Amex Global Business Travel: Long Lake CEO Alexander Taubman के साथ दुनिया का पहला AI Take Private

Today Pars, we're joined by Alex Topman, the co-founder and CEO of Long Lake Management. आज No Priors में हमारे साथ हैं Alex Taubman, Long Lake Management के co-founder और CEO। Long Lake recently announced their intent to acquire American Express Global Business Travel for $6.3 billion in what I believe is the world's first AI take private. Long Lake ने हाल ही में American Express Global Business Travel को $6.3 billion में acquire करने का इरादा जाहिर किया, जो मेरे ख्याल में दुनिया का पहला AI take private है। They have previously bought around 30 companies and they transform and optimize them with AI. वे पहले भी करीब 30 companies खरीद चुके हैं और उन्हें AI से transform और optimize करते हैं। So, we're very excited to have him on board today. तो आज उन्हें यहां पाकर हम बहुत excited हैं। Alex, thanks so much for joining us at Enterprise. Alex, No Priors में आने के लिए बहुत-बहुत शुक्रिया। Pleasure to be here. यहां आकर खुशी हुई। Thank you for having me, Lad. Thank you, Elad, आपने बुलाया। Um, you just announced what I believe, and I I could be wrong on this, but I think it may be um the world's first ever AI take private where you've agreed to acquire Ammex Express Global Business Travel, the world's largest corporate travel platform for 6.3 billion, which is pretty amazing. आपने अभी जो announce किया, और मैं गलत भी हो सकता हूं, लेकिन मुझे लगता है यह शायद दुनिया का पहला AI take private है, जहां आप Amex Express Global Business Travel को, दुनिया के सबसे बड़े corporate travel platform को, $6.3 billion में acquire करने का समझौता कर रहे हैं। यह काफी amazing है। And before that, you've already done 30 acquisitions under this premise that you can buy businesses and transform them with AI, what some people are referring to as AIdriven roll-ups or AIdriven um uh buyouts. और इससे पहले, इस premise के तहत आप पहले ही 30 acquisitions कर चुके हैं कि आप businesses खरीद सकते हैं और उन्हें AI से transform कर सकते हैं, जिसे कुछ लोग AI-driven roll-ups या AI-driven buyouts कह रहे हैं। And so I it's very exciting to have you here and learn more about your business. तो यहां आपसे मिलना और आपके business के बारे में और जानना बहुत exciting है। You mentioned you have this Nexus platform which helps your employees serve their customers better and automates a lot of their work. आपने बताया कि आपके पास Nexus platform है जो आपके employees को customers को बेहतर serve करने में मदद करता है और उनके काफी काम को automate करता है। Can you give examples of some of the things that that automates or how it helps them in the context of HOA and I know you're now in four other verticals or three other verticals and so what you do kind of crosses across the different businesses you're involved with. क्या आप कुछ उदाहरण दे सकते हैं कि यह HOA के context में क्या automate करता है, और मुझे पता है आप अब चार या तीन और verticals में भी हैं, तो यह आपके अलग-अलग businesses में कैसे काम करता है? Yeah, that's right. हां, बिल्कुल। So we've taken an approach since the beginning of investing very heavily in our horizontal AI platform which call Nexus. तो शुरू से ही हमने अपने horizontal AI platform, जिसे हम Nexus कहते हैं, में बहुत heavily invest करने का approach लिया है। Um I'd say roughly 80% of the infrastructure is shared across the verticals and then there's a lot of work to take it and deploy it into those end markets and the deployment involves mapping workflows understanding data sources cleaning up data sources integrating with them to make them easier for the models to access and sort of our next platform sits in between the models on one side we're model agnostic and the data sources the skills the workflows of the business and so that takes a lot of customization and significant applied AI engineering capabilities which we've built built at Long Lake. Infrastructure का करीब 80% हिस्सा verticals में share होता है, और फिर उसे उन end markets में deploy करने का काफी काम होता है। Deployment में workflows की mapping, data sources को समझना, उन्हें clean करना, और उनसे integrate करना शामिल है ताकि models उन्हें आसानी से access कर सकें। हमारा Nexus platform एक तरफ models के बीच, जहां हम model-agnostic हैं, और दूसरी तरफ business के data sources, skills और workflows के बीच काम करता है। इसमें काफी customization और significant applied AI engineering capabilities लगती हैं जो हमने Long Lake में build की हैं। So once we can take that platform, we buy a company now or partner with a company now very quickly. तो एक बार जब हम platform तैयार कर लेते हैं, अब हम कोई company खरीदते हैं या किसी company से partnership करते हैं तो बहुत जल्दी काम होता है। You know, in the beginning it took us over a year with our first acquisitions to actually, you know, find the the real potential of AI uh and see it in the in the business outcomes. शुरू में अपनी पहली acquisitions के साथ हमें एक साल से ज्यादा लगता था, AI की real potential को ढूंढने में और उसे business outcomes में देखने में। But now within days of partnering with a company, we can deploy this very quickly and see immediate impact. लेकिन अब किसी company से partnership के कुछ दिनों के भीतर ही हम इसे बहुत जल्दी deploy कर सकते हैं और तुरंत impact देख सकते हैं। So you can buy a company and then within a couple weeks you have instant margin lift because the employees of that new acquisition just go on a platform you've already built for similar businesses. तो आप एक company खरीद सकते हैं और फिर कुछ हफ्तों के भीतर तुरंत margin lift मिलती है क्योंकि उस नई acquisition के employees उसी platform पर चले जाते हैं जो आपने similar businesses के लिए पहले से बना रखा है। Yeah. हां। What we what we see is instant time savings. जो हम देखते हैं वह है: तुरंत time savings। And then the question is how do we grow to give our team members so we actually invest very heavily in growth. और फिर सवाल यह है कि हम कैसे grow करें, अपने team members को ज्यादा दें, इसीलिए हम growth में बहुत heavily invest करते हैं। We're actually not really you know we're not focused on cost saving. हम actually cost saving पर focus नहीं कर रहे। We're actually focused on driving growth and customer experience. हम actually growth और customer experience drive करने पर focused हैं। That's our that's our big and what we've seen it's a much more powerful model because it's our view of AI is it's incredibly positive sum. यही हमारा बड़ा model है, और हमने देखा है कि यह कहीं ज्यादा powerful है क्योंकि AI के बारे में हमारा नज़रिया है कि यह incredibly positive sum है। I know this is a little bit of a narrative violation but we actually think AI makes people more productive and we have more productive people. मुझे पता है यह थोड़ा narrative violation है, लेकिन हम actually मानते हैं कि AI लोगों को ज्यादा productive बनाती है, और जब आपके पास ज्यादा productive लोग हों। You want more of them and when your customers are happier you grow faster. तो आप उनमें से और चाहते हैं, और जब customers ज्यादा खुश होते हैं तो आप तेज़ grow करते हैं। You actually create jobs and everybody wins. आप actually jobs create करते हैं और सब जीतते हैं। And so we're seeing this in our companies. और हम यह अपनी companies में देख रहे हैं। We're fastest growing company in the HOA industry now. हम अब HOA industry में सबसे तेज़ growing company हैं। We are growing organically. हम organically grow कर रहे हैं। We when we invested in the businesses, they're typically growing 0 to 5% a year in terms of volume. जब हमने businesses में invest किया, वे typically volume के हिसाब से 0 से 5% सालाना grow कर रहे थे। We're now growing 20 plus% a year. अब हम 20% से ज्यादा सालाना grow कर रहे हैं। And that's because we've made our team members, we've given them extra capacity to go and serve more customers. और यह इसलिए है क्योंकि हमने अपने team members को, उन्हें extra capacity दी है ताकि वे ज्यादा customers serve कर सकें। We actually have better uh more attractive customer acquisition economics. हमारे पास actually बेहतर customer acquisition economics हैं। Um because we can serve those customers at incrementally lower costs with better products and services. क्योंकि हम उन customers को incrementally कम cost पर बेहतर products और services के साथ serve कर सकते हैं। So, we've been able to take sort of the the software style playbook of go to market and apply it into these sleepy industries and and I think it's a win-winwin. तो हम software-style go-to-market playbook को इन sleepy industries में apply करने में सक्षम हुए हैं, और मुझे लगता है यह win-win-win है। It must be hard for your employees to go and work anywhere else in the industry then because if they're dramatically more productive, they're doing less busy work. तब आपके employees के लिए industry में कहीं और जाना मुश्किल होना चाहिए, क्योंकि अगर वे dramatically ज्यादा productive हैं और कम busy work कर रहे हैं। Have you found that you've decreased employee churn or have other things? क्या आपने देखा है कि employee churn कम हुआ है या कोई और बदलाव आए हैं? That's right. बिल्कुल सही। We've seen very very high retention of our team members across all of our acquisitions. हमने अपनी सभी acquisitions में team members का बहुत ज्यादा retention देखा है। Um, and actually this is the vision. और actually यही vision है। This is the vision long lake. यही Long Lake का vision है। We want to basically be the best place to work in every industry that we operate so we can get give the best people the best tools with the best customers and that flywheel becomes self-perpetuating because if you now leave Long Lake or you leave our one of our partner companies to go to a competitor you have to start doing all this mundane work again that you 25% of your day 30% of your day you have to go do that again and the thought of it's like it's like giving up email or something you know you're not going to it's just so actually we've started to become a real talent magnet in these industries, in our companies. हम basically हर उस industry में सबसे अच्छी जगह बनना चाहते हैं जहां हम काम करते हैं, ताकि सबसे अच्छे लोगों को सबसे अच्छे tools के साथ सबसे अच्छे customers मिल सकें। और यह flywheel self-perpetuating बन जाता है क्योंकि अगर आप अभी Long Lake या हमारी किसी partner company को छोड़कर competitor के पास जाते हैं, तो आपको वो सारा mundane काम फिर से करना पड़ेगा, जो आपके दिन का 25%, 30% हिस्सा था, आपको वो सब फिर से करना होगा। और यह सोचकर ही ऐसा लगता है जैसे email छोड़ना हो। आप नहीं जाएंगे। तो actually हम इन industries में, अपनी companies में एक real talent magnet बनने लगे हैं। And by the way, we can pay people the most because they're the most productive. और वैसे, हम सबसे ज्यादा pay भी कर सकते हैं क्योंकि वे सबसे ज्यादा productive हैं। They're actually making more money. वे actually ज्यादा पैसे कमा रहे हैं। And we're we're delighted about that. और हम इससे बेहद खुश हैं। So, we can pay you the most, give you the best tools, and we're growing the fastest. तो हम आपको सबसे ज्यादा pay कर सकते हैं, सबसे अच्छे tools दे सकते हैं, और हम सबसे तेज़ grow कर रहे हैं। Um, you know, that's part of our vision is it's just it's it's really making things better for team members and customers. और यह हमारे vision का हिस्सा है, team members और customers के लिए चीज़ें सच में बेहतर बनाना। And by the way, the other important thing is you give your team members superpowers with AI, the customers are much much happier. और वैसे, दूसरी important बात यह है कि जब आप अपने team members को AI के साथ superpowers देते हैं, तो customers बहुत-बहुत ज्यादा खुश होते हैं। So, we're seeing customer retention going up. तो हम customer retention को बढ़ते हुए देख रहे हैं। We're seeing response times much faster, Response times बहुत तेज़ हो रहे हैं, errors going down in sort of things like board reporting, budgeting, email, you know, and uh that's driving up customer board reporting, budgeting, email जैसी चीज़ों में errors कम हो रहे हैं, और यह customer satisfaction को ऊपर ले जा रहा है। Mhm. हां। Oh, so cool. वाह, कमाल है। Why why do this v acquisition versus just offering software to people? Acquisition क्यों करना, software लोगों को बेचने की बजाय? In other words, the traditional Silicon Valley playbook would be, you know, you find the HOA industry, you realize that there's a need in terms of software, you build software for the industry, and then you sell it as a vendor and in this case, sell it as a sort of AI product or tool. दूसरे शब्दों में, traditional Silicon Valley playbook यह होती कि आप HOA industry ढूंढते, समझते कि software की ज़रूरत है, उस industry के लिए software बनाते, और फिर उसे vendor के रूप में बेचते, इस case में एक AI product या tool के रूप में। Yeah. हां। um why not do that or why did you decide to go down the acquisition path? तो acquisition path पर क्यों गए? We we think that you can drive better win-win business outcomes with deeper alignment. हमें लगता है कि deeper alignment के साथ बेहतर win-win business outcomes मिल सकते हैं। And so by actually owning the companies and and owning those customer relationships directly, we can drive better better results. तो actually companies को own करने से और उन customer relationships को directly own करने से, हम बेहतर results drive कर सकते हैं। Software companies are wonderful. Software companies बेहतरीन होती हैं। We partner with many of them. हम उनमें से कई के साथ partner हैं। But when you're just selling software and you don't actually then care what happens with the with the business outcomes, you just don't see the same business. लेकिन जब आप सिर्फ software बेच रहे हों और business outcomes के साथ क्या होता है इसकी आपको परवाह न हो, तो आपको वही business results नहीं दिखते। Your engineers are just viewing your employees as their customers in some sense then. तो आपके engineers आपके employees को किसी हद तक अपना customer मानते हैं। Is that correct? क्या यह सही है? That's right. बिल्कुल सही। Our our team views our employees and our team members in the field as the customer and that feedback loop internally. हमारी team हमारे employees और field में काम करने वाले team members को ही customer मानती है, और वह internal feedback loop। That's the other point is we have a much tighter feedback loop. यही दूसरा point है: हमारे पास कहीं ज्यादा tight feedback loop है। So you know the old skunk works thing of you want the engineers and the factory to be colllocated so you can have more innovation. तो आप जानते हैं वह पुराना skunk works वाला concept, कि engineers और factory को collocated रखो ताकि ज्यादा innovation हो सके। That's what we have at Long Lake. यही Long Lake में है। So our team members and our engineers are together in the field all the time. हमारे team members और engineers हमेशा field में साथ रहते हैं। I think there's of our engineering team they're probably in 20 different states right now sitting with team members across our architecture business across our HOA business across our HR services or you know specialty tax business and so they're sort of um and there's a deep amount of change management that's involved so this is a lot of you know sitting with the team members understanding their pain points and so there's a real like solutions orientation of how do we take the pain point we and then we build a tool within Nexus to solve it and that feedback loop is really important. मुझे लगता है हमारी engineering team अभी 20 अलग-अलग states में है, हमारी architecture business में, HOA business में, HR services में, specialty tax business में, team members के साथ बैठे हुए। और इसमें काफी change management शामिल है। तो यह बहुत कुछ team members के साथ बैठने और उनके pain points समझने के बारे में है। एक real solutions orientation है, pain point लेते हैं और फिर Nexus के भीतर उसे solve करने के लिए tool बनाते हैं। और यह feedback loop वाकई important है। So you get to better outcomes this way. तो इस तरह आपको बेहतर outcomes मिलते हैं। No, it's pretty amazing because I think one of the biggest issues for actually adoption of AI is uh change management uh changing processes, changing organizational design. यह काफी amazing है क्योंकि मुझे लगता है AI adoption की सबसे बड़ी समस्या है change management, processes बदलना, organizational design बदलना। And I guess if you own the actual company, then you can make those changes. और मुझे लगता है अगर आप actual company own करते हैं, तो आप वो changes कर सकते हैं। If you're just selling software, you can't really impact it very much. अगर आप सिर्फ software बेच रहे हैं, तो आप उस पर ज्यादा impact नहीं डाल सकते। I think in general, um in order to do this very well, and I've talked to dozens of people trying to do different forms of AI rollups and, uh things like that, um is you really need three competencies. मुझे लगता है कि यह बहुत अच्छे से करने के लिए, और मैंने AI rollups और ऐसी चीज़ें करने की कोशिश करने वाले दर्जनों लोगों से बात की है, तीन competencies की ज़रूरत है। It seems like it seems like you need some folks who are great at the the private equity style motion of purchasing things. ऐसा लगता है कि आपको ऐसे लोगों की ज़रूरत है जो private equity style motion में, चीज़ें खरीदने में, बेहतरीन हों। You need somebody who's great at engineering and building out the uh AI uh stack and then you need really good change management. किसी ऐसे की ज़रूरत है जो engineering में और AI stack build करने में बेहतरीन हो, और फिर आपको सच में अच्छा change management चाहिए। How how were you able to pull those three disciplines together? आप इन तीनों disciplines को एक साथ कैसे ला पाए? Because it's very rare and again I've seen very very few very very few companies in this area who've done this. यह बहुत rare है और मैंने इस area में बहुत कम, बहुत ही कम, companies देखी हैं जिन्होंने यह किया है। Was it a magic initial founding team? क्या यह एक magical initial founding team थी? Was it just how you've hired? या फिर यह आपकी hiring का तरीका था? I'm just sort of curious how you because you've also gotten exceptional engineers which most you know uh folks aren't able to get in this industry. मुझे बस जानना है कि आपने यह कैसे किया, क्योंकि आपने exceptional engineers भी attract किए हैं जो इस industry में ज्यादातर लोग नहीं कर पाते। Yeah. हां। Well, thank you for saying that. शुक्रिया आपने यह कहा। Yeah. हां। So, because we were purpose-built from day one to kind of be this uh cross functional company with technology, DNA, change management and M&A, we were able to attract, you know, the right type of people from network. तो क्योंकि हमें day one से ही purpose-built किया गया था, technology DNA, change management और M&A वाली इस cross-functional company के रूप में, हम network से सही type के लोगों को attract कर पाए। And so, I think 100% of our first 20 people were through network. और मुझे लगता है हमारे पहले 20 लोगों में से 100% network से थे। We knew them really well. हम उन्हें बहुत अच्छे से जानते थे। and people from, you know, places like Palunteer, RAMP, Robin Hood, you know, um some of the top Glean, some of the top um sort of modern AI and data uh companies. और Palantir, Ramp, Robinhood, Glean जैसी top modern AI और data companies से लोग आए। And so, you know, Rasmus, our our co-founder and CTO, uh he and I were connected through one of our, you know, early investors and board members who've we've all known each other for kind of 15 plus years. तो Rasmus, हमारे co-founder और CTO, उनसे और मेरी connection हमारे एक early investor और board member के through हुई जिन्हें हम सब 15 plus साल से जानते हैं। um we all started our careers together and it's हमने सब अपनी careers साथ शुरू की थीं, और यह वाकई rare है। really rare by the way I think for many business people to have those deep technical networks like what I've observed is that often these are separate worlds and technologists are very bad at hiring business people early on at least in their careers and vice versa business people tend to be awful at hiring engineers and so you end up with these mismatches on the early teams so it's pretty amazing you were able to pull people out of some of these great companies मुझे लगता है बहुत सारे business people के लिए ऐसे deep technical networks होना rare है। मैंने observe किया है कि अक्सर ये अलग-अलग worlds होते हैं, technologists early career में business people hire करने में बुरे होते हैं, और vice versa, business people engineers hire करने में बुरे होते हैं। तो early teams पर ये mismatches हो जाते हैं। तो यह काफी amazing है कि आप इन great companies से लोगों को निकाल पाए। yeah well thank you हां, शुक्रिया। I mean I think it's a re what it's a really exciting project मुझे लगता है यह वाकई एक exciting project है। I think that's you know the idea of bringing AI into the real world there's a lot I mean यह idea कि AI को real world में लाएं, इसमें बहुत कुछ है। What the labs are doing is extraordinary obviously and they're enabling all of this and all the you know hundreds of billions of investment that's going in models are getting part of our thesis was the models are going to get better every day. Labs जो कर रहे हैं वह extraordinary है और वे यह सब enable कर रहे हैं, और सैकड़ों billions के investment जो models में जा रहे हैं। हमारी thesis का हिस्सा यह था कि models हर दिन बेहतर होते जाएंगे। There's going to be a tremendous amount in the trillions of investment Trillions में investment आने वाला है।