash because you must have evolved it and you must have a way of which you like to run a company and also we'll get into like inheriting people because that's also challenging i assume yeah so

SPEAKER_119: look we did inherit i did inherit 5 000 people and um some of them have moved on because we've been

SPEAKER_01: transforming the company we have hired possibly north of 14 000 people in the last five years

SPEAKER_00: i think the part which you take from larry is yes you know you have to have a series of filters about good people you have to make sure there's an organizational conversation about hiring those people it just can't be four people interviewed them for half an hour each now with two hours of

SPEAKER_01: data of which probably half of that was spent in pleasantries you've decided that you're going to spend hundreds of thousand dollars if not more to have somebody do that role so i think there needs to be that process that conversation and a series of checks i think the only adaptation one has had

SPEAKER_114: to do is that it's both the iq and the eq okay and i'd say the google google bias might have been more an

SPEAKER_01: iq bias um because of the strong engineering culture and the fact you have to create great products and uh you know in a way ad sales was more evangelical like you were going to tell people

SPEAKER_147: because it's quite funny you should joke about this like when you go to sell ads at google well actually what do you sell say well how many can i buy well i don't know it depends on how many people

SPEAKER_71: are going to search today what's the price well i don't know depends on what other people are bidding for it other than that yes i'd like you to buy some ads it's great so do i can't just give you a

SPEAKER_148: blank piece of paper saying buy me you can set a price you can say i won't pay more than a dollar a

SPEAKER_151: click yeah so it was kind of here you know here we we sell to our customers we have to make sure the

SPEAKER_00: people we hire have relationships have the ability to sell have competence and domain knowledge specific sort of con sort of knowledge so we adapted the google algorithm but i'd still say you know that the guts are still significantly influenced by how we did things at google and if you're selling something

David Friedberg: brand new uh and all sales has a trust component to it unless you're selling widgets even then you know people have to get their widgets on time and they have to be a certain quality so there's such a

SPEAKER_155: big trust trust in widget you need the most trust in widget because there's no differentiation

SPEAKER_07: yeah they're going to come on time and they're not going to be fugazi yes but when you're selling

SPEAKER_06: something that nobody even understands like yes yeah it's like really take a leap of faith so talk a

David Friedberg: little bit more about the eq piece and why that's important to you and your algorithm and and how do

SPEAKER_07: you quantify eq uh and and what what are the sub categories of eq that you see manifested in an actual

SPEAKER_119: work environment well like out of the 14 000 people we have here i think five to six thousand

SPEAKER_19: them are in direct customer facing roles right you're constantly dealing with customers who are who are sort of cios cecil chief security officers or who are working in the technical parts of our customer organization so you know somebody has to come across as empathetic as somebody who understands the problem they have to be trustworthy like you said you know there's a significant competitive trust we're in the security business customers have to believe we solved their problem they have to

SPEAKER_00: believe our products are going to work and most importantly the customer has to believe when the hits the fan will be there for them because for the most part you know our products are working you're

SPEAKER_72: fine you're not in a breach you're not in a situation where you've been attacked the moment something bad happens they want you there yesterday they want you there to help solve the problem standing next to them they want to make sure it wasn't your product that caused the problem so from from that perspective all those things are as important as the technical competence when you're selling the product to them right you have to be there for them trustworthy consistent available all these things become important and part of that shows in your personality part of that shows in your track record have you

SPEAKER_165: been doing this for a while you've been doing it for the right companies and it's pretty easy to unearth with a few reference phone calls ah yeah if you're talking to the people who've worked with

SPEAKER_35: them before and that especially i just i never thought about it in terms of security which is a business

SPEAKER_06: you're in what things are just steady state everything's working fine the attack happens it's very scary

David Friedberg: and now you find out about loyalty reliability you know it's just a stand-up person who's going to fight with you to solve this problem i think it doesn't stop at the person right it goes all the

SPEAKER_01: way to the organization so for example you know we have a very simple set of policies if you have a problem we will we will open up the floodgates we will turn on all your licenses we will turn on products that to protect you that you even possibly haven't paid for we will send people there without

SPEAKER_171: saying sign this order and we'll say just sign this nda so we don't we're not in contravention but we're not going to come and say you got to buy this for return on we will throw everything in the kitchen sink at trying to protect you at that point in time because we want to be there for the customer

SPEAKER_13: especially in their time of need oh so that speaks volumes you're not like the sharp elbowed hey you

SPEAKER_06: don't pay for that product we told you to buy it you didn't buy it now you're suffering now you need

David Friedberg: to turn it on we'll send you a purchase order it's hey we're going to just we're going to help you

SPEAKER_13: get out of the situation and then afterwards we can have a debrief and if any of these products help

SPEAKER_174: you should use them well jason we've got a customer we closed deals recently they had it running for

SPEAKER_72: four months we spent four months getting them back up back up and running until they were up and running and secure and half of them are not even our products right we're supporting them with other people's products it took us three to four months to get them up and running then we had a conversation about you know do you want us to leave this stuff there and have it running and configured for you and of course they want you to but we don't show up the first day and saying sign this order

SPEAKER_176: or we don't say you know we want to we want to get them back and healthy first

SPEAKER_104: so in terms of responsibility which is a lot of what it is about i security is about who's

SPEAKER_52: responsible who do you get to blame you just take the approach it's you know we're responsible no matter what the situation is we're just going to come in there and do as much good as possible

SPEAKER_179: uh yeah i don't even know that reputation i don't even know if it boils down to responsibility right as you can imagine if you're thinking about a you know as uncool it may sound or cool may sound

SPEAKER_00: in a cyber security situation this thing can happen for a variety of reasons it could be somebody's credential was stolen somebody logged in as you somebody hacked your password so it could be a simple thing social engineering that causes your credentials to be lost but then once you're inside

SPEAKER_171: you start moving around and collecting data extracting data you know locking down desktops and creating ransomware events so it's that's not the time to figure out who to blame that's the time to

SPEAKER_172: figure out how you go sort of throw a blanket over and protect the customer and we'll figure out and

SPEAKER_182: analyze it afterward all right listen we work with super early stage companies at my investment firm

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SPEAKER_186: it's easy peasy lemon squeezy and if you're not getting insurance you know at some point you're gonna have to get it so let's make that point today right now this weekend tonight just go to in broker today with the code twist and you'll get 10 off their startup package how do you get the startup package in broker.com twist that's e-m-b-r-o-k-e-r.com twist make sure you use that code twist for 10 off that also more importantly than getting the 10 off that shows them that you're

SPEAKER_187: listening to this week in startups so we love and broker uh they've been amazing in terms of supporting our founders for years and of course this very podcast great job and broker how does one uh not

SPEAKER_52: coming from a security background wind up being the ceo of one of the most if not the most important

SPEAKER_190: cyber security company in the world how i mean i'm sure you must have gotten this like hey what are

SPEAKER_06: your bona fides here like have you worked in security for us no i i did adsense and i worked on google and then i worked with masyoshi on investing and now we want you to run a security thing how did they recruit you for a job that you didn't come from a security background and versus also having somebody come up the ranks there and hiring like google did you know everybody who was in

SPEAKER_196: senior positions like i said they typically moved up the ranks how'd you get the gig you know it's funny i got

SPEAKER_199: a phone call from a headhunter saying listen uh palato networks would love to talk to you about

SPEAKER_65: possibly joining the board or maybe something else so i show up and i talk to the board and we spend some time again one more time i'm not working i'm hanging out at home um this is both soft bank and you know we end up having the conversation and they end up saying we'd like you to consider becoming the ceo i'm like listen i don't understand cyber security uh my only perception of security is like you know consumer security like anti-virus my laptop i don't understand how cyber security works

SPEAKER_19: i said don't worry you've got 5 000 people who understand cyber security we need somebody to come help put this together and run the company now now that could have been interesting and i think this is a good conversation for the board because they went ahead and did this i can only imagine in hindsight

SPEAKER_01: you know if it had all gone wrong the board should have been in so much trouble saying wait you hired a guy who didn't understand cyber security he'd never been a public company ceo he'd never done

SPEAKER_71: enterprise sales before i mean honestly how desperate were you for options that uh you had to go find

SPEAKER_147: this guy and i you know i was joking when i took the job i still remember my first meeting with uh a ceo of a very large tech company now he used to be a ceo of a different company not hard to figure out

SPEAKER_19: i'm going to see him like two weeks in a job like he sits me down and says okay so tell me why do they hire you to be the ceo of politics networks like i had an interview after getting my job with a customer

SPEAKER_204: he's like i don't get it yeah why do you have this job to me why yes and what was your answer what what

SPEAKER_13: is it that you know and that that board knew that would lead to you having you know an amazing run so far at this company without having the cyber security background i guess uh the conversation which i had

SPEAKER_19: with the board jason at that point in time was i said listen i'm not a cyber security guy but i

SPEAKER_00: am a technology guy i do have an electrical engineering degree i can understand how this stuff works but more interestingly i am a business person i said if you think about the technology industry which is about a three trillion dollar industry a year give or take and cyber security is

SPEAKER_171: about 200 billion of that give or take it is the most fragmented industry in technology the largest player had one and a half percent market share at that point in time so the opposite of

David Friedberg: smartphones the opposite of google ads versus facebook ads like the opposite of crm the opposite of hr

SPEAKER_209: systems the opposite of you pick your enterprise platform no duopoly nothing nothing yeah no it's 100

SPEAKER_19: openly right so crazy if that is a word and uh i sat there and said this is this has to be something structural in it what is the structural problems the industry has where every company taps out at one one and a half percent market share and i said the problem is that every tech every cyber security

SPEAKER_171: companies that comes out they get there because they innovated versus legacy and they stay there because they stop innovating they find some really cool trick they go figure out how to go sell it to

SPEAKER_71: tens of thousands of customers and here's the funny part the moment you solve the last problem the bad guys have moved on to the next one so it's the most innovative adversary in the world they're always

SPEAKER_209: looking for the way to come after you while you've stopped innovating because you're busy selling what

SPEAKER_07: you had so in a way it's all tactics right like it's it's a tactical war it's tactical warfare counter

David Friedberg: measures new ideas and it never ends so then what was your approach to get out of tactics and build a platform to have a strategy here that was more long-term relationship building how did you conceive of

SPEAKER_72: changing the business so what what i sat down and said listen i cannot undo so you know i went and talked to 75 different cios and discovered that everybody had 30 or 40 cyber security vendors in their infrastructure and they didn't feel any much more secure and the bad guys are getting to it

SPEAKER_00: faster and faster so i sat down with the founder and and the chief product officer and i would spend about two hours a day with them for the first year one hour in the morning hour in the evening and i just bang at them with all these things i'd learned and ideas and i'd say listen you know if you think about this we can't undo what's broken what are the big technology trends of the future how are they going to impact security so our plastics moment was cloud

SPEAKER_218: i said i spent 10 years at google i used to sell google cloud this cloud thing is going to change

SPEAKER_72: everything sure yeah and so we sat down and mapped out what does it do well it fundamentally changes networks because people have to be able to access it from wherever they are and then the pandemic

SPEAKER_00: helped that it fundamentally changes how you do application development because now you're writing code using open source widgets and putting it on you know gcp or aws or azure out there or or whatever else you want to choose from and the second moment was because again because having spent the time at google and google was chomping at the bit about ai even then in in 2014 when i left so i said cloud

SPEAKER_224: they are the two biggest trends how does it change security so yeah in five years uh we said first

SPEAKER_00: you said second we used to spend 12 of our revenue on r d i said that's not enough that's that's the amount of money that you spend if you're sort of you're milking your last cash cow so we bought 17 companies all

SPEAKER_171: in cloud security and ai in the last five years we uh focused our business on the future we now play in three out of the four biggest swim lanes in cyber security we have 19 products that are in an

SPEAKER_01: enterprise there's a single magic quadrant where you have to be you know people buy you if you're good we're the third company in tech ever after ibm or microsoft we have been in so many magic coordinates ever so we turned the whole company around into what i call a cyber security innovation engine

SPEAKER_19: and we've convinced our customers are going to be evergreen and then we did that by effectively building three cyber security platforms which are sort of in the early stages and i think our best one is still ahead of us because we were able to build an ai based cyber security platform which suddenly with all the conversation around generative ai and ai has suddenly become center stage for us so you know we had this clip notes version of this no no i mean i i totally get it and there's so many

SPEAKER_52: jumping off points here i want to just i want to get to be a ips because that's fascinating to me we had a really interesting conversation a year ago when you know chat gpt came out and on all and we were

David Friedberg: just kind of talking about what are the possibilities here and i was like you know this is completely dangerous because you could fire up fishing attacks and just have an agent running incessantly trying different things and iterating on them and it can go at a speed that you know right now is throttled by human ingenuity uh and is that actually happening in the field right now you're 100 right you're 100

SPEAKER_199: right now the the the slight saving grace right now jason and it's i don't think it's for long

SPEAKER_71: is you can have an agent running incessantly and the only the difference is right now the agent doesn't know why it got blocked right now what we have done in our quote-unquote lab we actually tell the

SPEAKER_171: agent this is how we blocked you so we've found we've got agents that have broken through our defenses after 30 plus tries because we keep telling it how it got blocked so it's kind of reinforcement learning

David Friedberg: oh wow you're doing that in the lab right now so you're training it how to be the greatest black hat so that you can now be two steps ahead of the hackers using this so we are giving them the

SPEAKER_242: countermeasures in addition to the measures they're trying yes so what we do is we do this we're

SPEAKER_243: training it now and we've you know we've been able to jailbreak most of these public models out

SPEAKER_01: there it's not hard to get around their guardrails in terms of getting them to generate malware um so i think fascinating well think about it there's thousand plus models out there in open source right so you can get google to be responsible you can get open air to be responsible lama to be responsible how are you going to take care of the long tail now are you sure that any of that long tail model that's going to sit in an nvidia box somewhere running in some you know basement is going to have the guardrails you want it to so we've been able to jailbreak them we've been able to have we've been able to get them to write malware or write attack vectors which then we run against our own products in the lab and we keep telling them how we block it so we see how many tries it takes to eventually get through then we build antidotes to those and then we put them in our products

SPEAKER_52: fantastic so it's no longer reacting to what happens that customer a to protect the next 999 customers this is you know you've got patient zero here yes and you are doing experiments on them and you're

David Friedberg: seeing if they're inoculated or not and it's all happening in a virtual environment so there's you know no harm is coming to it um yeah but i think i think we're going to have to work hard

SPEAKER_253: with the industry to do that on a grand scale because you know we're not i've not held the

SPEAKER_171: belief that palo auto is going to solve every problem the good news is we are aligned with everybody in the cyber security space we're all trying to make sure the bad actors don't get ahead of us so yeah i have no problem taking these malware antidotes and sharing them in the industry

SPEAKER_251: i want to make sure that every product inoculates against them not just mine

SPEAKER_07: right uh why why would you take that approach i mean why not give it only to your customers

SPEAKER_129: um and let them benefit from it so you get the revenues and then you can reinvest it and secure your customers why are you giving them out to other competitors well look i have other ways of

SPEAKER_55: creating a moat and other ways of creating economic advantage what i don't want to do is to hold the cure as ransom no pun intended yeah for the rest of the world have to buy my products

SPEAKER_59: right you could take another approach like licensing it or you know yeah but i also

SPEAKER_55: want the benefit of their their research and intelligence right i don't believe this is a

SPEAKER_01: singular problem i want to make sure that microsoft does it or crowdstrike does it or google does it that we all share in this common goal is it collaborative did you find it was collaborative

SPEAKER_52: when people find exploits solve for them do they quickly tell their cohorts and their compatriots hey you know we figured this out there's an attack vector or do people kind of hold it slow you know

SPEAKER_06: when people go for the check and they slowly go get their wallet do they kind of slow roll it before they tell you so they can get the max advantage of having it well there's two different scenarios right

SPEAKER_19: jason one scenario is that uh i've discovered a vulnerability in somebody's product right or my own

SPEAKER_01: product now of course there we there is a bilateral conversation we tell them listen we found this you want to fix it before we tell the world right because you really don't want to you know yeah

SPEAKER_171: you have to right and that's what happens in in most even public private partnerships there are certain nation states which won't tell you because that for them that is a future exploitable opportunity but in most cases let's just say 99% of the case that communication happens with some nuance uh but people tell each other i think the the other scenario is that there is a certain attack factor somebody's figured out how to break into something i think it's fair to say there's probably

SPEAKER_01: 10 or 15 high quality research labs around the world which are both private and public and there there's a very strong collaboration something called cyber threat alliance where people go and provide their solutions to everyone as quickly as they can because there's an attack vector out there you

SPEAKER_263: want to make sure that everybody's products are inoculated against that vector how much of an impact

SPEAKER_264: yeah well no i get the alignment piece yeah and so how much impact has the secs you know now kind of

SPEAKER_52: coming down on um companies and saying listen they're kind of intervening right they they gave

SPEAKER_06: a mandate you have to like report this stuff and there's consequences i know from some of the companies i've invested in there were people who didn't report there were c cso's who didn't report things maybe they had egg on their face they slow rolled it they tried to solve it before it came out and now it seems like we got a lot of three-letter agencies who are now monitoring this so what is the

SPEAKER_52: state of our government intervening whether it's the sec or others and saying when you have an exploit

SPEAKER_271: we need to know about it because it's the incentive as a cso or the person on the security team who

SPEAKER_196: messed up if they in fact screwed up if they are um if they have to go report it self-report it they lose their job or they could get fired i mean it's just yeah so like like i think uh let me let me give

SPEAKER_273: you a little bit background this i think i still believe security is still broken right so what

SPEAKER_72: happens is in 30 or 40 of the case i know of no i know a bad thing i stop it if i'm in your enterprise infrastructure i see a bad url you're clicking on i see malware i know it's bad i stop it when i don't know it's bad i just find it suspicious what i do is i give you the tools

SPEAKER_171: to save and if it's suspicious i'll send you an alert now the problem is i got organizations with 70 80 000 alerts a week they don't know what to do with them that's the current state of affairs is

SPEAKER_72: most organizations get between 30 to 80 000 even 100 000 alerts from security events across their

SPEAKER_271: infrastructure which is like just noise and that's a function of the fact that the attackers can do very large scale attacks from computers or there's just a large number of them no it's just

SPEAKER_171: a function of let's just say that you know an organization in the palato says listen downloading one gigabyte of data is bad from our network but don't stop that download because it could be

SPEAKER_283: legit just send me an alert ah i got it so that's the rules so it's 80 000 alerts flipping around and i don't know if it's right or wrong somebody has to investigate it that was cool 20 years ago when

SPEAKER_171: you had 20 of these now you got 80 000 so and the reason i bring that up is what we've done is we

SPEAKER_72: basically said we're going to watch every bit floating through your infrastructure and we'll tell you if it's good or bad so we collect 75 terabytes of data a day at palo alto we analyze that we take those 80 000 alerts make them 200 events if they're real so we basically flipped it to an ai problem the reason i say that is i started giving a longer answer but in the industry there's something called mean time to remediate how long does it take you to fix a security event how long does that take on average yeah the average united states is four to six days four to six days yes the mean time to

SPEAKER_287: exfiltrate data today is 11 hours the hackers come in and say too late yeah that's like me

SPEAKER_290: reporting my house got robbed like a week later in san francisco they won't care but they won't

SPEAKER_293: care anyway don't bother you could you could wait you can hang on to that one it's funny because it's

SPEAKER_184: true yes it is that's why both jokes are funny life for your in-house legal team can be so hard chasing down signatures pouring over contracts toggling between all the different tools that back and forth with the sales team it's brutal and legal stuff oh my god bane of my existence right there's just so much it's a deluge but it doesn't need to be that way all you have to do is use link squares it's the first ai powered end-to-end contract management solution what does it do it gives your legal and your revenue teams the tools they need to help sales close deals faster while delivering a seamless experience for your customers so you can create review approve and execute your contracts easily in one place while prioritizing tasks and integrating with the tools your team already knows and loves link squares is where all your legal needs come full circle start streamlining your contract management process today and make life for those in-house legal people so much easier with just a few clicks learn more at links squares.com twist that's links squares.com twist to start streamlining your contract management process today and if you're not doing your contracts right it's going to cause all kinds of downstream problems do it right links squares.com twist back to the sec

SPEAKER_71: the sec has put out a mandate that they must report in four days which i think yeah so i think that puts

SPEAKER_151: the pressure on a lot of organizations because you really don't want to report you are breached if

David Friedberg: you haven't fixed it talk to me about this tension of the people who are responsible for reporting it are also responsible for um causing the problem in some case or not defending against it how does that tension get resolved in the industry are there groups of people who are responsible for reporting

SPEAKER_06: the groups of people responsible for defending and they don't talk to each other so if something blows

SPEAKER_78: up they're not covering stuff up what's the best practice there i'm curious look this rule is about

SPEAKER_171: two weeks old i think what the sec has done as he's put sec has put a gun to the head of boards and

SPEAKER_01: management it's like i don't care ceo cfo you are not following the rules if you haven't reported this

SPEAKER_171: so i think that that's like no longer a debate because i think it's pretty clear to most organizations when they've been breached they've been breached right this is not it doesn't happen on the slide you don't get an email saying we've locked down 10 000 your desktops you have ransomware attack going on you owe us millions of dollars you know when that happens and you know how to count for you know four days from there so i think the reporting is not the the issue i think the bigger challenge is most ceos most boards don't know how long does it take for them to reliably block stop clean out

SPEAKER_307: a cyber security event so i think there's going to be a lot more pressure and focus and trying to get that done sooner than later which is good for all of it it's such an after that security right

SPEAKER_13: people worry about it after they've been compromised not before and so they're reactive so generally the

SPEAKER_52: whole ecosystem has to get an education from boards on down and take it more seriously yeah yes and

SPEAKER_119: that's where i said you know four five years ago when i joined palo alto we were an 18 billion

SPEAKER_00: dollar company we were in one swim lane today like you said we're now a 70 plus billion dollar company we play in three swim lanes and our big underpinning and our big pivot five years ago was to focus on

SPEAKER_171: collecting good data across the enterprise and applying ai so we at any point in time we have about a thousand plus machine learning models that run underlying our ai product to solve the problem

SPEAKER_147: and i think you know we're going to talk about ai but we did a big analyst here on friday and we try to distinguish uh or try to figure out if this term will hold i call it precision ai versus generative

SPEAKER_19: ai so if you're if you're in your tesla you don't want it to hallucinate oh i thought that was a good

SPEAKER_320: turn that's kind of like life i'm on somebody's lawn i'm on zucks lawn that's right if you're lucky

SPEAKER_321: there's probably a room to slow down you're wrapped around wrapped around electricity pole is a bigger

SPEAKER_171: problem then zucks yeah but for sure that notwithstanding so even in security you need precision ai i need to be able to block an attack i need to know it's a real attack i can't start

SPEAKER_01: blocking legitimate things in enterprise while all hell will break loose so there's this notion of precision ai where you cannot afford to be wrong then there's so there's this notion of generative ai

SPEAKER_171: where there are many right answers or many possible answers i don't have right like you know show me a blue bird uh on a black background well there's probably 200 different variants from

SPEAKER_71: great to horrible which are all perfectly legitimate answers so yes our business is a

SPEAKER_323: business of precision ai it's not a business of generative yeah it's precision ai possible today is

David Friedberg: because generative ai like you're saying i ask it for five ideas for a blog post three of them are terrible two are pretty good then i asked it to take those two refine them give me some bullet points give me some sections and then i you know i polish the last 20 and oh wow i got a two great blog

SPEAKER_59: posts out for my corporate blog you know in 10 of the time okay great that was a fine process that's

SPEAKER_13: not the process of precision ai it's not the process of making a left turn or a right turn

SPEAKER_07: into an intersection nor is it blocking security so is precision ai here and i guess that's very verticalized right you have to narrow the scope in order to get to precision ai well yes i mean look

SPEAKER_71: at the end of the day the precision ai as you rightfully articulate like it boils down to first and

SPEAKER_171: foremost owning data collection and making sure first party data belongs to you right you know

SPEAKER_114: elon is not going to rely on you and me sending my data if you're saying here's the data feed for all the traffic information that you can have like no i'm going to have every car that's out there assess

SPEAKER_171: it every second and go to feed feed into large ai system process it locally and give it the response time that it needs so that you can sit there and feel safe so yeah it'll be there but it'll be very domain specific you'll have to own the first party data you'll have to have full control you have to

SPEAKER_01: make sure the models run the way you want them to run and you're going to determine it every second and you probably have guard rails and safeguards against you know actions that are taken post

SPEAKER_171: precision yeah and they'll get very very very specific like i'm pretty sure everything that

SPEAKER_114: tesla does all the gig petabytes of data they're collecting is focused on one thing is one thing called driving experience similarly for us we're collecting 75 terabytes in our own in our own instance at pala alto we connect four petabytes a day across our customers and we only

David Friedberg: focus on finding the anomalies to stop bad things from happening so precision yeah it's exactly how tesla does it like now if your tesla disengages it asks you describe what just happened so as a tester in fsd full stop driving you hold the button and you say oh you know a bicycle went across the middle of the

SPEAKER_06: road and then that is what you know they don't need people on the 280 driving perfectly they need

SPEAKER_336: the instances where something weird happened and that's by definition what you're doing right and

SPEAKER_160: yeah so but i think on the flip side i think you know what we've been seeing in the last one

SPEAKER_01: year with the open ai and this whole journey i think i think this is going to be huge this is going to be so big it's going to transform how we how we do technology okay so this is going to open

SPEAKER_52: up a big can of worms here and i think it's the next jump off point i wanted to get into the 17 acquisitions and ask you how you did those but we'll put that on the side for now because this is i think a more important discussion which is this technology has captured people's consciousness for about a year you've been at it for years you alone's been at it for years you know google's been at it for decades it's uh it's obviously ready for prime time and so when you look at running your

SPEAKER_06: organization what are the gains like inside of the organization right now and then what does this do for the core business that you have because when you were talking about data this to me seems

SPEAKER_59: like the greatest moat ever yes tesla has two million cars i think on the road that have the cameras in it you can't buy a car without full self-driving on it you can turn it on or off if you

SPEAKER_06: want to pay the 12 grand or whatever it is but they have that data i think even in the cars that are not on and they have the right to pull that data you have all this data from all your customers

SPEAKER_50: collected this becomes a compounding moat and advantage over time does it not so let's separate

SPEAKER_171: i think the data just the case of tesla and for us is a precision ai opportunity i think you cannot do precision ai if you don't control first party data access right so the fact that he's got millions of cars which are collecting the data the right way is not something you replicate just because if you're gm or ford you don't have the data collection going on you have the cars in the road similarly we have 62 000 customers with firewalls who have been analyzing their data for the last 17 years and obviously in the last four years more so we have customers with you know 14 million plus endpoints on various different technologies so we're collecting first party data and delivering ai outcomes that's on the precision ai side and i think that's hard to beat if you haven't been doing it if you haven't been collecting data many of our competitors haven't i'd say there's probably four out of 3 000 who have tons of data and cyber security and that's going to be a sort of a race between those four and we think we're still the largest and the most comprehensive route with them so that's one of one side i think the generative ai side is a whole different followbacks i think

SPEAKER_01: there's two if you if you abstract it the two best things that generative ai does for you is one it is phenomenal summarization so take my last employer right if you did a search for what's the best restaurant in san francisco it'll give you 10 links if you're lucky maybe 20. yeah now it's your job to read through those 20 links to find out and parsen said how many times did i see a reference to a b or c and you're kind of mentally say okay it looks like a is the right answer now what open

SPEAKER_171: the eyes doing is reading all those 20 and saying based on everything i read statistically i think this one is the most mentioned hence it must be number one because it's associated with the word number one everywhere so it's providing you phenomenal summarization capabilities and two

SPEAKER_01: it is based on all this training able to talk to you in natural language i think those are the two most interesting things that it does if you abstract it if you take that and say what does that mean for

SPEAKER_171: me in my organization one in there are many use cases in my organization where there are lots of documents where we don't have good summarization and good sort of answer extraction out of it right

SPEAKER_19: i could save hundreds of millions of dollars in every enterprise if i figured that out

SPEAKER_171: every yeah for sure it requires like you said requires us to do that tesla thing fsd thing you know click here and tell me what happened i get 300 000 plus customer issues a year if every issue was recorded i'd figure out how the solution was created for that customer anytime those things reappear i can fix that using some sort of generated by llm under underpinning that stuff so

SPEAKER_283: that's kind of like logical we'll see that explode across every enterprise in the world

SPEAKER_52: world customer support it's already happening like you you that's the perfect data set customer

SPEAKER_254: support reps and yeah so there's tons of enterprise use cases where this is going to be sort of brute force perspiration work or data cleaning will be required you'll get efficiencies i think the more

SPEAKER_362: profound impact is going to happen on product development now if you think about development

SPEAKER_171: yes if you think about product development we have spent our life and technology doing phenomenally good engineering work then we have these guys called product managers what do they do they take all that wonderful engineering work and say how do i make it easier for the customer to consume let's design a ui for this yeah right take the take the travel booking example i'm pretty sure all of us are trained we can go to any travel site we know we want one way round trip you want to have multi-stop single stop you want economy first business and you have to fill about 10 10 boxes and out pops a set of options for you to buy a ticket we're all trained now we have been trained in doing those

SPEAKER_369: searches yeah yes but some product manager actually designed that ui that was their job they took

SPEAKER_171: engineering back end built ui on front and allowed us to interface like learning a new language yeah all of us can all of us can can envisage a scenario which says find me a quick inexpensive ticket from here to new york i want to go this evening are we back you know day after and make sure it doesn't cost me more than a thousand dollars i can say that phrase you can all imagine a generative ai

SPEAKER_369: llm looking at through the all the options and popping it out and saying here's the options

SPEAKER_374: right and you say two yes and it books it so the ux is gone what i do i just eliminated ui

SPEAKER_171: yeah the ui chat window yeah but if you if you take that and abstract that to the hundreds of thousands of companies and apps that are out there i think 50 ui vanishes in the next five to ten years

SPEAKER_06: incredible yeah you know talking to a computer was like gonna happen star trek etc we were just going to

SPEAKER_59: talk to a computer and then the task would be accomplished and then actually we're just on

SPEAKER_00: the cusp of that happening but if you think about it you know people say well i don't know so i say well when i worked at google there was this thing called a web page we used to all interact with the

SPEAKER_171: web page this thing showed up called mobile right people say oh yeah guess what we're going to have to have a mobile presence and i'll tell you the first wave of mobile presence for most web oriented companies was a diminished user experience you couldn't do everything on the mobile phone that you could do on the web page that was your primary interface and then you saw this wave of companies called whatsapp uber doordash all these things are mobile only there is no web interface nope so i'm telling you in five to ten years we're going to have ai generative ai only ui with no mobile or web interface if that could happen in mobile it's going to happen with this and that's going to be very interesting to watch how many companies get sort of obliterated or possibly refactored because either all or

SPEAKER_52: your ui has vanished yeah i mean i am an expert at finding restaurants and i use eater and i use yelp and google local and i was explaining to somebody how i find incredible places in tokyo and that i put them all on google map and then i share them with friends and they lose their minds because i've done all the work and ai is clearly going to take that little proud process that i have of finding whatever the hip new places in tokyo are from a bunch of bookmarks and it's just going to be totally abstracted um now if you have a good data set like you're saying then you could still be the winner or if you have the network so it'd be quite nice to just take your watch out and ask for an uber and it just knows where you are it knows what your preconditions are and it just works um but right now i wouldn't trust i

SPEAKER_390: think over you can do that i think you can order an uber through siri i just don't trust it to do that

SPEAKER_147: um yeah well that's that's going to be a whole different debate is you said something very interesting what you said right you know uh maybe i'll do the jumping off point you can tell me the

SPEAKER_387: answer yeah uh you said you can tell cd or an uber so is it going to be the siri chatbot or the uber

SPEAKER_68: chatbot you're going to talk to hmm i have a feeling it's going to be the uber one i think it's going to

SPEAKER_253: have more knowledge siri sucks i mean the question then becomes are you going to talk to 40 chatbots

SPEAKER_336: wow i mean i think you're going to have to have a comp maybe maybe they're yeah that's a really

SPEAKER_339: great question you could have a doordash chatbot an instacart chatbot an uber chatbot a booking

SPEAKER_254: chatbot a kayak chatbot or pick your favorite i think they're all being built right now and the four seasons chatbot because they all want their own so now you're basically taking the apps and exploded that into a series of chatbots yes right well there could be a meta one so i think claude

SPEAKER_59: and some of these ones that people are building are supposed to be your chatbot that'll interface

SPEAKER_13: with the apps and the and the atis that are out there so we're going to have to have we're going

SPEAKER_01: to have to have a whole new episode on that jason because i don't know i think this is a battle it's going to be a battle of apis because it hasn't been done before today most of your spotify's instacart doordash are not opening apis for action because they know if they open an api for

David Friedberg: action they've lost the customer interface yeah i mean that is the scariest thing to ever happen i was talking to with somebody who owns hotels and he was just talking about their relationship with

SPEAKER_06: like the expedias of the world yeah or rupert murdoch had this relationship with steve jobs where he's like you can't subscribe to wall street journal i need the person's contact info and steve jobs told

David Friedberg: murdoch you don't need the contact info and murdoch like looked at him was like yeah i'm not putting my stuff on your ipad silliness if i don't get the person's name and and and it's steve's credit he

SPEAKER_07: he caved with um he caved with rupert murdoch and let him get the contact info we'll be back there

David Friedberg: with the battle of the chatbots yeah and this is i think yeah with the interfaces i see some people

SPEAKER_325: like i think kayak and zillow made like little plugins already but yeah i i don't think you want to

SPEAKER_421: track that similar let's take your tokyo analogy right if if you went to your favorite chatbot and booked a ticket for next week to tokyo somebody knows you're going to tokyo next week if your restaurant app knew that you're going to talk to you next week it could recommend recommend recommend restaurants for you next week but the question is who's going to have what data

SPEAKER_52: yeah see that's why i think the data provider wins when you were saying it and you were like everybody has a chatbot i'm like there's a group of 30 people at yelp right now building that chatbot there are a group of 100 people at amazon building that yeah yeah and my view is that that's

SPEAKER_72: why again distribution becomes very interesting if i am if i'm a phone if i'm apple i still have some

SPEAKER_01: degree of influence on how all these things get shared with the customer right because there's got to be some semblance of control there's got to be some control of data in here because suddenly now you've got the same problem you had with you know five million apps taking your data and running away

SPEAKER_171: from a data privacy perspective you're five million chatbots they're gonna do worse things to it than

David Friedberg: the last set of app guys were doing interesting if you think about it right now you can when you talk to siri they built a plug into spotify so you don't go to apple music if you said you wanted to play a song it would automatically go to apple music now you can tell it hey go to spotify and so i think that's

SPEAKER_272: going to be it's very interesting that apple could intercept all that information they're going to

SPEAKER_19: just i don't know in a very microscopic way i think you know i have a summer home automation app

SPEAKER_114: and until about a few months ago it had i could play songs from spotify and sonos now it makes me go to

SPEAKER_437: those apps it doesn't they've sort of restricted their api to take control back it's going to be

SPEAKER_07: interesting to see what happens it is if you try to use questron or savant yes like uh many of us who

David Friedberg: have nice homes they they come with this it's the worst interface on the planet yes and then you

SPEAKER_06: just rip it all out and you put in sonos and apple tv so you actually can use your products yes it is where everybody eventually winds up everybody i talk to is like the savant remote control sits there the 800 savant remote control sits there and then everybody immediately just

SPEAKER_444: picks up the apple control and and they're done if it's only 800 you got a great deal exactly no and

SPEAKER_13: having somebody come program how to make your netflix work is a really great experience at 400

SPEAKER_07: an hour let me ask you a question about your time at softbank uh you got recruited to be i guess as i understood it you tell me if i'm right or wrong to be moss's right hand man and then eventually maybe

David Friedberg: the the heir apparent is number one was that reporting correct and then number two what was it like working with moss i know moss i've had a couple meetings with him he's kind of like a mad genius swing for the fences hail mary throwing visionary what was it like coming in there and and experiencing masa at peak gamble like masa making peak bets i mean that must have been extraordinary yeah so like

SPEAKER_19: masa and i uh our sort of association began when he came to google one day with a crazy idea saying

SPEAKER_00: listen uh i've been left at the altar uh yahoo and and microsoft have a deal to do this thing with bing and yahoo's getting out of the search business but they kind of

SPEAKER_114: didn't focus in japan and i also have you know japan which uses it algorithm from yahoo us so would google be willing to work with me on the algorithm in japan like wait a minute how can that be possible and we're competing with each other in japan how do we do this and to masa's credit and his crazy idea he and i sat down and crafted a way for us to have both be powered by google search but to have separate ad ad auctions so it would be not anti-competitive so they had their own ad sales team

SPEAKER_171: there and their own ad sales pricing we had our own pricing so the customer got good pricing and they got the best technology and it was kind of unique because it was hard to construct but we became friends

SPEAKER_00: then and one thing led to another and he did come to the conclusion that he wanted me to become his his heir apparent uh when he turned 60. so he hired me when he was 58 and then you know uh he changed his mind to 60 and that's kind of that's the story that's true but in the meantime those two years i'll tell you um when i turned 40 i decided that from now on i'd look at people and see what do they do something that i can't do how can they do this in a way that i cannot do it and masa has this amazing quality where he's untainted what does it mean and well you know

SPEAKER_01: we all get look it's from the time we're born uh we're constantly risk minimizing ourselves when our kids walk across the street we're like be careful look left look right we're like you know

SPEAKER_171: don't do this and don't do that what is happening that's there's a constant process of risk minimization that happens you buy a house you get married your risk profile risk appetite

SPEAKER_165: continues to go down masa is a guy i think unique in his ability to have infinite capacity for risk

SPEAKER_456: every morning capacity for risk wow do i need to explain that from an investor perspective watch him

SPEAKER_460: you watch what he does and that explains it perfectly yeah and so and the beautiful part is

SPEAKER_464: there is no reinforcement learning there no no he's just going to keep doing it he'll end up on zuxlan every time it doesn't yes yeah he's going to swing for the fences and you know what it it

SPEAKER_318: seems like it works out uh it works out and i think the only thing you know when you do that that's

SPEAKER_71: fine but it kind of goes back to how you guys do investing like you got to play in your weight class right if you're constantly investing a million dollars in a million companies your math will work out but you start doing one big one here and small ones here that one big one can

SPEAKER_390: wipe you out and everywhere else right yes risk of ruin is what we call that in gambling that's right

SPEAKER_71: there we go so i think that's where it it may get it may have gotten a little more complicated

SPEAKER_171: for him but other than that i think he's he's got immense intellectual curiosity he's got at some level he has immense humility at some level he has immense confidence and he has as i said an

SPEAKER_19: insatiable appetite for risk and he works hard so all those all those make for great ingredients and sometimes you know i was you know the ying to his yang um right you could create some structure

SPEAKER_52: there maybe some downside protection thoughtfulness around how big is this bet does this bet need to

SPEAKER_19: be this big you know like you know i you know i we there's an example out there on we work which is

SPEAKER_65: written in the book where i was not for investing in we work multiple times but then i left and you know he became a large investor and we work and what do you think his blind spot was there i mean all of

SPEAKER_06: us looked at it including the early investors like benchmark probably made more money than anybody off of we work as they sold their position maybe second only to adam himself went with his buyouts well we

SPEAKER_190: all saw it and said that is not a technology business it's a real estate business and what what didn't

SPEAKER_137: he see what what was his blind spot there you think look masa masa is uh as i said he's all those things

SPEAKER_119: is also uh and he falls in love with certain ideas and certain concepts and uh you know he

SPEAKER_19: and that also that is where he gets his passion from right he's an extremely passionate guy he gets

SPEAKER_00: very excited about certain things and you know he recently does a reasonably good analysis most often than not um and sometimes they work against him so he can't pick anybody's one bad investment and go back and question him at the end of the day he still made billions of dollars for himself and many other people out there so you know i think the the good swings come with the bad swings yeah i mean uh there's

SPEAKER_52: a very famous phrase in china no gamble no future and i think he's like i think it's probably his

SPEAKER_71: operating system i'm staying away from the word gamble because i know you guys like your poker but

SPEAKER_07: you know i think the masa is an investment placing bets i mean you are placing bets in venture is the is the nature of what what did you take away from that what did you take away from the time there like

SPEAKER_13: that added to your game and then what did you sort of file away as yeah this is something that i don't

SPEAKER_273: need to add to my game look i think the the the risk appetite the lack of the then don't become

SPEAKER_00: complacent you know constantly be looking at seeing what's around the next corner uh this desire to constantly learn all these things are things like you know saw masa do and they have helped me at power also like you know we are constantly paranoid we're constantly out there trying to figure out what's around the next corner we're constantly looking at what's the next technology event that's happening

SPEAKER_147: in the industry and we talked about generative i from the you know i was on a plane to india when open ai came out i i logged in i was supposed to make a graduation speech at my alma mater which i did i rewrote my entire speech on how this is going to be the next big thing that's going to happen

SPEAKER_00: and i literally called the iphone moment there i think jensen said the same thing possibly a few hours a few days later or the same time i don't know and i've embraced it we have hundreds of people

SPEAKER_01: about also working hard towards making that a reality now if i hadn't learned the lesson that you've

SPEAKER_72: got to embrace these technology trends as quickly as you can because these become inflection points those inflection points allow you to distance yourself from competition so you gotta grab them

SPEAKER_13: and run with them as hard as you can yeah and this is something microsoft didn't do when it came to mobile right they just totally missed it they whiffed and did it face facebook did a phenomenal

SPEAKER_55: i think facebook was one of the best pivots from the web world to the mobile world and most people even

SPEAKER_52: better than google i think and he was stumbled a couple of times right the app didn't come out

David Friedberg: perfect and they you know built it with react they had to take two or three swings at that bad and then he realized and he's like you know what i'm buying instagram and whatsapp because this is the future i mean yeah uh let's close on m a you bought 17 companies yeah what did you learn about m a proper

SPEAKER_06: way to do it and how to integrate those crazy pirates into a ship of 14 000 people and let's be honest people who are attracted to working at a big company are slightly different than the people who

David Friedberg: start companies so how do you bring in 17 founders probably more because there might be two at each one of them how do you bring in those founders and then you got this executive team here that's cranking on this you know aircraft carrier and now you got all these speed boats whipping around you know doing

SPEAKER_06: donuts and these are two different cultures yeah and how do you integrate it yeah so i think you know

SPEAKER_119: first and foremost i think most people get m a slightly wrong uh we have some principles one we always

SPEAKER_00: look for the best in the field i think number two and number three trade trade at a price for a reason so you always try and buy number one or two because the markets get really small

SPEAKER_01: after the first two or three players in enterprise it's like just there's a long tail you want to be one or two so you always buy and you pay for what you buy so one we did that two i always tell my team they kicked our ass by having less resources working 80 hours a week going to customers understanding the problem we were there the customer we didn't understand the problem we didn't solve it so they will run that space for us we want so our people end up working for the founders

SPEAKER_283: wow that's that's intense bro that's an intense approach give them our people and our resources

SPEAKER_270: and we double down so we didn't get it done they did we admit that they got it done and now they're

SPEAKER_484: in charge they're now yeah 70 70 of my product organization is run by acquired founders not by

SPEAKER_35: existing problems of people well that's a way to change the culture real quick yeah so that's that's

SPEAKER_71: the second rule the uh third rule is once we make a deal we spend the time between term sheet and due diligence and da on having a joint product and resource plan i don't do my lawyers do the diligence i do the product and diligence plan and i say before you sign the definite agreement this

SPEAKER_72: is what's going to happen this is my house i'm going to decide what color i paint it but you see right here it says yellow here blue here green here you sit in this box he sits in that box he sits in that box if you don't have agreement we don't have to deal right so there's no surprises after the deal is closed they know exactly what's going to get built how it's going to get built who's going to do what so we solve all of that beforehand we know exactly what we're going to inherit who's not going to work in the job we map every individual to what that's what's going to happen because with our first owner in a mistake we realized it takes three months once people have all the money

SPEAKER_283: doesn't people fight for position fight for role fight for strategy you solve all of that way ahead

SPEAKER_07: of that that is so brilliant you know when i got acquired by aol and john miller bought the company he said to me what's important to you whatever and jim bank off and i said well we have this earn out

SPEAKER_52: i got to keep my sales team because that's one of my things i'm good at i'm good at sales and i know what the customers want for blogs and for this kind of content and uh he's like well we've got a really big sales team and i was like yeah i want to keep the sales team for as long as i have an earn out and then if you take the sales team away from me the other big sales team could sell into it but my guys still get the commission for processing it so we pay double commission and to to jim bank goes credit they respected that um and i think that's what made the easy transaction

SPEAKER_507: for me as a founder because you have found a regret after you sell your company that found regrets real

SPEAKER_19: yeah jason i don't do earnouts i don't do misaligned uh incentives objectives i i align them they all get one auto stock and they have only one incentive double or triple the palo alto stock we'll all make

SPEAKER_509: money see that's much better yeah you don't and we pay up it's fine maybe company at the time so

SPEAKER_07: they had to you know come up with a different uh incentive structure but i like the approach because

SPEAKER_510: you do have this founder regret moment yeah yeah and that that could kill the company kill the deal

SPEAKER_13: all right listen this has been an amazing episode of this week in startups thanks so much for coming on

David Friedberg: the program nakesh it's amazing um i'm gonna will you come on again in a year and just catch us up on how this ai thing worked out can we book you for one year from now sounds like a plan i look forward

SPEAKER_515: to it all right uh we'll see you all next time on this week in service bye bye