00:00Joining us now is MongoDB CEO CJ Desai. And CJ, I know you've had to deal with a lot of
00:06the fears over software and its impact from AI.
00:09At one point, I think you were down something like 46 percent from the highs in January. You're still down
00:14about 10 percent from those early January levels.
00:17But I would note Morgan Stanley raised their price target on MongoDB saying that you're about to see a tailwind
00:23from AI.
00:24For those who aren't familiar, how does a database company profit from AI?
00:30So, Danny, it's pretty simple. First, you know, and the foremost, we had a very strong print after Thursday on
00:39Friday and Monday combined.
00:41We are up 24 percent. And our last three trading days before today, we are up 37 percent.
00:49So very, very good reaction by our investors. And coming back to your question, Danny, it's very simple.
00:57No matter what kind of agentic workload you run or AI workload you run, you always need LLMs, you need
01:05harness, and you need a data layer.
01:07And we provide that strong, robust data layer that can scale as agentic workload scales.
01:14So we are seeing early momentum where AI companies, including Frontier Labs, are building on MongoDB as the data layer.
01:24And we are seeing that early traction in the enterprise as well.
01:28So I'm very encouraged by what I'm seeing play out across the globe.
01:33I mean, I just wonder why it takes Jensen Wong mentioning this, CJ, to really get investors focused on the
01:42fact that in order to use these apps,
01:44which we now all use on a daily basis, you need to have operational data, vector search data, persistent memory
01:51embeddings, re-ranking,
01:52like all of this stuff, which we're currently using a lot of times many different tools for.
01:57And you provide all of that as one back-end layer.
02:02Why are you only a $30 billion company?
02:04Why hasn't the market bid you up to $60 or $90 or more?
02:09That's a great question.
02:10And what you outlined is 100% true that we provide the data layer with search, vector search embeddings,
02:19all in one with real-time data and operational data, so you can truly run agentic workload.
02:24From what we are seeing right now, you know, we had a slight acceleration in Q1 compared to Q4.
02:31Our Atlas business that investors really care about grew 29.4%,
02:35and we raised the guidance significantly for the year, including the profitability.
02:42And what I would say, Matt, is that we are, it's not if, but when.
02:47And as enterprises start building this agentic applications, most of them are in early journey,
02:53compared to like Frontier Labs who build on MongoDB,
02:56or AI-native companies like Eleven Labs, Mercor, others, they completely build on MongoDB.
03:02Eleven Labs has 40 million agents running on MongoDB.
03:06So I would say the enterprise is on a little bit behind trajectory.
03:12And, you know, at 2.5 billion plus, we are very optimistic, and I feel very good about the business.
03:19Hey, we're looking at just a wall full of your logos, customers of yours.
03:25How many more logos are you adding to these walls, CJ?
03:30I mean, how many more clients are you going to be adding, you think, in the next 6 to 12
03:35months?
03:37I would say, you know, when I look back at Q1, or consistently over the last five to six quarters,
03:44we add somewhere between 2,000 to 3,000 customers or new logos.
03:49So in Q1, Matt, we added 2,500 new logos, and I feel very good about it.
03:55They come through our self-sermotion and sometimes directly via enterprise.
04:00So 2,500 is a quarterly rate.
04:02And as more and more customers are aware of our capabilities that you outlined beautifully,
04:08I would say we will continue to add.
04:10So I would be disappointed if we don't add 5,000 to 10,000 additional new logos in the next
04:166 to 12 months,
04:18whether it's startup ecosystem, digital natives, or AI natives, or enterprises that continue to expand with MongoDB.
04:26CJ, at the same time, you've come into this company more recently.
04:30And since then, as leaders tend to do, doing some shakeups of the C-suite,
04:34a new chief product officer for AI, a new CRO from Confluent.
04:38Do you have any more changes you think you'd like to make to the company?
04:42I would say that, first, I feel very good about the team in place.
04:47They all joined recently.
04:49One chief product officer who we promoted internally has been with us, Ben Cephalo, for the last nine years.
04:55And he will run core products, Atlas and our enterprise advance.
04:58And Pablo Stern, based in San Francisco, would be running everything AI.
05:03AI is currently the center of gravity in San Francisco.
05:06So I feel very good about him leading AI and emerging products and working in the AI ecosystem.
05:12And for chief revenue officer, yes, Ryan McBendt joined us end of April on the tail end of Q1
05:18and really fired about him and Eric are running the go-to market.
05:22So the team is in place.
05:23We're a great CFO.
05:25And I feel very good overall about the team that will help MongoDB scale.
05:29It seems like Jensen Wang, though, is your best.
05:33Like, he should run your IR division.
05:37How important is it that the godfather of AI, as Dan Ives calls him, calls out MongoDB, calls out software
05:45makers?
05:46Because it feels like he does more for you than your earnings.
05:52I would say that is true.
05:54Love Jensen, probably the best innovator I have met in life and really enjoy our partnership with him,
06:02even when I worked with him in my previous jobs.
06:06And here is what I would say.
06:08There was this whole fear about SaaS, software, what is the role of software, will it get disrupted.
06:15And it's just not true, right?
06:17I look at enterprises.
06:18They are actually creating because of AI, as Jensen calls out, even more apps.
06:23And what we are lacking right now for software industry is optimism.
06:27What we are lacking is imagination.
06:29And you combine the two, imagination and optimism, I believe we will create fantastic AI applications for years to come.
06:38Most of the AI transition, like cloud transition, Matt, takes 10 to 15 years.
06:42And I would argue that we are still in our leanings.
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