Why Partner Leaders Must Master the AI Landscape to Win Hyperscaler Partnerships with Alan Chhabra

0:00Data no longer is the center of the landscape for selling tech. And I was trying to figure out what was the center. And I go back to the analogy of the gold rush, right? Long time ago, when people were traveling out West, they were looking for gold. Was gold the center of that era? or were the people that were selling the pans and the shovels, you know, the whatever, the axes to hit the... Pigs. Right, the pigs and axes. Were they the center? And I'm like, you know what? That's the center.

0:39Alan Chhabra: Welcome to the Revenue Builders Podcast, a weekly show featuring B2B sales leaders and executives. Hosted by five-time CRO John McMahon and Force Management co-founder John Kaplan, the show takes guests in the barrel, behind the scenes with the people who've been there, done that, and seen the results. Revenue Builders covers best practices for scaling and growing your business while sharing the pitfalls to avoid. Welcome back to the Revenue Builders podcast. Today we're talking about one of the most critical and definitely often most misunderstood levers for hypergrowth, scaling strategic partner ecosystems.

1:19And joining us today is Alan Chauber. He's the executive vice president of worldwide partnerships at Cerebra Systems. Alan spent over two decades operating at the center of enterprise tech, direct sales, and global channel strategy. Over a nearly 10-year run at MongoDB, he scaled their global partner network from the ground up, turning hyperscaler alliances and partner channels into a revenue engine that touched, you ready for this, 80 % of all new business deals. Prior to MongoDB, he ran a$200 million business at BMC Software. Alan holds both a bachelor's and a master's in engineering from MIT.

2:01He's now at Cerebus Systems, the company building the world's fastest AI supercomputing and inference platforms, and he leads their global partner expansion as Gen.AI redefines the enterprise tech stack. So whether you're trying to align direct sales with partners, build hyperscaler relationships, or navigate revenue leadership in the AI wave, Alan has done it at the highest level. Welcome, Alan Chabra. Alan, welcome back to the podcast. How are you today? I'm a great boss. I got two bosses on. I'm enjoying it already. There you go. Okay, so you left MongoDB. You went to Cerebris. Tell us a little bit about it.

2:46He didn't leave MongoDB, dude. He spread it to Cerebris. Okay. All right, you're at Cerebris. Is that fair? Let me put it into a different context. You're at Cerebris. And tell us about, give everybody a little overview on Cerebris. And congratulations on the IPO. Thank you. And tell us a little bit about your responsibility there. And then I'd like to focus on how the landscape and AI has changed for the role that you you have at Cerebris. Yeah, I mean, first of all, thanks for having me on. And it's my second time. So hopefully the first time I did a decent job or you wouldn't have brought me back.

3:25So thank you. I mean, I spent 10 years at MongoDB, as you know. and then prior to that John I got to learn from you in particular at BMC Software and Blade Logic and Mongo was a phenomenal run I got to learn from people like David Tucharia, Chip Hazard, Cedric Pesch and when I was close to you know nine years I started building a succession plan like all leaders should, maybe even earlier than that. And when I hit 10, you know, I wanted to take some time to do something new. And I asked Dave and set his permission and they, they granted it. Now, I remember you, you and others said, Hey, you know, take some time to really consider what you were going to do and jump into.

4:17So I was planning not to go back that quickly. But then, man, I ran into Andrew Feldman at Cerebrus, and I'm like, this timing is perfect. I remember that. We talked about you taking some time, and then two weeks later, I made the announcement that you're working at Cerebrus. But John, I do listen to you, okay? Because you once told me. I'm not saying you made a bad choice. You have to listen to me. The choice has been a good one. I guess you remember we talked about it. That's all. But there was a piece. I'm going to unwind that. there's a piece of a conversation that has stuck with me from that day and I have told others and it's about your kids okay whatever you do in your career maybe when you're younger it's harder because you're still grinding and you're trying to put food on the table well when you're a little bit older and you've accomplished things and you have kids you said when you have a child for the first 18 years of their lives, you see them spend time with them.

5:15They're with you 365 days a year. But after 18, you may only get 365 days of their lives to spend time with you after that, because they go to college and they have partners and they get married and they have jobs and they travel. So I have a 18 year old and a 13 year old. At the time, my son was 17. So I said, to my wife, Shelly, whatever I do next, I'm going to make sure I don't miss that those hours, those days. So when I took the job at Cerebris, Andrew and I discussed, and we agreed that I wouldn't take a CRO job, CRO job, you're going to have to travel around the world. We would split the job into two.

6:03So I still could do what I wanted to do, which was spend time with my kids and help them and be them and help them get into college and go to their games. And I'll help Andrew find a head of sales, which you had on a previous talk. Alex Verrill, very good friend of mine, Exumon, going to be XZscaler. We decided to split the job into two where both of us work for Andrew, where Alex gets to do what he does very well. And I get to do what I do well on the partner side. So I did listen to you, except I did take the job two weeks later. no it is a scary step for people out there that have young kids I mean it is a stat that you get 365 days a year with them when they're up until 18 and after that you're lucky if you get an aggregate of one year in the rest of their lives you know so and I want them to remember the time we had it wasn't just being like driving them to the mall or something Like we've gone to World Cup games together.

7:10We traveled to Europe together. And I want him to remember those moments so that when he comes back, we can do that. And hopefully one day when he does it, when he has kids, he remembers to do the same thing. I get it. It's hard to do when you're between 25 and 35 because you're grinding. You're trying to like put food on the table. You're trying to buy the house. You're trying to get a second car. But once I feel like you've made enough, you really got to reset a little bit and not just keep grinding because you could lose the purpose, which I think is family. So I listen to you. I listen to both you guys about that.

7:47Alan, was there anything? Was there such a great topic? We just came off another podcast dealing with something similar. Is there anything when you look back on it, you're fortunate enough to grind and get an outcome, get some great outcomes from the grind? and then be able to make some decisions. If you had to do it over again, would there have been some simple things or simpler things that you could have done during the grind to, you know, maybe somebody doesn't get the chance to do that afterwards. Somebody passes away or, you know, we just hear all kinds of different stories. Is there some things that you talking to somebody else may be giving some advice?

8:29Can you insert anything? We're not expecting you to say to the grind, don't do the grind. But is there some things that you could insert into the grind that you would do differently? I mean, 100%. Now, I think I may have started this a little bit too late. But one thing that I'm not ashamed to say is it's okay to ask for help, whether it's from a therapist, coach, family, wedding, marriage counselor, people like yourselves, both you, people like Dave who are maybe have already gone through that and ask them and be vulnerable and talk about it. I think when I was in my 30s, I thought I'm just pounding my chest and like, yeah, I just got to go close as many deals as possible.

9:18I'll be on a plane. If I've only slept one hour, like it's all right, I'll power through it. It's okay to have that extra scotch, you know, at the restaurant. But I think when I started to like maybe have some problems with the wife and some of the kids, I took my stress out on the family. I think it was an awakening moment. So I think anyone who's going through it, it's common. It's okay to ask for help. And it's don't be ashamed to get professionals to be involved, especially for marriage or for just your own. Like, you know, if you're a salesperson, get a coach, ask a mentor. I mean, this podcast is a great example.

9:57Like you guys bring in all these experts. I'm sure all of them have gone through the same things that we have. And it's just to share. Now, this is a elite sport. Okay. Like if you're not going to excel, you're probably not going to last. So every athlete I know that's an elite athlete has a support cast. You need to build that around you. You're not, if you just go it alone, which I did between probably 30 and 40, and I just got angry at people because I was stressed or overworked, you're not going to be successful. So find that support cast. You got to find it. It's not, don't be ashamed to say, and if you build a good one, you'll probably excel even better and you'll save time.

10:44Instead of working 80 hours a week, you probably could be as productive working 50 hours a week or four. Thanks for sharing that, buddy. Yeah, thank you. Let's switch gears and talk about your new role. So when you're at MongoDB, you had all the partners, hyperscalers, ISVs, resellers, consulting firms, all of it. And now the world's changed. You're at Cerebris. You're selling an AI chip, for lack of better. This baby right here. There it is. Talk about how your role in this new world that you're in has changed from where you were at MongoDB and your huge responsibility that you had there. I think the waves, it's interesting, the waves of the industry that we're in, in tech, how like in the 2000s, the hot thing was storage and virtual computing and the beginning of cloud.

11:50And then in the mid-2015s, the data wave, like data was the center, whether it was observability or database like Mongo or data warehouse, like the snowflakes of the world. And then you come across in the 2020 range between where we are today, the advent of machine learning and then the creation of chat, GBT. like when that came out, it just flipped things on the head. And what really has changed is the stack, the landscape. All right. When I was at Mongo, I think you brought me in. Either you or Dave. I'm going to say you brought me in. Dave's there. I'm going to say Dave brought me in. When you guys brought me in.

12:34Well, he hired you, but I brought you in. I know you did. I know you did. I didn't really understand the open source database world, but I knew that's where the center of the landscape was. So when I joined, we built a partner's program that would get other people to sell what we were selling to help our Salesforce. And at that point, the hyperscalers were really important and system integrated. Hyperscalers wanted to offer open source database as a service. System integrators wanted to write applications or modernize legacy applications. All right. So that was the whole thesis. And we did a pretty good job at that.

13:15By the time, at least when I left, at least 50 percent of the revenue that MongoDB had went through a cloud marketplace. And I think 90 percent was touched by partners. So the company, obviously, it's over$2 billion in revenue now, phenomenal company. When I decided to leave and then got this opportunity at Cerebris, what my thinking was, the world is changing. Data no longer is the center of the landscape for selling tech. And I was trying to figure out what was the center. And I go back to the analogy of the gold rush, right? A long time ago when people were traveling out west, they were looking for gold.

14:00Was gold the center of that era? Or were the people that were selling the pans and the shovels and whatever, the axes to hit the picks and axes, were they the center? And I'm like, you know what? That's the center. They made money either way. They gave customer value either way. So I thought about two years ago that NVIDIA was the center and that anyone who could create an alternative architecture would be viable. So that was my thinking two years ago. And I got lucky to find Andrew at Cerebris. Now, when I joined the company, it was definitely a little bit nerve wracking, right? Like to compete with NVIDIA is no small task.

14:51it's actually probably the biggest mountain that's out there because they're so good at what they do they have such a large volume of chips in the industry everything from gaming computers to now you know some of the best chips for ai so in order to tackle that we had to bring in alex for sales uh which which i convinced andrew to do and then you have me for partners In order to go solve the partner side, we had to get into a hyperscaler. Okay. NVIDIA's five largest customers, at least four of the five, are hyperscalers. They're the ones buying them. Cerebus at the time, we had a great customer, G42, which is a smaller kind of NeoCloud, but we were not in AWS, Azure, or Google.

15:40We were not. We were not in Meta either. So, okay, the easy thing is, Alan, just go. you build a team, work with Andrew, get us in the hyperscaler. We have a phenomenal board. You've spoken to some of them like Lior, Susan, Steve, and Eric Vischer. These guys are phenomenal. But it wasn't that easy. So I had to figure out the landscape first. So what people don't realize in AI is the landscape is completely different than what it was in 2015 team with data. The landscape for AI, if you think of a pyramid, at the top is the consumer. We're at the top. We're using AI every day. But there are consumers that are willing to use AI at home.

16:24There are consumers that are enterprise, large enterprises, banks, pharmaceuticals, automotives. There are consumers who care about sovereign. They need their data to stay in their country. And it's really important that we bring that up. So the top of the pyramid, you have the consumers, us, people, but there are different types. At the bottom of the pyramid are actually countries and land. Countries and land, the United States, the UAE, KSA, obviously in Asia, China, Europe. That is literally the foundation of the stack. So we're In order to crack into hyperscale, you have to understand all the rows of the triangle.

17:13Would you put them there because of the regulation? Or what makes them the foundation of the stack? The land, they have access to create easy ways to get power and water. Power, yeah. And then minerals, okay, which are all needed for the second row. So the second row is creation of power. different types of power nuclear uh solar wind obviously uh you know um fossil fuels okay then you have the creation of things you need for computing a water obviously access to water you need that for new types of uh compute and then minerals to create for example silicon okay so so you have to understand like countries and land and then you have to get access to power water minerals to create the next row, which TSMC has done a phenomenal job, the fabs, the people that are going to create silicon chips for the industry to run AI.

18:19So TSMC, I would argue, is the dominant player in that third row. Then you go one up above that, and you get to the juggernaut, which is NVIDIA. All right. So NVIDIA is creating all types of chips, but GPUs are the ones that are most popular for AI. And then you have the people that are competing with NVIDIA. You have Cerebris. You have Meta creating their own AI chip. You have the hyperscalers, whether it's Tranium for AWS or Maya for Microsoft or TPUs, TPCs for Google. And NVIDIA is the dominant player there. And they've also acquired, NVIDIA acquired someone that we used to compete with in Grok, and there's a company called SambaNova.

19:07But Cerebris plays there, all right? So then these chip providers are building infrastructure that then can be used along with the power and the water and the minerals to create data centers. That's the next role of the stack. So some of the largest data center builders are like Holder or Turner. And these are companies building data centers at scale or potentially retrofitting existing data centers. Because I keep bringing up cooling like water, like old ways of handling AI computing doesn't work anymore. Air conditioning is not the way most AI computing will be cooled in the future. It'll be through liquid or water.

19:54So data centers have to be built to handle that. That's why you hear stories about what's going to be the most popular job in the future. The plumbers could be the most popular, right? Because you got to run all that piping for the water. Or you have to retrofit existing data centers. Then we get to the hyperscalers. They're not the bottom. So John McMahon, when we were at Mongo, the hyperscaler was the bottom. At the bottom of the stack, it was the hyperscaler. Hyperscaler are now one, two, three, four, five levels up. Okay, because hyperscalers will contract third parties to build out data centers.

20:35They will retrofit their existing and they have AWS Azure Google has data centers all over the world. Then you also have colo providers. Colos are actually people thought they were all going to go out of business. As soon as AI hit, they're now peaking in demand because you like the core weaves. The core, well, the core waves would be the third, John. That's the Neo clouds. Okay, you got the hyperscalers in that row hyperscalers colos, which is just I'll give you space. You could be a bank, you could be a Bitcoin mining company, or you could be someone who wants to create a cloud, which would be the Neo clouds.

21:16okay the core weave the nebius cerebrus has our own cloud too so we are not only a chip provider we have our own cloud and so we're so we fit in the chips that area we fit in the neo cloud area and we are now in ada we are going to be in aws the contracts are signed so we'll be in a hyperscaler as well but then you're like okay alan you haven't brought up open ai or anthropic or Met or any of these guys yet. They're the next level. They're not at the bottom. They're a huge component. But the people that are building models every day, obviously OpenA and Anthropic are the biggest and probably most popular.

22:01But then you have the open source models, whether it's from Meta or from the Chinese, right? You have the Alibaba models. And then you have smaller like US companies like RC that are writing models like these open source models. So that I think people gravitate to, well, AI is about the models. You got to understand the full stack before you can figure out the partnerships. And then it doesn't end. Okay, above the models, the models are served in the hyperscalers on the chips through the power and cooling and minerals that TSMC is also creating chips. And then you have with the countries. Above the models, you have ISVs, software companies that are using AI to get ahead.

22:50And one of the most popular types of software companies are on AI is code generation companies. OK, so these companies like Poolside or Cognition or Cursor, they have taken AI models and created code generation platforms. So developers, which we have so many in the world at the top, can use the AI easier. Other types of ISVs are agentic, like they're writing, they're building agents. So instead of having to go to a lawyer, you can go to a software company that has lawyer agents and that can analyze your documents for you. So you have this whole layer of software companies that are coming. And then that's where the companies like MongoDB and Datadog are also injecting AI into their stack to make them more relevant in that layer.

23:46And then only then do you have system integrators. Okay. I used to have system integrators way at the bottom of the stack where I would be all over them earlier along with the hyperscalers. but they're now even higher in the triangle, the pyramid, because they're the ones that are helping customers write applications, develop applications with AI. But in many cases, they're also becoming less relevant because AI is replacing them. So there is a little bit of a conflict there. And then finally, you get to the customer, us. Enterprise customers, ISV customers, B2C customers, customers sovereign customers who want that part of the pyramid in their own country which brings us back to the bottom so in order to crack into the hyperscaler at cerebrus i first had to like kind of figure this out with andrew and then if you figure out the landscape and i recommend anyone who's a partner leader in ai you need to figure out your own landscape only then can you go crack into one of the ranks of the pyramid.

24:52Holy smokes, that was a master class, bro. Yeah, very well done. Very well done. Where do you have to spend your time right now? Okay, so the good news is within the company, we've got a great COO, Dirich, who's working on building out our own NeoCloud. All right, so we have our own cloud. We're contracting data center space with the colos. We're building out our own space. We're using telco data centers to build out our own cloud. My job in particular is to get into the hyperscaler so that they can use Cerebris in their own cloud to even build more capacity for Cerebris around the world. Remember, why do people want Cerebris?

25:41Okay, I didn't even go through that. They want fast inference. In the old days of AI, people were making money in AI because they were selling training of GPUs, training of models on GPUs. Training alone, there's little ROI because no one's actually going to use the model until you get inference. You ask it questions. And today what you've seen is people are asking AI models so many questions. They're asking in the form of not just text or pictures or PDFs, but also with voice. the interaction with AI has to be millisecond response. You need to have ultra fast AI. So this chip, the way it was architected, it is in many cases, 10 times faster AI on this than on anything on GPUs.

26:32So if you're a hyperscaler and you're trying to win more customers over your competition in AI, why wouldn't you want to bring in Cerebris? because we can give you 10 times faster performance for inference. So I spend 99 % of my time trying to get in as deep as possible into the hyperscalers. Can I ask you a question? NVIDIA has five big customers. I want those same five customers as well. Can I ask you a question about the politics of that statement? making a sound technical decision is not as easy as saying who's the best at this. It's because of the way these relationships are built and because of the way this stack was built, how do you run into that?

27:28Like, well, we'd love to use yours, your chip, but we started and they're a big customer of ours and they're on our receivables for this and we're on their receivables for that. How have you navigated all this? Because it's not just the best technology wins, correct? Or is it not? 100%. So this is where I think some of the lessons from MongoDB come into play. So at MongoDB, we had a challenge with the hyperscalers. At the time, for folks who are listening who are in the data space in the 2015 to 20 area, they'll know what I'm talking about. For folks that weren't, it's okay. But the hyperscalers were offering their own open source data as a service.

28:17And they were bringing in all the open source players like MongoDB and Elastic and Redis and like things like Databricks, like Apache Spark. So we had to convince the hyperscalers, do not use our open source alone. Partner with us and sell our enterprise versions. And it was like a lot of creating FOMO amongst each other. Like, hey, you could have our entire sales teams backing you or we could compete with you. So which hyperscaler wants to get with us first? And at the time we got Google first at MongoDB. And then quickly when we were competing with Google with the other guys, AWS was like, yeah, we want in.

29:02And then we did a massive partnership with them. And then Microsoft as well. So same thing here, right? So if I unwind your question, cracking the hyperscalers is extremely political. First, they have deep relationships with NVIDIA, deep. And they don't want to upset the supply chain they have with NVIDIA. Second, they're not stupid. They're creating their own chips. Like I mentioned before, they have their own AI chips. They want to be more relevant in the stack. Not only are they just cloud providers, they're writing their own language models, even though they're partnering with Anthropic and OpenAI.

29:42They're creating their own models and they're developing their own chips. So AWS has Tranium, Microsoft has Maya, and Google has their TPUs. And like, so number one, they have deep relationships with NVIDIA, and they don't want to upset NVIDIAs because if they somehow their supply gets disrupted, that's a big problem. Second, when you say that, because there's a shortage and if NVIDIA says, okay, well, we're going to put you on the slow boat. Yeah. It's a lot about supply and demand right now, right? NVIDIA is not perfect. I mean, NVIDIA has the same, like they can't make enough of what they want.

30:16Like no one can, right? So you have to kind of understand the politics of there. So they want to keep NVIDIA as a good partner. They're not stupid. They're building their own chips. And then you have this competitive advantage, Cerebris, that comes into the fold. So what we decided to do with AWS is come up, and this is a lot of like kind of what going back to MongoDB, where we came up with a joint offering where they still were selling Mongo, but we were working on it as an OEM platform. Our sellers were compensated to work with them. Their sellers were compensated to work with us. In this model, we use technology a little bit differently where when you go to AWS in the future and want Cerebris, the architecture is a combination of Tranium plus Cerebris.

31:10tranium for input of tokens cerebrus for output of tokens tranium for a pre-fill asking it you know pre-fill and uh cerebrus for the decode the answers and that architecture still is just as fast it leverages aws's own ip which is tranium and it's probably even better for price performance So when I work with these phenomenal leaders at AWS, like Peter DeSantis and Dave Brown and Rubel Borno and Posada runs Data Center, they love that architecture, right? Because it is using their own IP as well as ours. It's competitive to GPUs and video, but they're not just like bringing in another third party.

32:05were almost working in tandem on the architecture. So it worked. So AWS signed a big deal with us and we're rolling that out. That's all public this year. So we had to understand the politics around it. You can't just like walk in and be like, hey, I have a faster widget. Why don't you use it? That's hard to do at AWS. You really have to kind of come up with the angle that they're going to buy into. That's a win for them long-term. And what does that do to you as being a expert on how these channels work. First of all, the masterclass that you just gave on the stack is amazing. I can't imagine that anybody could be in a role like yours and not being able to articulate that.

32:47Like that's a must because you just put the stack in there, but now you're putting the relationships in there and you're putting the money in there. And some things that don't make sense, like the models, the frontier models that made all the money in the beginning and they have all this influence and all this money and they're buying their way into this. I don't want to be disparaging, but they're influencing. A lot of them have their own VC funds that are sending money up the channel, right? Yes. So what does it really take today to now kind of explain your role? Like the definition of your role is so much is like it's like a superhuman.

33:29Seriously. That's right. John McMahon, he'll tell you I'm not superhuman. But I do. But he always has been a master. Like even going back to MongoDB for the audience, you know, AWS, for instance, had a competitive product. And they wanted to come right after MongoDB. And he went and did a big deal with AWS where they were selling MongoDB. So he, Alan does go into these sales situations very strategically. strategically. He doesn't just show up and throw up. He's knowing exactly the angle he's going to take when he goes in the door. It's the hashtag of coopetition. You have to be a student. You started this conversation with landscapes, and I think that's so appropriate.

34:15I think if you don't understand, number one, the landscape that you're in, and you don't understand your customers and your partner or the landscape that each, the lens that each one of these layers has, there's no way that you can be successful. So it's not - Think about how the enemy inside the account, right? So you're talking about you going and selling your product into, let's say, the hyperscale. And the hyperscaler has a product also. So someone inside that hyperscaler is essentially your enemy. And they'll politically try to do anything to kill you off, to not allow you into their company, right?

34:54So you're going up against that. Talk about having to get strong champions and how to neutralize enemies. There's a masterclass right there. Well, your champion in one situation could be you're an enemy in another, and that's just reality, right? And an enemy doesn't mean wailing and gnashing of teeth. It's just not in their best interest to do X with you, but is in their best interest to do Y. Correct, Alan? I agree. I mean, so I think if you're a CEO of a AI company that is coming out and you're trying to, every CEO is going to be like, yeah, I need a partner leader. Okay. It's like a checkbox.

35:35You need a sales leader. You need a partner leader. Obviously, you need all the engineering folks. Like that's the core. But then you need these two other people. Okay. And you need a CMO and a marketing leader. If I'm a CEO of an AI company and you come across a candidate, the candidate you should first ask, tell me about the landscape of my business. And if they can't answer what we went through, they haven't done their homework. Or if you're like a salesperson, you haven't done your research in the account. So I think it's so important before you just like jump in and do a bunch of activity and do a bunch of pipeline generation and set up a bunch of meetings for your CEO at a conference.

36:19Any candidate and CEO of an AI company that's going to hire for partners needs to first show them the landscape. Ask them the interview question. What's the landscape of the of all the potential partners we could have and why? Relevant to us. the second test I would give anyone give me the org chart not like all 20 ,000 people but at the top level show me the org chart of the number one partner you're going to work with at our company and if that's AWS show it to me so I do that all the time I mean Andrew did that with me right so when I joined Cerebus hey actually I don't know if he asked me but I just showed it to them.

37:04Let me show you the power base of these brilliant people at AWS. And what you realize is it's like Game of Thrones. There's five kingdoms, six kingdoms. Some kingdoms work for the parent, for Amazon, all the way up to Andy Jassy, and others work for the subsidiary, AWS, up to Matt Garman. And within AWS, there's different components as well, right? There's great leaders like Dave Brown, like Prasad, like Swami, like Ruba, like Greg Pearson. So it's not about being superhuman, but I do respect that, what John's saying, like we had to learn a lot at Mongo the hard way, but it's about doing your research and all these - You also have to have political capacity, meaning you can't look at one deal or one relationship.

38:05Yeah, if you look at one deal by itself, you never get it. So what John didn't tell you, at Mongo, one of the ways we cracked in the hyperscalers was not just partnering with their competitor. We partnered with Alibaba in China. So both you guys know we had a great leader who worked for me, Steve Su in China. So I was on a plane to Beijing, Guangzhou, Shenzhen. I think I went like seven times while I was there. And we cracked into Alibaba. And the last thing AWS or Azure wanted was MongoDB to have a great partnership with the Chinese before we had a good one with them too. So same thing in this new world, right?

38:53When we crocked in AWS, we embraced Tranium. Tranium plus Cerebrus to compete with NVIDIA. A lot of neutralization of enemies if you can do something like that. Well, that's also finding the champion, the champion for their chip, right? Agreed. And helping that person sell more. Yes, I agree. But now we're talking about like, just at a high level, if you wouldn't mind, Alan, because I get a lot of questions like we at force management are dealing with it right now. Just at a high level. What are some of the things that you would look at a success profile? You said being able to name the landscape, not only where you come from, but where you're going to.

39:37What are some other competencies that you would say are non-negotiable for a role like this? I think first, see, AI companies changed a little bit. Most AI companies are heavy engineering, heavy, heavy engineering. Silicon Valley. So I hate to say it, but I'm going to say it proudly. If you are a candidate that doesn't understand technology in this landscape, they should be disqualified right away. You don't have to have two engineering degrees. You don't have to have work for MongoDB, but you need to be smart technically. Anyone can go, like you were saying earlier, can go play around with AI at home or use OpenAI all day and some of the advanced features.

40:35So the non-negotiable thing, especially with the landscape of AI CEOs, deep engineering CEOs, deep founders, you better have some technical background. You could be a mechanic. Hey, I work on cars all day. Or hey, my father-in-law has an HVAC company and I helped him for a bunch of summers. You know what? That's actually relevant. You understand piping, cooling, water. But that is the number one. I hate to say it because we used to be like, hey, we could teach some of that as long as they were smart IQ. But because the DNA of these AI companies is so engineering, if you come in without some...

41:19Their resting state. It's their natural resting state is an engineering conversation. You'll be considered the stepchild. Okay. So that's number one. All right. That's the first. The second thing is, you know, it's not, it's like, you don't have to know every, like, oh, I have a great Rolodex, but you better have, understand the landscape. Like we spoke about the org charts, who are the players, who are the players you can pin against each other from a FOMO perspective. So that's obviously comes from IQ and smarts. But a CEO can easily ask someone on a call. If they just name drop four people, anybody can do that.

41:55But if they go through the exercise we went through, and they showed the org chart, which I always show on when I used to interview for jobs, and then when you, you know, show the pyramid of the different power players, that's number two, non-negotiable. Number three, and this I truly believe, has that leader ever pipeline generated in the last 10 years? And people were like, Alan, you pipeline generate? Of course. I'm trying to get meetings for my CEO with some of the most powerful people in the world in tech every day. and I don't just like say, hey, some of the guys on my team, you go do it.

42:38I do it. I do it through LinkedIn. I get yesterday, I found a leader's cell phone number. You don't want to know how I found it, but I found it and I started texting him, hey, we need to talk about the proposal on the table and I need to get you, me and my CEO on a phone. So if they're not willing to do that, how would they be able to teach a team under them to do it? Or not willing, but able. Able, able. There's a skill to it. AI makes it a little bit easier, but you've got to be able to want to do it and teach others. This is a highly technical mind, a highly technical brain that also has a high capacity for understanding, qualification, value creation, landscape, political landscapes.

43:29That's a pretty rare bird. I'll give you the fourth. They need to be a negotiation killer. Okay, this is not for the weak minded. They need to understand it's okay to pin people against each other as long as you're doing it fairly. It's okay to ask for more. It's okay to be the bad guy. I think as a partner leader the top partner leader in an organization in many cases needs to be the bad guy in the company not the one that does all the fireside chats and goes on the keynotes they got to be the one that's willing to stand up to protect the company the best compliment I ever had is some partner told Dave our old CEO at MongoDB, please keep Alan out of our meetings.

44:21Consigliari or whatever they call it. A mob. Every time he comes into the meeting, he makes us do something crazy. And I'm like, so that was, so like sometimes a partner leader needs to be the one that is not the most, they have to be respected, but they have to ask for the things that no one else is willing to. Now, I think you definitely, the first thing isn't hard to find. The first one, but you got to factor in, They got to have some technical background to be able to stand up to folks in an engineering company in Silicon Valley. I think the second, third and fourth are the harder things to find, but they're out there.

45:01They're 100 percent out there. And I mean, I'm I'm a I'm a friend of force management. Anyone can ping me or through either of you. And I'm happy to recommend folks because they are out there. and some of them maybe have only done it for four or five years but they're hungry and they're willing to to work hard and i think ceos just got to think about it smartly don't just get the the someone who has a fancy title at a company for 20 years or has been in the partner program for years doing you know doing a great job but it doesn't mean that that's the same game as what it is you know what it is today so that was not the same game master class number two thank you brother.

45:42That was amazing. Well, thank you. Well, it's also have they opened up an account, right? So there's plenty of people in the partner programs that are just relationship people in accounts that someone else opened up years ago, and they just maintain relationships. They never really opened up a door before. Yeah, I mean, they're just kind of harvesting. Yes. Now, the good news in the AI space is most of these partnerships are not open. But the bad news is, to your point, it's hard to find people that want, okay, if you've opened up some tough partnerships in the past, it's a grind and it can wear on you.

46:21So you got to kind of find people in their career that want to do it again, right? They're not just like coasting. I personally find it exciting to open something and I get a little bit bored when it's, you know, kind of sailing smoothly. So you just got to find the right, I mean, a lot of salespeople, I don't know if John thinks so, but I used to be mostly sales and I said, mostly services and then sales and then partners. Cause Dave told me I had to run partners and I was like, fine, I'll do whatever you tell me, but I loved it. I enjoyed it. So you just got to find the right people with the DNA that, that we have here on the call.

46:58We also told you the attention of the rest of the sales force with that WD 40 presentation. Oh, yeah. Well, yeah. You have to have good coaching when you do your keynotes. So I don't do, if John said, I don't do keynotes like publicly, like to third, but sales kickoff. Those are the keynotes I enjoy doing to get the sales guys like to really start getting hungry and sweating and ready to go. Alan, you talked about sovereignty and you talked about it at a country level and you talked about it, you know, I'm sure you talk about it at a data level and regulation level and minerals. I love all that.

47:41Here's what I'm struggling with sovereignty right now. is because everybody with this duopoly or whatever we're calling it, everybody's got a hand in every part of this stack. At least the people with money have hands in every part of this stack. The sovereignty question, the migration from clouds, now it's almost like we're going backwards to on-prem. It's not called on-prem, but basically people are saying, I want my data close to me as possible. Can you give me a little masterclass, little mini masterclass on sovereignty right now as it relates to AI and what companies should expect? And not just data, IP.

48:29Yeah, IP, data. I think, okay. So if you, if we go back to the landscape, right at the bottom, we had countries, nations, Canada, US, obviously EU, UK, and then you have dominant players in KSA and UAE, and then you have Asia, okay, in particular, China. So those countries have also funded ways to build out data centers and infrastructures in their own countries, as long as the data stays in their own countries as a competitive advantage to the future, right? Like, for example, many were, I don't know if it's controversial to say, many say in the Middle East, folks, leaders there realize that, hey, the next oil is computing, is AI for the Middle East.

49:29They can build data centers at scale, even in the desert, right? So when I show you back to the master class of sovereign, the real players that are popping up, there's two rows. First is the model providers. Model providers that are built in country or trained in country is a huge business. So I mentioned OpenAI and Anthropic, U.S. type model providers, Meta. Okay. Well, Canada has Cohere. France has Mistral. China has all these Alibaba models. You don't think those are going to keep rising and governments are going to pour more money into those because they see that as a competitive advantage for the future.

50:21So that's the first class. if you want to work with those countries or sell to those companies, realize that the countries are backing the Coheres, Mistrales, OpenAIs, Anthropics. And as you know, in the U.S., there's a little bit, and SpaceX, right? There's a little bit of competition between SpaceX, Anthropics, and OpenAI for the president's time. All right? So you have to first understand that Roe is extremely political. It's not just what's the best tech wins, right? The tech is backed by these government leaders. They're helping fund the development of it because they see it as something competitive.

51:00Just like the size of the military or the size of our oil reserves, how much AI the country is producing is a competitive advantage for the country. You have to understand that. So then, all right, these governments are also wanting data centers locally. And there's three types of data centers being built, as I mentioned before. The hyperscalers are building their own, and they're using third parties like Holder and Turner to build them. And then you have colos that are using Holder and Turner, like Digital Realty or Equinex, that are building more staging facilities that you can build a cage and put stuff in.

51:44And then you have neoclouds. So neoclouds are loving the sovereign concept because they can move the fastest. They're the newest. They're the most agile. So if a country like Switzerland has a sovereign AI initiative funded by the government, a neocloud could swoop in and say, I can win that business, build a data center or partner with one of these parties I mentioned in Switzerland much faster than anyone else. and poof, you get these like$100 billion deals around sovereign AI for countries like Switzerland or Germany or parts of Europe. But it first starts with the governments. They're the ones that see this as a competitive advantage.

52:34They want the data in country. They want the AI being built there and they want the data centers there. Okay, but so let me make sure I understand. So Mistral's in France. That's a NeoCloud. Mistral is the model. Okay. Okay, they are model. I thought they were also a NeoCloud too. So now you're 100 % right, John. So they're smart. They're not going to rely on third parties to build out clouds for them. So they have a dual strategy, similar to Cerebris. We have our own cloud. Mistral has their own cloud, but they also partner with Microsoft and NeoClouds to run their models there. Okay. So then in that case, then they would not allow, France typically wouldn't allow a core weave into.

53:17If I were France and I'm subsidizing the business there and I wanted Sovereign, the data center has to be in France. And let's be realistic. The model providers that are built, the models that are built in country are going to get a leg up. So in that situation, Mistral may end, I don't know for a fact, but like OVH or Orange in-country clouds may have a leg up than a CoreWeave, which was started out of the US, even though it's heavily partnering with GPUs. This is a bias against other countries coming in and they're worried about any type of IP based upon their model. I just don't understand how they're going to keep up with some of the giant frontier models in the US or even in China in the end the customer matters the most so if the models are horrible the customers people are using OpenAI in France today that's the amount of money that the frontier models in the US are throwing at technology in France like Mistral I mean, what are they throwing?

54:34A couple billion or something, maybe? Nothing. I think that's why the U.S., these two models, Anthropic and OpenAI, are still leading. Well, here's the problem I see with it, is now you have these model, these frontier model companies that are, it's very strange for me, because they're the first one raising their hands to say, hey, we got a problem with open source. So when I first started thinking about like open source, it was like, oh, that's going to be a bad thing for like data security and sovereignty. And then all of a sudden I started to think, well, the people that are telling us it's a bad thing are the ones that have the most to risk.

55:13And they're the ones trying to get the government to make all this recommendations against like, you know, Anthropic and OpenAI are like they benefit from the most from government regulations of, you know, non -open source. source. I'm working my way through that. But the next layer that I'm trying to figure out, if I'm a company today, Alan, and I'm working with these companies and I'm bringing the parts of these stacks into my company, how about the sovereignty of the IP and the data from a company perspective as it relates to protection of that, like not getting out into the wild? Like, yeah.

55:59So if you're an enterprise customer using AI. Yes. Yeah, no, I mean, I think. What are your recommendations there? Meaning where Johnny's going, it's like you have very deep domain experience inside your application or your software product. And you're now utilizing frontier models in your product. What's keeping them from stealing your IP? I think there's a trade off here. I think folks like software companies, like, I mean, great ones like MongoDB and Snowflake and Datadog, they error more to the side of innovation. We need to get out fast. We need our developers to be able to use CodeGen faster without losing with the risk of, yes, there could be some IP risk.

56:47And I actually agree with that because the risk of going slow and waiting is existential. Your software company could be gone if you wait. So if I'm a software company, I go on the side of innovation every time for AI. Every time. Use it. Plug it in. You take some chance. Now, an enterprise company like a bank, I would go on the other side. Would you say the same thing to Figma? Well, I mean, Figma is in a different – I mean, I've got to be careful what I say about Figma. Sorry, but I'm just saying it like that. They're a great company. That's an example for me. That's kind of an example for me.

57:24Figma, on the other hand, I mean, this is where either you use AI or you get gobbled by AI. What are you going to do, right? I think that's a general statement, whether it's a Figma or a MongoDB. I think MongoDB is smart, okay? Hey, we're going to use AI where anyone can install MongoDB with AI. Brilliant. but you still have the database that you can sell them, right? You just can't sell them any implementation services. That's a smart use of AI. Figma, obviously, if it's going to be gobbled by AI, I would get ahead of it, like a lot of software companies. I think the enterprises is where I would err more to the sovereign protect data story and get it right before you go all in.

58:08And that's why, if you notice, to spend in AI is much larger by software companies and much more delayed by the traditional enterprises. And that's why you see even salespeople. So you guys obviously talk to more sales leaders than I do, but I still keep close with many of my friends, including Alex here. most sales leaders today probably at least in the ai space are selling to software companies more than they used to versus traditional enterprises because of what i said in the you know 10 years ago it was the opposite and now it's kind of flipped i don't know if you guys are seeing that yes with the people you're talking to 100 and it's because of what i just said software companies are willing to embrace AI, are willing to take the risk, either use it or get eaten by it.

59:07And enterprises are so kind of risk-averse because of the sovereign data protection, IP protection, that they're going very slow with building out agentic workflows. Do you think that's where the NeoClouds and all these different cloud versions, and do you think that's where this is going to get solved for the enterprise? I still think the hyperscalers win the enterprise. The NeoClouds are more, to me, where they're kind of more focused on the ISVs. And the hyperscaler is because of the rigor they have on how to handle data security and sovereign. Now, there's a third layer to this. There's also like the traditional kind of government type organizations that are building clouds, right, for top secret data.

59:54But in that middle ground, like you're a bank. if you're a bank, are you going to trust AI in the future with a hyperscaler, a neocloud, or a slower kind of government-funded organization? I would argue they'll go all through the hyperscalers. I think the hyperscalers are banking on it too. And there'll be some regulations there, I'm sure, that will mandate that for banking at the hyperscaler level. 100%. I mean, this is where when we were at Mongo and we were selling, um i remember even i mean it feels like attorney ago but we remember like john mcmahon but banks don't want to move to the cloud they want everything on premise remember those days and like slow movers that's all and that's typical in any time you sell a new technology they're always the last to move i mean the government and insurance companies are the last to move.

1:00:50Once they went, but they go. Then they go big. Then it's like giant deals. I think that's coming. I haven't seen it too big yet. I've seen it with the ISVs, no doubt. Yeah. Excellent, Alan. Three master classes in an hour. My man. The man. The myth and the legend. They're amazing, Alan. I want to be invited a third time. You're going to be, bro. We'll get you back, brother. So I think for all part of the leaders, John, both Johns, it's a cool job. Just I think CEOs are different these days. You just got to take some time and do your research before you get in front of a CEO of an AI company or you're going to get eaten alive.

1:01:36Yeah. Thank you, Alan. Thank you, Johnny Cap. Thanks to everyone for listening to another episode of the Revenue Builders Podcast.

1:01:48Alan Chhabra: Thanks for listening to today's episode. If you enjoy the content, please subscribe, rate, and review the show to help us reach more people. This show is brought to you by Force Management, where we help companies improve sales performance, executing the growth strategy at the point of sale. Check out forcemanagement.com for more information.