3 Episode results for "Bain Consultants"
Product Market Fit, with Matt Lerner, Startup Core Strengths
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So you know went to reach out. And what to say sell more enclosed bigger deals by sending personalized messages directly to buy ready to talk tech g to smarter software. Decisions made together joined the community at. Www dot l. dot g to dot com slash sas stock. This podcast is sponsored by charge for charger. Flight provides specialized billing data monitoring tools to give be to be sas companies competitive edge over the past twelve years charger for has partnered with champions incest. Like spark male gun connect an earth close mile to streamline their processes build a nurture lossing relations with customers and strategically optimize their organizations for long term growth. Charge fis innovative software. Pows every beat to be sas company to step into the future of billing visit flight dot com forward slash sas. Talk to learn more if you go back and look at these graphs hub spa or airbnb or any of these slack. Daily active users or revenue. Whatever it's sort of like a hockey stick like nothing. Nothing nothing nothing bank. So this is a long journey of indeterminate length. I abandoned this idea. There's illusion of certainty. There isn't a plan. There isn't going to be time. There's going to be as a process of rapid experimentation everyone. Welcome back to the central pollution show brought to you by system. The conference held fast companies. Get traction growth and scale. I'm your host alex and all be looking at what it really takes to build and grow sas company today now founders and entrepreneurs healthy on the journey now on with the show welcomes this aspiration. Show matt lloyd as ceo of starts strength. Welcome that thanks. Alex pleasure to be here. Great to have you on the poco costs. We've had to speak at a couple of events being impulsive in virtual at you doing a workshop for the family members next week which will be true but great to have you on the podcast for the first time to what welcome to the show where macedonian from i mean kingston-upon-thames so look very nice. Haven't been if eased. But i used to go to a nightclub there when i was about. Twelve nautical nineteen. They let anybody in according to remember the name of it tip. You have kids like to keep you away from it but but could stuff so Matt Debate about who you are as a past Little bit into and start a full strength and then we'll talk a bit depth about fit so who's learn of as a sure thing i mean. I guess it's obvious from accent. I'm american. And i guess i i grew up i think through union such a studied philosophy and interdisciplinary studies undergrad with a minor in cognitive psychology. And then i had a summer job working as a chemist and a refinery. So i would just sort of quite background academically but i think the the common thread through my entire career has been that. I like solving puzzles. And thinking through working through things from first principles and that's why eventually got into the start up world and venture capital investing and even starting my own business for me the the most fun challenges of businesses where there is no playbook where it's a bit of a puzzle you've got to figure out so that's me and too you're in american in king cynical sense how that happens as a bit about that. And also the other at eight powell at five hundred submission measurement capsule to five hundred up to about. And now that's gonna shape. You will now sure. I'm i sorta potter professional history. And how it ended up bringing me to kingston-upon-thames of all places i. I was working for start ups in silicon valley in the late nineties and the first startup i joined cratered. The second start join cratered even faster. You know this is this is what's startups do. But the third one we actually to get some traction and we ended up selling it to company that some of your listeners might remember called macro media you know they made dreamweaver and flash so we sold them for about forty million which wasn't a bad exit back in the day and the united i was selling products to web developers so then after that i ended up getting approach by some folks that pay pal in about two thousand and four because they had sort of realized that if you're trying to get payments integrated with a website it goes through the web developer. Sorry originally joined pay pal in two thousand and four to do develop marketing and then he'd just point just been acquired by ebay so it was kind of a weird cultural shift. Because you had some of the old school you know pay policy scrappy type start-up types and then you had a bunch of like bain consultants. Mba's in their kind of do in the ebay thing and just over the years. The company the company i joined scrappy startup company. I left was making over ten billion a year. So i kind of learned a lot as the company scaled through those different stages building and running marketing teams and general management roles in two thousand twelve. I had the opportunity to move to pay pal. Uk as general manager. Which for me was just amazing. Whenever i traveled internationally just a love what was happening in the regional offices and it has to live and work in another country as it happens. My my wife's was a scientist and she did her. Phd in the uk had a lot of friends who already it was quite happy to get back your so. It worked out well for both of us and then in two thousand fifteen. After eleven years. I ended up leaving. Pay pal just say my learning curve and sort of plateaued and i had a chance to become an early stage. Vc joined the fund five hundred startups as a partner in europe. And i think that's when we met and it's a fantastic job being. I don't need to tell you but you know you see rapid fire. Dozens hundreds of companies are pitching you. You see the dachsie. Meet the founders. You see their metrics and you know for me. I was sort of like this super deep marketer like was selling payment processing. Sme's for ten years and then slow to sorta suddenly go super wide. Start to look at pattern match across different businesses and what works and what doesn't so. It was a great learning opportunity. I ended up backing thirty five companies in my time there and some of them have been very successful. You know have grown ten twenty fifty one hundred x but i think my main takeaway from there is. There's a lot of people in europe who can invest in companies. There are not too many people who know how to gross really know how to grow a startup and a lot of the companies. That i was seeing and that i was interacting with. We're really kind of either guessing. We're trying to do like scaled down versions of how big companies grow and just stuff. That that i knew wasn't going to work and you could see wasn't working and so that's why i decided to leave and joy found startup core strengths. And so my business now. It's sort of like a virtual accelerator where you work hands on with experienced people who built grown and started in scaled startups to kind of sort out alumnus. Stuff that we're gonna talk about today like how do you find product market fit. And what are the right metrics and one of the right hires it early stage and sort of. How do you write that playbook for growth. So that's how i spend my time these days. It's just working hands on with early stage. Startups helping them find product market fit and their play books For how they're going to scale thanks for the background and what a great place a great journey but it was a great place to be white male Up doing sast working with these early stage founded install thompson. You're doing my best to to help them. Grow achieve Investment and scale their businesses asuka. ruling You can help Well as many as many families As we can to wrenching about a Ed again i know the spoke about loss. Romy disaster obsessions remarks during the crisis said and again that you know very well with you running in depth workshop with this founder members at next week and yes we see it. Obviously at the earliest adrian Very common problem. We surveyed of founder members of Recently like what topics you want to say You know offering at m. p. m. f. was like It's just great timing. Let's you know. Let's use the danes of the need to discuss that at and getting to and So yeah so the. There's a number of things that become one. Wanna three run through. You'll you'll the expert On this right so let. Let's talk about what companies families that are gonna listening at needs to. Do you need to think about to get product market fit and so perhaps starting with the matrix if you want assault there and we'll go through some of the other points absolutely so first of all like i already think it sounds like your audience and whenever i speak at your events you have you. Assemble the most impressive people which is why. I'm always happy to anytime. I get an opportunity to participate in your events. I mean it sounds like your audience have already kind of if they're asking about product market fit. They've overcome the first risk. Which is just sort of assuming it will work or you know trying to scale something before you have. Product market fit so just even just being open to the fact that you don't have it actually is like skipping about seventy percent of the domestic so metrics is a critter is the place i start with companies because if you have a good team you get what you measure. I mean if you don't have a good team like that's a whole other ball of wax but if you have a good team you get what you measure and i think the mistake is that people sort of look at post product market fit companies and say. Well you know they're targeted metric is revenue. It's free cash flow. That's how you're running company. We need to go make revenue. And certainly you could be forgiven for thinking that it's not entirely heresy to think you should make revenue but before you have product market fit. You've got to understand their revenues and output and the input is that you need to be able to attract engage customers. And so you need to focus the entire team before you have. Product market fit on getting really good at attracting engaging customers. Now the reason i make that distinction is because there's lots and lots of ways to increase revenue can do it with short-term promotions you can do with bundling strategies there's all this stuff the doesn't create customer value and doesn't get you any closer to product market fit. You can do consulting work. the does make revenue. So normally the way you do this. And if you've heard most like famous startups have this. You'll have north star metric which is not revenue but if some number that increments every time value is delivered to a customer so facebook's northstar is daily active users. Amazon is repeat customers when i was a pay pal. It was gross total payment volume so the amount of money sent through the system. Because if someone sends money through pay pal and receives money. I being delivered so these companies will figure out. What is your north star metric that indicates you're delivering value to customers and then worked backwards and say okay. What are the one two three biggest levers we can pull to make that number go up and so like for amazon. No it's not a business but it's a simple example in this. This actually hasn't changed since one thousand nine hundred seven for their e commerce business. It's price selection inconvenience and if you look at it. I was on business like they don't do anything that isn't price congress business. It isn't price election or convenience. They don't do graphic design. It don't do nice trucks or good delivery service. They don't do brand. They really just do those three things. So sorting out. What is your northstar metric. And then what are the kind of the strongest leverage you can pull the hit that is i think. Sas companies need to start on the journey to find product market. And what about the The challenges back that company's gonna face. I guess kind of in order to make sure that choosing that right so the focus area. What are some of those. I think you know it's the that you're asking again. What's the focus. Area is already super important. Because i think the first mistake people just think they need to do all the stuff to make a huge plan and they try to do everything. And there's no the sounds kind of cliche but there's no points for effort you don't get prizes for effort in startups. You know when. Pay paul when we were trying to kind of knock over a piece of the credit card associations and the banks and take a big bite out of ebay. They had hundreds of thousands of employees that had hundreds of millions in revenue. You're not gonna out work know. Airbnb isn't gonna out work. The hotel industry. They're not outspend. The hotel industry. It's really about which work you do. Because if you've ever been part of a really successful growth story you know that ninety percent results ended up coming for about ten percent of the stuff you do it pay pal. We only really ever leaned on about five levers to drive growth ebay and shopping carts and hose and web developers and a bit of inbound sales of drove all of their growth. That's not all we did. We did tons of stuff. We mobile apps. No in hughes we redesign the logo. A couple times you know how much time and money cost to change the logo inc. None of this stuff did anything. But if you're a little stop you don't have ten thousand employees and ten billion in revenue to go try stuff doesn't work you've got to figure out what is that ten percent really quickly. And so that's why i say. What are those key drivers sort of if you work backwards. Where's the biggest leverage. So for example companies will spend a ton of money on facebook advertising kind of works. It kind of doesn't work and they're like well. Facebook doesn't work for us. We need a better facebook advertiser. Well facebook works. It's not broken right. There are plenty of companies making money on facebook. So you're probably focused in the wrong area is probably what you know. What's happening to that traffic. Are you sending him to a compelling page is your conversion rate ten or twenty or thirty percent to a free trial book demo if it is facebook's working for you if it's not then facebook probably isn't the problem is is probably finding language market fit. It's probably something runs your proposition. So that's kind of one common example but the main point here is if ninety percent results come from ten percent of the work you do after you've really honest with you solving good if during out. Don't try to do everything. But what's that ten percent. What's the suffolk and really big for us. How do you feel like finding that. Ten percent What are some of the things that you need to do to bouncy welcome from backwoods but say yet. How would we get to that. Yeah so first thing is back to the metrics if you sort of not the entire business in equation right so we get traffic. Some percentage of those visitors convert to a lead. Some percent of those leads are marketing qualified. Percent of them are sales qualified blah blah blah. All the way through a monthly recurring revenue. You can map out the first thing this is to identify. Like where's the bottleneck in that system. I told you in union worked in a refinery and is literally you know dirty. Oil comes in one end and clean. Oil comes out the other end. The entire throughput of the system governed by some bottleneck in there somewhere. There's some piper. Some machine is the narrowest point the entire system. And if you can find it in open it up. The whole system runs faster. We'll same thing right. There's a bottleneck in your funnel in your business somewhere. I don't know if it's conversion of visits to lead or retention or revenue per user. Whatever it is you're gonna figure out what the bottleneck is in first of all just focus on that and stop doing things that don't impact that and now the other piece of that is just sort of do a simple mathematical experiment on paper and say if we absolutely get this right could this business right if if your sales team is closing seventy percent of their sales qualified leads. You can't do anything to there's going to attend your business. You can only get from seventy percent to one hundred percent in your probably hitting diminishing returns so just ask yourself on paper you know how big this be okay. We're going to do some. Pr we get a bunch of press coverage. How much could i get us. We keep getting trafficker will go away after the press coverage so the next piece is just to take all your ideas and just ask yourself. How big could this be if it really works. So then you've got a bunch of ideas you're trying to narrow down to the most impactful stuff ask yourself one is focused on the bottleneck and then to if this works could it be really big and if it doesn't pass those tests and you're a little startup and he got ten employees and three hundred grand in the banker. Whatever like don't do it. just do something else. What about instead of debating. Whether you've like key assumptions around the business you you. You're getting to that initial gonna phase. You really want to deepen that in any you want. Then you think you're ready to kind of scan off. How do validate the those key assumptions at that point to say that we do have it now. We really want to deepen it and go through. So how do you know when you have. Product market fit yet easy way saying it right. Yeah i mean the joking answer. I give people as i say if you're not sure you don't think i forget who said this. Very wise person said that looking for a product market fit. It's like pushing a boulder up a hill and having product market fit like chasing a boulder down the hill that both of those are hard work but very different kind of work. So if you ve very good unit economics on your acquisition if you know you can hire sales people off the street and with some basic training they can sell your product right. Then you've got product market fit if you're getting customers coming in the door you've never met before and you ask. How'd you hear about us and they tell you someone else that you've never met or heard of before right when things really just started taking over and you start to feel that flywheel effect that's fantastic. That's what it means to have. Product market fit and at that point again. You've got to figure out like okay. What's causing this. What is the playbook which which things that we're doing are working and that's gonna be which messages which who's our what's our ideal customer profile. Who's really buying this. Pb who in the organization if it's a consumer who are these people are what are they specifically trying to do. There were helping them do. And then how are they finding out about us like what are the main channels that we know are working for us because there there just. Aren't that many channels out there right. There's inbound and seo there's pay channels is variety and referral word of mouth is banging the phones and outbound so like which then partnerships. Yeah so which of these channels is really working for us and then double down on what's working and just say like spent seventy percent of your resources and your energy just lit scaling and try to max out what's working and then spend thirty percent of it starting to experiment and say okay. Well this is great. But we're kind of a one trick pony on this one channel or this one audience. Where's our next s curve of growth. Gonna come from so keep thirty percent. Resources continuing to explore customers product features channels and see where he can sort of get that next s curve growth. What are what are the the pixels common mistakes that somebody's making that. Say the way wrong from athlete getting that. They think that they're getting it. But actually probably not narrate and making phone sex. What would you say the pimples. you know. we've we've talked about a lot of them. I think it's kind of implicit in what we've talked about. But you know the first one is thinking that you have the playbook already. You have a plan to do this. You know if you're trying to grow and establish company. Things are fairly predictable. You know what works for you. You can take your revenue targets. And maybe you're at forty percent year-on-year and you wanna make it sixty percent year-on-year and you'll know by march if you're on track or not because march was going to look a lot like february is gonna look a lot like january but if you're a product market fit you just don't have that predictability if you've seen these if you go back and look at these graphs hub spot or airbnb or any of these slack. Daily active users or revenue. Whatever it's sort of like a hockey stick whereas like nothing. Nothing nothing nothing bank. So and these companies like notion was working on their product. I think i've been working on notion for ten years before it sort of became an overnight. Success slack was seven years. Airbnb took about five years. This is a long journey of indeterminate length. And you don't see that sort of incremental progress so the first thing is like you know especially like and see people coming in with their pitch dax in. Here's our three year revenue forecast. It's like like almost no customers or revenue. How are you going to make a three year revenue forecasts. So i just abandon this idea. There's illusion of certainty. There isn't a plan there isn't going to be a time line. There's going to be as a process of rapid experimentation gotta hypothesis about the customer about the message about the channel about a referral partnership about which features you wanted to talk about. Just get those things build measure learn. Get those things in market and tessema quickly as you can and every step every iteration of that journey like slack seven years airbnb for five years. They weren't just sitting around and playing. Xbox were trying stuff they were experimenting and getting smarter and learning. So i guess back to answer your question like mistake number one is just sort of assume. You've there's a playbook or a plan and just execute it if it's not working like keep putting more money and hiring more people in buying more and raising more money that's not it like you've got to go find product market fit and i think that ends up kind of putting your new hires in opposition to. I think people are very quick to say okay. You know we're just gonna go out and hire a genius head of growth. Who's done this before and knows how to do it. And the problem with that. First of all the companies are going to hire them from already. Have their playbook sorted so this person skillset if successful in a scale up is running the playbook and you need different skill set. You need like figure out the playbook so the first thing is your high for someone different but the other piece of it is like growth. Can't just be a silo in a startup in a big company. You got your marketing team and blah blah blah and head off of the sales team and two product managers goes to engineering in a startup. When it's time to grow the entire company needs to focus on growth because a lot of times your growth is gonna come from product features. Your insights are going to come from analytics. Your customer feedback is coming from the sales team. And if there's only eight or fifteen or twenty view in the company you silos. You're one advantage you house a startup over these big companies is that you can ally in and be nimble and change directions quickly in view. Start creating all these process silos early on like. You're just you're kind of cutting off that one advantage. You have so the other big mistake i think. Is this idea of just like we're going to hire a marketing person. Gala marketing budget. They're going to build a marketing team. And we're going to grow and really does know that you're going to have to figure this out is gonna take your whole team. You need to hire people who are good at trying stuff and figuring stuff out not like the person who went and did this at this other company. Let's recap the on top sort of three of three to five things that that listens can do to achieve two. Pm f- an will come on to portray a couple of questions at that. Okay sure so yeah. Let's go back over this. The first thing we talked about was metrics you get what you measure so alive figure out your northstar metric and align the whole team around it and that when i say a line i mean you should be able to go to any employee your company and say how does your work impact on northstar metric. And that's going to encourage them to make good decisions about which work they do. Which is kind of point to is. Ninety percent of your results are going to come from ten percent of your work so which work everybody does is actually super super important. And so you can use that northstar metric to force conversation with each person. About which work are you doing and then really only do the stuff that's can potentially have a really big impact now. In the case of a lot of companies that impact is not the level of like which channels are we using or are we any good at facebook advertising. Seo is not about how much money raise is much more around kind of actually locking in product market fit finding proposition and ideal customer profile and channels. That are gonna work so you pursue that through this process of experimentation like. Let's try. This is getting closer or not driving our customer engagement. Getting on northstar metric. So that's kind of it. You know if you run through this with a metrics driven process of rapid experimentation with the idea of being able to find customers engage them and get them using your product. Then you know the work you do is going to cause learning. The learning is going to cause customer. Engagement and customer engagement will cause revenue like. It's not that hard to monetize a saas business. That has really good engage customers. So that's kind of my quick summary. Maisy thanks matt. Who's drinks you as you said. It's like a remote virtual accelerates and you got a new kogo starting at soon issoire wins that june. I we actually just open applications. I'm not sure what date this has gone out. But you may nineteenth and then What sort of person would benefit from. Houston icy paid for strikes. Yeah so we tend to work with seed and series as startups. Really kind of vertical agnostic. We have coaches in there who've done mobile app distribution enterprise be saas but i think these are companies. Who are ready at this point to like. i said. Turn the entire company's focused to growth. So if you're still building a lodging product this is the right time if you're in the middle of a fundraising. This isn't the right time. But companies who are really ready to say okay. Let's align the entire company right. Our growth playbook figure out what are going to be our levers the best channels for us in language market fit figure out what is northstar metric and align the team around this metrics driven process of experimentation if i can be a priority for you right now if the stuff that you're doing isn't working then that's kind of the ideal customer for us also waking people Scared to find out more on on that and apply if the fits so our website is startup core strengths dot com all one word startup core strengths and also on there. There's a section called. Learn where i put up a bunch of resources. There's like a marketing strategy. Template you just wanted to kind of think through from first principles. What should our marketing strategy be. I've got an e book with some tips on hiring as a bit of stuff on language market fit product market fit as well. So there's a bunch of free resources on the site as well so if you want to check it out startup core strength dot com recently just did our website changes looking nets so the joke on and For the workshop next week matt satin family members. These go out all that workshop. But i know he's going to be L. a. and interested moral more absent found them. But just gotta stop of the ships and they. Then you find out more on that but matt affects a much great speaking to you Greg set that shannon acre site on the more death next week Fabric is being pleasure. Thanks for sharing knowledge the woods by pleasure. You ask you asking exactly the right questions. It's always great talking to you. Alex thanks for tuning into this week's episode of the says revolution show. I hope you enjoyed it. And if you learn something from at checkout sas stock dot com slash events to find all the upcoming sesto conferences around the world.
Tech's Tiger King
"There's been the sense over the past year when it comes to tech. Investing more deals are actually getting done because start founders and investors can schedule more meetings over resume. Gone are the days of in person. Pitches on santo road or new york. You get your mic your custom backdrop. And you go you pitch for hours. On and for tech investor tiger global management that meant an eighteen thousand dollars. Zoom bill last year. That's nothing of course for a sixty five billion dollar fund but it's another data point in a clear story that amid the flurry of tech deals tiger has been the flurry. Est it's alpes. Sequoia capital recent horowitz and excel in the number of startup deals. It's done this year. But who is this quiet press-shy firm who is tiger. That's the subject of our first segment on today's episode of the four one one. I'm cory weinberg. And i'll be speaking to my colleague. Kim clark who just came out with a fascinating feature inside tagger goebbels deal machine. Kate spent time talking to founders. Nbc's about what makes the firm unique and what they're investing streak tells us about venture capital in twenty twenty one. Then we talked travel startups investing in trouble. Startups has been understandably week over the past year. But it's starting to turn the corner. Wendy pollock the information senior editor. And i will have a conversation about story. I wrote this week about what's going on with startups like saunder trip actions and hopper and we'll even talk a little bit about airbnb but first let's talk tiger global with venture capital reporter kit. Clark all right kate. You've just spent a bunch of time trying to understand. What has the impact been of this one. New york hedge fund tiger global management but their impact has been on startups in silicon valley. Why did you pursue the story. What what got you intrigued by by tiger. The reason i chose tiger was because they were leading rounds pretty much every day. I was getting an e mail in my inbox from you. Know someone that works. Npr saying are you interested in in writing company. They're announcing around. Tiger global is leading and i've heard of them for years of course they're not new as very clear in the story. They've been around for two decades But this year kind of you know the tail end of kobe. I guess you could say and amid covid. They were striking more deals than they had an in their history and this year in particular about four deals on average per week so so nearly every day so that to answer your question it was the sheer pace of deal making that got me so interested and the fact that these people that work at these firms have not really been written about they managed to deploy several billion dollars a year. Many many tens of billions of dollars a year and yet fly very much under the radar. So i thought there was a good opportunity to sort of discover who these people are and what they're interested in and how they manage to move so quickly despite being a really really really small team totally. Let's start with the people will go into like the pace but tigers not affirm that like some other attention grabbing. Vc firms They they aren't about big personalities. They're not about people you know they're not masayoshi said mark andriessen who of you know historically put themselves front and center who are these guys so the first led by this guy chase call men and his private equity unit. Co-founder scotch lifer. Who really calls the shots. When it comes venture capital deals he has the autonomy to make decisions and does and like i said. They've invested in over sixty companies in just a three or four month period. Twenty twenty one and then kind of reporting to him is this guy named john. Curtis who has only been tiger for about four years. And has you know led rounds and i think fifty or sixty companies in that timeframe many of which you would you would certainly have heard of data bricks and others that have generated a lot of headlines in the last few months and that is really the some of the team of people who have a lot of power firm who are calling the shots on deals there are others including other partners and analysts and data scientists who work in the private equity side. I think one of the biggest takeaways from me and something that really surprised me about this reporting was how small of a team it was and how somehow they're able to move like three or four times faster than a lot of the capital firms that we cover i think one of the most eye-opening examples in your story discovered a little bit of play when people tweeted about the piece It's not like tiger has an army of internal. They don't have an internal team that is like coaching startups on sort of what to do. They're like hiring bain consultants to do that. What is tigers edge is. It just is it. Money is a moving quickly. What has gotten in them into so many deals in. What makes them unique. yeah. I do think it's moving quickly. I do think that these partners that i mentioned of course have expertise that they that they do share. I would say generally and you just kind of hit on. This is that they don't spend a ton of time with their portfolio companies. I mean if you're a team of three or four or five people and you have a portfolio of say five hundred companies. You don't really have the ability to take board seats or to spend your evenings and weekends and afternoons on the phone with various ios in instead they spend a lot of their time pursuing new deals. I think edge is. They can write really big checks. They're very comfortable tie valuations. It's not something that trips them up like might a different more classical. Vc firm and they have a lot of expertise on navigating the public markets. If you're if you're pre ipo business looking for pre ipo round of capital you probably want to raise money from tiger Versus a traditional venture capital firm that focuses more on growing a business in say product market fit and go to market strategy. Those aren't really things that you're worried about your worried about. Who were my investors going to be a public company. And can i find investors. Now that might stick with me through my entire. Ipo process and actually several years beyond that po. They want those long-term holders k. That's what they always say exactly long-term and that's what they want. It's a that's the truth. And that is something that gives them an edge coupled with all these other things that we just talked talked about the last time that there was a venture capital firm or sort of generally like a private tech investing firm That was showing the spotlight or so in the news every day. And you mentioned the centerpiece. It was softbank the pace of deals that they had coming out of their one. Hundred billion dollar vision fund was insane and vc's kind of traditional vc's kind of derided softbank. You know kind of considered it dumb money. Sometimes they're returns been obviously quite good. Thanks to coupon an uber. And and some other bets but people. How do people feel about tiger. Are these guys super savvy of the super smart people skeptical of how faster investing when i started reporting on this. I did expect that. The investors that i spoke to who of course weren't affiliated tiger. I expected that they would not like tiger. I expected that they would. You know sort of insult them in the process and say they're stealing all of our deals. They don't care about valuation their irresponsible. They're just like softbank. And that's not what i found. I think a lot of investors do like working tiger and don't necessarily want to insult them because they mark up the value of their companies and not just not just not just more value but like they provide capital to companies that are very capital intensive. And who need to raise more money and who's investors may not want to be the ones writing them a five hundred million dollar check another five hundred billion dollar check like can do so. What i learned is that they have a lot of valuable relationships within venture capital. They really admire a lot of venture capitalists. I think there are you know. Of course i came across people who think they are way way way too. i guess. Insensitive toward valuations and our way too willing to pay up in such a way that is actually affecting the entire markets. And i think in a year or two or three will be able to see more clearly as we are now able to a- sopping sort of what. The impact of tigers increased pace. This year has been but for now. You can't really call them out and say oh you know you're you're being really responsible because it looks like on paper that they're doing an amazing job investing in some of the best companies stepping back like after you spent close to a month on on this piece. What do you think this streak. The tigers been on tells us about venture investing in two thousand twenty one. I think it tells us that. The hedge funds and the quote unquote crossover funds which includes corporates and mutual funds Have the power and quote unquote silicon valley right now Basically every late stage round that you will see is and will be led by these players. And i think that's a trend. That's not going to stop historically in a downturn. Those types of investors have backed away from venture. And that's kind of something that venture capitals. We'll tell you if you want if you talk to them about it. I don't think that will probably happen this time. Though because tech companies are are an essential part of the economy in a way that they weren't in previous downturns. So i think for me. The biggest takeaway is just sheer influence that these hedge funds have over venture capital and startup fundraising rounds and and how easily they can swoop in. And when deals by due to the assets they have under management again the public market expertise and They're you know they're more casual. Approach toward valuations. The story was inside tiger global deal machine and you can find it at the information dot com. Thanks kate for joining me today. Yeah thanks we're going to turn our attention now to travel. I'm wendy pollock senior editor the information so corey a little over a year ago travel pretty much ground to a halt around the world as covert hit and for travel. Businesses suddenly wasn't clear. How or even whether they were going to survive and now a year later it looks like plenty of them are not only surviving but thriving in the sense that they're attracting investment. Let's going on. Yeah it's it's kind of crazy. Phenomenon is particularly if you think about just the past year so the best example of this that we let our story with this week is startup called saunder which essentially leases out apartment buildings in cities and run them as almost hotel properties. They were one of the first start ups to really lay people off in earnest last march they laid off like a quarter of their staff hundreds of people and they you know their revenue fell their losses. Grew in the past year but they arrived and now they are going public spac at evaluation. That would be nearly double. Its last round. And so then. Sandra if handful of other examples like really show that if you survived as a travel startup over the past year here maybe sitting in an okay position. What does this fact. You really indicate is it. That travel is coming roaring back or is does this have more to do with the investment frenzy right now valley so i think you could ask that question about almost every deal because when a company. Ipo's historically you're like okay. Yeah this company has made it. You know they they have gone through the gauntlet. The banks have have said like this. Is a good company blah blah blah with the spec weird indicator because the companies don't have to necessarily have everything buttoned up or tied up in the same way as you do an ipo so dishonor spac mean like it is now poised to take off not necessarily but it at least means that some pretty smart investors are are interested in taking this company pop public and it at least good enough at raising money and cutting costs to survive and in a crisis. That's better than a lot of its competitors And at least give it a chance to keep fighting and like you to have a lot more capital and when you're talking to people in the sector. What are they saying about travel. It really depends on what type of travel you're talking about and what geography i think still this year within the. Us people are really expecting a pretty significant travel. Rebound people travelling mostly within the country. Internationally it's obviously tougher. There's still a lot of international travel restrictions and lockdowns. And if you're a business that relies on a lot of overseas travel. You're not going to see great numbers yet. So it really depends. Companies in the. Us are definitely feeling better than companies in europe. For example where. Vaccinations have been a lot slower. People have predicted the demise of business travel for a long time now. But is it possible that the pandemic is going to have a permanent dampening effect that now that we've seen that a lot of office workers can can have meetings very effectively using zoom that companies. Just frankly aren't going to want to pay for business travel anymore. Totally i think that real likelihood as a permanent outcome from this pandemic it was sort of one of the classic lines that i i remember people having even when the pandemic i broke out last spring i remember sam altman the y combinator former white combinator leader writing a blog post talking about permanent shifts from the pandemic last april and he also sits on the board of expedia and invested in airbnb and he said busy travel not coming back and so this has been like a threat and brian chessy. Ceo very says. It's all the time. So let's talk a little bit more about airbnb. Because a year ago it was borrowing money at exorbitant rates to keep cash coming in by december. It had this blockbuster. Ipo so how did it manage that. A few ways it. It is quite surprising though. They raised this capital in. May that essentially apply to seventeen billion dollar valuation or eighteen billion dollar valuation. It's there now trading at a as a at a above a one hundred billion dollar market cap they got there because a they cut costs bulat. They have this story to tell investors that they rely more on direct traffic than they rely on google advertising which is like a huge thing and in online travel every other company relies. Heavily on. google and airbnb doesn't quite as much so they've had this nice story to tell investors whether they're valuation is justified by the fundamentals of their business. Like probably not i but you know for now. They have like a little bit of runway. So what should we be looking for from airbnb over the next year i would definitely be looking for. How do they grow supply. So what does that mean. so that means they. Are they getting more homes onto their platform onto their site or more people deciding. Hey i want. I want to rent out my home. Airbnb are more property management firm saying like. Yeah we're going to gobble up more homes and post them on airbnb. Are they able to attract enough places to stay that. The potential travelers that go on their site are going to find a lot of good stuff. They're going to want to be like okay. If i'm going to travel this year it's gonna be on airbnb. It's not going to be on a hotel or another travel site. The question is going gonna be when people go terribly and be are. They're going to be like enough places for them to want to book. That's really interesting. To what extent does is regulation a factor in the supply of places to say. If you wanna give me the benefit of the doubt you would say say travel is now going to. Maybe look a little bit less focused on big cities if people truly use the pandemic to rediscover worl- environments and and hidden gems a place to go and not everyone's just flooding into paris in new york every year. That's great fair beam because they are you. Know regulation in those types of cities are really tough for them and are only gonna get tougher. I think but if travel is more spread out into places that maybe would welcome more tourism rather than over tourism. That would be good fair being be. I don't know if you know if that'll happen. You know maybe he's got a taste for life without airbnb and they like it I think that's definitely something to watch. That's our show. Thanks so much for listening this week. Thing so much to kate and wendy for joining me and thanks to ariella markowitz ver- producing the on. What have a great weekend everybody.
Ep. 57 AI & Blockchain insights from Dessa
"The. Hello. Hello. Hello. Welcome to insure blocks. Your dedicated podcast of blockchain and smart contracts and insurance industry, I'm Willie dos. Cough. Your host for this podcast. We will discussed AI and blockchain was insights from Desa for this podcast, very pleased to have pct Bricusse insurance industry lead. Desa paul. Thank you for joining us today. Could you please give listeners quick introduction yourself? Yeah. Of course, thanks for having me on the show at great to be able to speak to your audience new about two areas, I think are going to have some really transformative impact on insurance blockchain artificial intelligence. So a little bit about me. I work for a company called Desa in artificial intelligence company or our mission is to help large enterprises scale AI in create meaningful business value. So my role Edessa focus. Is on the insurance fertile where actors an intermediary between large companies, and we're looking to solve challenges using on one side. And then connecting that with our solutions. Viet our product services platform on the other. Been my insurance been in the insurance vertical entire career. I started as a reinsurance broker with a on Benfield been worked at the seated reinsurance team as jump turns. Most recently, I was a management consultant with Deloitte in the strategy and operations practice or I focused on transformative technologies in the industry. Very specifically at Deloitte, a in blockchain were to areas where I spent a lot of time on excellent. Thank you for that. So as you know, as it is cost me here, tra- blogs. Could you please explain to our listeners what is blockchain and how does it work? Interesting question. Yeah. So I think she blockchain gets a very big rap. Very simply I think it's a new form of data storage were records are stored on shared ledger between a group of participants at this is a very valuable idea where speed of Advantix Shen is important like in the transfer value, and this is recognized this sort of evidenced in like payments or in say transfer of assets in what's really revolutionary about blockchain is all of this can be done digitally, so good way to conceptualize this or maybe a party trick at your next. Cocktail event is let's let's play a quick game. And let's pretend that we're going to assign something astore value. Add in this case. Let's pretend it's my phone and possession determine or ownership is determined by possession. And there's no other phones in the room. So let's say I'm holding the phone. Right now, I'll ask you like who owns the say you own the phone. Exactly either done other ones the room, you know, it's very easy to see that ownership is mall. So let's pretend I passed the phone over to you. And let's say in question who owns the phone, technically? I would say it's still you you just pass it over to me. Sure. But it's in your hands. Like, you know, you feel fairly confident that you're holding that phone, okay? So we can see that the transfer physical goods is very easy to transfer ownership. But let's play the same game. But this time let's change the rules where I'm gonna take a photo of mice own and say that photo is redeemable for the phone in the picture. And now, let's assume that I Email you that photo. Do you still believe you thought may own the phone, but UMass send somebody else exactly it's digitally things are easily reproducible? Transacting digitally. It's really hard to create trust of ownership. And this is like this really novel turn that the communists have called this double spent problem in that physical goods. There's no way to know. It's been spent twice ambit- digital world. That's really hard and the world to combat this stash all forms of these intermediaries. So we trust that will be on these things. People say we own you need a third part about date that where blockchain is this really revolutionary idea where suddenly we can transact with parties we don't necessarily need to trust. But have confidence that we're getting what's the other party says we're supposed to be getting? Yes, we can the context here at insurance is a lot of very exciting things that are happening in particularly of where I've spent my career in reinsurance. There's a lot of advancements that I think will be very transformative in the future. Great great. Thank you very much for that. And I love the analogy between the phone physical phone and the picture of the phone. So could you tell us a little bit about what is Desa and also what is its mission? Yeah. Happy to add. So like I said before I mean Dessus a relatively new company in the world by I'd call us dinosaurs in the term or in the length of time in. So our mission is to help large enterprises scaly, we think it's very transformative technology, and we really think companies are gonna need a lot more of it in the future. Ab so there's probably two parts to company you need to understand the first is truly world class a team. And happy to get in a little bit more of that maybe later on. But the second part of our company is being very skillful at selecting where should be used for this team. We've hired a whole bunch of former MVP consultants who are very skilled. Let's say at navigating large enterprises and their focus focuses to find opportunities ad defines, very specific problems get management buy in and then transfer those requirements to that engineering team. I previously mentioned to start building those models. So Dessel works in highly regulated industries, so we focus on financial services telco. Within financial services were working in banking, investment management, credit cards and insurance at so all industries, which require very unique skill set to operate, but compounded by very deep expertise in a. And then it we've developed a platform, which helps enterprise build more artificial intelligence models. Justed? It's interesting when he said, he know that went on do you guys specialize in is where a I should be used which is kind of similar to some of the challenges that were blockchain where in two thousand sixteen was used pretty much everywhere. Independent whether or not it was relevant. So it's good to see you guys have a focus on using a only for the right business cases. But before we proceed a love for you to give us a definition of what is artificial intelligence. Yeah. Similar perspective. I think is laughably baked term. So I'm sure there's a lot of places you can go to get it defined. Maybe I'd answer this in a little bit different way in the story that not a lot of people know about. And that's really sort of the advent of the modern KFI euphoria so much like I'd say bitcoin was probably sort of the reason why watching Don on the map. I mean, we can really trace the roots back to. All the excitement in the space to contest in twenty twelve called image net and consider image net in the Olympics of difficult computer science challenges, and in this challenge as they came up with a really hard contest to say we want computers to be able to classify images and to make it really hard. They put five million images into a data set and someone labeled all five million ages in a way that you can test computer programs on the accuracy of how well they were classifying those images. Well. Mike hometown Toronto has this very good university university of Toronto. And there was a team there at who really cracked the nut in twenty twelve by combining two things. That hundred month for so they figured at the recipe of by using very very powerful computers at this technique called deep learning, and essentially they blew the competition out of the water in twenty twelve and that's really seen as sort of the. The advent of when a is came very topical all three of those team members went onto spectacular careers at Google bought them out. And that's really when we will be came in a I I company. But I guess the real interesting part of the story is the guy who made that technology by the name of Alex tra-, chef ski and his technology was called Alex neck were very pleased to say that we actually just recruited in to our team here at Desa. So wanna talk about bench strength on the side. Really the most cited at condemning in the world. Helps us advise the largest companies in the world. Now. I remember when we when we met a London you were discussing about some ways of looking at our fishing talent in terms of three buckets. Do you want explain to our audience? What are three buckets are? Yeah. Happy to. So I think when we discuss a I it's this really broad rela term, and it makes for just a very good anchor of saying, you're doing some form of prediction, and where we start getting a little more granular is when we start focusing a definition on something called machine learning, and this is gaining tunnel. Traction and machine learning is. Technology that essentially is improving itself, and we can use it for things like predictions, and we can put in some data it a learn an it'll get better and better. Where I think the most excitement is coming from in something where we spend. Most of our time is this area called deep learning and deep learning was the technique used to win image net in twenty twelve and this is gonna show the most amount of lift in the future. So when we think about machine learning performances, very good in you'll expect to get some, you know, single digit left, but deep learning is truly transformative, and he would expect to see outside for terms on using this technique in certain types of problems. So what actually differentiates too 'cause I mean, I can understand what machine learning is. And but deep learning sounds like something like machine learning deeper. I mean, get could you perhaps some light on? Yes. So maybe another you've been hearing a lot about this term of blackbox. In that we ingest all this data into an algorithm and that algorithm spit soda solution and machine learning is very linear. We can see all the moving pieces. We know why derives certain answer what's very different about deep learning as we don't know why it's coming out with at times. So we just it with massive amounts of data billions and millions of records, and it's computing all this to figure out the weightings of which data's important in which does not AB. And that's happening over hundreds of thousands of simulations to figure out that account. But what's difficult is as humans? We can't understand why it's giving us that outcome. So something we spend a lot of times thinking about well, can we control for the data that goes in start creating ledger to sake, we understand more about the process to get there? Don't some controls into making sure there's no bias in the data or making sure that we have some sort of good governance of it. But that's truly where the new era's happening is in this area of deploring. Okay. So I guess one example pimple machine learning is the IBM Watson, and is derelict such an equivalent example of deep learning or it doesn't exist yet. Yes. So. Image net is probably like the real best example of where it became import. His in ossification learning is being used extensively amongst at the high tech companies of Google Facebook Amazon. And really I'd say deep learning is one of the technologies that power himself driving cars would be the immediate one people would have the able to associate with excellent. So we've got machine learning deep learning and what is the third bucket? So I call a I is broadly the field which people are in sheen learning is a technique within that field. And then deep learning is an advancement with the net. So I'd call maybe two techniques in an umbrella term, okay. So what does having a first tried mean in hot as one adult one? Yes. That's a really good question. I think the analogy would be if we look at companies that are very successful right now, say Google, Amazon and Facebook. Things that are making very successful is a large pool of data and than in -bility to create insights from that data, and what they've been able to do is they've been able to replicate or they've been able to produce a large number of these models at scale. And what that means is everything they're doing is getting better and better. Far greater than humans can do. And so an AI for strategy means setting up the capability that everything they can do relies on producing lorrimore models. So if we were to compare those companies in the number of models, they have and while it's not public. We could assume it's somewhere in the neighborhood of hundreds or thousands of various malls. I think when we compare it to more traditional non tech companies. We see that foundation to get there doesn't exist yet. So they obviously don't have enough models generating the insights at the technology companies have so to Dopp. This our thesis is that companies are going to need to become. Builders of models in that they possess equally large amounts of data assets. And they need to start thinking about how unlock those data sets. And one way they can do that is starting building models internally to generate those insights. Now to do that. I mean, you know, we've had a couple of episodes on intra blogs regarding data how data's unchanging both in terms of the quantity of the data on terms of type of data structure, non structured and the third one is that the data has certain perishable element. You know, if you don't consume in straight away it will perish it's in terms of its value. So how does a work and for those companies was quite a few of them, especially with an insurance not digital. How how can you? Adopt such a is dry. If you have limitations on how digital you are. Yeah. I mean, that's a really good question. And we've been quite successful at starting to build momentum. On use cases that aren't obvious, and I can use an example of working with one of the largest investment management companies in the world. And you know, everyone would jump to be like, well, are you are you doing some trading strategy or doing some asset allocation and the first use case, we deployed for them was doing fax two courses types in that they had a if you can believe that they still get a whole bunch of submission documents for new policy account or new best accounts fax machine, and they had a whole a pool of people reviewing. X documents that were coming in. And essentially we were able to create a very powerful tool that would review the facts documents make sure they were accurate before they went into the court system. So there's tremendous amounts of data resist in the vertical earth within enterprises in to the point earlier, we talked about it's being skillful at knowing which asset which data valuable in. How can you leverage that to really get some lift by deploying models? Understood. So there is a move already picked up on one similarity between I'm blockchain, which is you know, we need to ensure that it is used for the right business case in his not used for other areas. What similarities do you have you come across? I mean, I'm assuming in blockchain. There's a lot of misconceptions in our around what it actually is. And potential. Impact can have on for example, replacing jobs. Yeah. Controversial question. I guess. I think similarities are both technologies are revolutionary over of Lucien airy. And both. I think have the ability to transform industries in the years to come. But I think if we were to kind of compare them, let's use Saint maybe a people process and technology framework when it comes to block chain or distributed ledgers. I think what's interesting is consumers or most people won't know transformation has happened because it's an infrastructure technology. No consumer on the front end should see any difference in the way, they transact with the company. And I don't know if we'll see in our lifetime. The decentralized organizations. That this type of technology enable businesses require people run them. And there's a lot of branding and insights that go into it. I still think that skin you exist on the question of disintermediation. I really think that blockchain is very powerful tool in wall. It has the power to dissenter mediate. It also has this network problem of it only works if everybody's on board. And I really think that the brokers in this case have a really compelling opportunity to be change agents that get this technology, deployed at scale. And I think if you were to compare a scenario of where. We try the industry tries to mediate don't think it would be as successful is if they enabled the brokers to be that change agents. So where Denver think two very big impact will be though on people is a out. And I think the reason why that is it's already working. We can see the impacted has a very very quickly, and we can measure the payback period in days when it's deployed, and it you're measuring you can measure very quickly because it's so affective like maybe I'll give an example on like a customer recommendation engine. When you're on YouTube, and you're looking at videos, there's a deep learning algorithm, which is suggesting which other videos, you wanna look at the same case can be for product recommendations say for an insurance company. We can cluster types of customers say instead of barring them with a whole bunch of offers. What's the most tailored offer to them? And that's showing a tremendous amount of left, and is very valuable. Where I also think we're gonna see a lot of change is supercharging employee effectiveness. And we're seeing this in a lot of work we've done at I can give you example in Oakland. Call center where we were able to take the official effectiveness of ozone call centers from something about the effectiveness of throwing dart to a dartboard to about ninety percent effective rate on the call, and what means is that employees are really seeing the by of how it's making their job more efficient. I think if we think on processes something, I think is very interesting. Is considering behavioral psychology in what types of jobs humans get a high degree of satisfaction from. And I think in deal t you know, it's transactional in nature in those jobs tend to be very tedious. And something very repetitive. Something don't think humans are necessarily well task doing. So I think we'll see a lot of improvement in reducing those types of rules. But where I think is very interesting is it can outperform humans on a variety of tusks. But where I don't think it will compete is on things like empathy understanding relationship building or even humor for that matter like, these are all very hard things. Computers. Can't replace right now. Right, right, in your opinion. Because something we discuss about both like a in blockchain you need a good business case. What do you feel constitutes a good business case for a I? That's a really good question. And something we get a lot. When we speak with a lot of companies. It's an area where companies don't know what they don't know. And the only comparison they have is all of these people who are coming with unique business cases, which make it very easy to evaluate. So I would say the way we look at a good business case are what are the trade offs between data infrastructure in the business case into your point earlier, you shouldn't be doing a for the sake of Irish shouldn't be doing blockchain for the sake of blockchain it needs to be grounded in some provable. Benefit case. In one way, we think about this as. We this is where our enterprise services team is very very valuable because they're able to go in and start building roadmaps. And generally what they'll do is. They can identify. I'd say the neighborhood of thirty to forty business cases in dots by business unit, and we could scale to function on or some other department, but there's a limitless amount of things you could be doing with your data in going in to figure out which one should you? Do I which one will have the least amount of risk generate payback. The quickest and thanks Paul. So can you give us a good example of what is a good business case for a I that you've recently worked on Jack. That's a great question. And we've done a lot of transformative work in banking in credit cards generating. Aggregates of hundreds of millions of dollars benefits. But I think the coolest thing that resonates with people. We talk about is actually work. We didn't space and very pleased to report that our team recently just broke a world record for supernova detection while. It's probably really good dimple of how we approach deploying at events day I systems in that. There's a very very large space culture here at the office. And we really like thinking about it poses some really interesting questions. So the team thought how can we apply our techniques to something we think is very meaningful. A- nettle drive a really big impact. So we started poking around the astronomy communities in saying like, how are you guys exploring the stars? Like, what are you guys doing? And by interviewing a whole bunch of subject matter experts in understanding their techniques in what type of data they producing we landed on what we thought was a pretty good use case. And the situation is essentially satellites pointing up at the sky and recording to the neighborhood of thirty terabytes of data and evening. And astronomers the next day need to go through in review hundreds and thousands of photos. So there was a four hundred million photo data set of supernova. Supernova czar important because they're essentially dying stars. But they make up say sixty percent of the energy in the universe. So the more. We can understand them the further we evolve our understanding of space. So. The problem is too much data and there's like the needle in the glow the university stack. So our team was able to create a model which essentially predicts what's a supernova. And what isn't and this cut the time. It takes astronomers to review these photos by almost half. So when we think about the time they're using they're now actually only looking at supernova and not. A negative result. So it's a really really cool story of. How we deploy deep learning models. A really big out Tom of something that wasn't possible with human efforts at in an area. We think is really cool. Ed. What's also, very interesting is we then thought this was some very interesting technology, and we called NASA with this. And we said, hey, we think we've done some really cool stuff. So we're now discussing with them about putting this technology on twenty twenty one launch into space. So stay tuned very early days, but Desa might be both global and staedtler. Geographies? So as great example thing so that Paul so do you have some examples from the insurance industry that have been using a I in an interesting in meaningful manner. Yeah. That's a that's a good question. And a one article I recently saw wise Munich agree created a triage engine where in this isn't a obvious use case. This would be like. If you were to sort of predict what the largest reinsurance company in the world would be publicly talking about Email triage was an area they identified is consuming a lot of labor hours and something they thought. Hey, I would be well task that. So they created a triage system to sort incoming Eanet. They were getting somewhere in the neighborhood of hundreds of thousands of emails so year, and they were able to create a system which allocated or made a prediction of who that Email should go to which they claim they're seeing a lot of lifted. Another really good example. I think is this idea of. To give it is like a digital fingerprint for driving behavior in that some companies are experimenting with. Is a combination of IOT. And I they can now predict who is actually driving the car based on driving behavior. So I like to drive a little fast in, you know, take a corner, they'll be able to tell that my wife likes to drive a little bit slower, and you know. Locations. They'll be able to say with a high degree of accuracy. This was Paul driving versus my spouse. Now, we touched upon in. What you think the future of AI you mentioned a little bit by supercharging human Forman's? In your case study was the about the the banks out. Call center is any undesirable. You want to touch upon that. Or how do we out covered it by now? Yeah. I mean, I think we take a perspective that. There's a lot of hype in the space. But I think it also requires a a sober view. Yeah. It not. It's not gonna be all changing, but it's very very powerful at cer- bits. And I think if we can leverage. You know, what what it's very very good at indulge momentum. I think that will be a win. One thing. I think we'd love to communicate to people is the idea of the net. New opportunities that exist Sar the paternity of what's that net. New use cases Catera at so we think of that twenty twelve analogy in combining GPU's in deep learning. I think a lot of organizations haven't even scratched the surface of accessing that type of capability, so if I were to predict what the future of AI looks like I really see it being the mass adoption of that capability in large enterprises. Now, whilst will still in the future kind of mindset where how and where do you see a on blockchain converging who? This is a great question and. Probably will be entirely wrong on this. If we think what Blockchain's very good at. It's very good at transactions. It's very good structuring data. Which I think are two very important components to a, but I think they'll address different parts. Of the insurance value chain in not I think blockchain will have a tremendous impact on at risk capital and knots the idea of how does risk origination to broker to carrier to reinsurance broker to reinsure to retrocession broker to the IRS markets. How does that get bundled a little bit more? And not I think is very very provocative where I think, hey, I will be very good is predicting what the cost of that risk is. And then administering that risk. And I think the closest analogy I would make prediction would be our ability to predict hurricane severity against a portfolio of houses. At was really revolutionary to unlock the insurance linked securities market in that when we can say this class of hurricane hitting Miami. We know it will cost X number of billions of dollars and instead of doing ground up claims payments from the reinsurer to the carrier. We can now say this event hits. Here's a very large. Check you're on your own to to deal with the claims. I think the analogy would be can we create predictions? At a more micro scale in personal lines in commercial lines. In casualty are is. There a way we can tie that risk capital closer to that underlying risk. So I think those are two overlapping ideas that will require both capabilities, but very different in where it's gonna be valuable definitely unexciting feature for for us to keep our eyes on Paul. I'm afraid we run out of time. Someone thank you very much for introducing us to a in. It's convergence was blockchain disrupts this, intra blocks podcast. We hope you've enjoyed this episode. If you like what you've heard this. We don't forget this podcast and live review on itunes love to have you back on the show. So that may happen six or eight months, we can discuss see how our respective technologies have evolved in how hopefully they will continue in converging together. Thanks for having be related. It's been a pleasure.