The Six Five Team discusses Elastic Q1 2024 earnings.
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Transcript:
Daniel Newman: Hey, let’s talk about a company we’ve never talked about in earnings before, Elastic. We brought Elastic up. By the way, Pat, you know how you and I love a good deck. One of the best decks I’ve seen from an earnings deck for a company that people probably largely don’t really understand much of what they do was Elastic.
We’ll put it in the show notes, but if you actually check out the deck, it was very, very interesting. Elastic’s really trying to tell a story right now. We talked about vector search, we talked about the whole idea of search in the relationship. When you’re doing generative AI, you’re talking in natural language to a machine. When you talk in natural language in a machine, there’s a lot of complexity that goes into taking that natural language and returning you something generatively that not only looks and reads like real text, but that’s actually accurate. We talk a lot about hallucination. The company did a really nice job of telling its story of how it’s grown through these inflections and that AI is the next inflection. We’ve seen huge growth. We’re seeing data grow exponential. We’re seeing basically the ability for natural language and the ability for API that can connect all this data that companies have, enterprise data as well as internet data and create these applications that are going to be not only in compliance, they’re going to take care of PII in privacy and data.
They’re going to allow human-like interactions and of course they’re going to be able to take foundational and models that have been pre-created and use them in a way that’s utilities to individual businesses. This is that trend and shift of training to inference that I talked about is this is what’s going on, but who’s one of the beneficiaries? One of the beneficiaries is going to be any companies that enable intelligently plugging into your applications and then doing enterprise search. And enterprise search is really the ability to use your data. For everybody out there, LLMs are great, but as we all know, it’s table stakes. When everybody is asking questions of the same dataset, it’s giving you basically what I would call search 2.0 or new filtrations of recommender engines and filtering systems, which has been the baseline of AI. And if you don’t believe me, go back and look at where Jensen Wong spent a lot of his effort in his early days of building out foundation frameworks, Jarvis, Merlin, recommenders, filters.
This was where it started. Now it’s how do we plug in and do this very, very quickly? Well, Elastic focuses on observability within Elastic search platform, focuses on security within its platform, and then it focuses on actually having an extensible, flexible search that you can use. One of the things that I also really like about any company that’s a little bit smaller like Elastic, that’s doing things. We’ve had the chance to talk. I’ve spent some time with Ash, their new CEO, a very bright guy, but the company’s doing a very good job of telling its story through its customers. And I can’t tell you how much I admire that. It went down the list and it looked at who’s using its observability and why. It was able to talk about Comcast, Orange, a European company. It was able to talk about BMW. And it actually breaks down in their earnings deck, but really why companies are using it, how they’re using it, and how they’re going to basically be able to drive it to create fast responses, handle multi pediment scale.
And then of course, relevance. Relevance is what you talk about when you talk about hallucinations, inaccuracies. Not only can it search through all the data paired with the public internet, large language models, but can it find you the most accurate, most relevant without hallucination? And that’s what I think Elastic is trying to accomplish. It’s going to be a competitive market, but they were early to this market. They have a very, very important subset of customers. They plug in with the public cloud providers and they’re really making a nice pivot from the first generation of filtering of the internet through traditional search to generative text where I think Elastic is going to have a significant growth opportunity. Strong quarter strong growth numbers, Pat, I’ll turn it over to you.
Patrick Moorhead: Yeah, I agree with you on the deck. In fact, it was 34 pages of information before they actually got into the first quarter ’24 results. Revenue was impressive, not overly impressive at 17%. But one thing that I really like here, and again, I was talking earlier about best of breed, narrow startups that grew, then basically going from a search capability to observability, security and then search. And then you have generative Ai, which depends on a vector database and essentially what the company has been doing for years. For all you listeners out there who don’t understand what a vector database is you have a standard database, which think of it as a giant spreadsheet in the data center that’s text and numbers. Vector databases are for things like images and videos and any content that is not text or numbers. Essentially, you create vectors to be able to do pattern matching and run generative AI algorithms against that.
My prognosis for Elastic is they’re going to see some mega growth once the generative AI kicks in. Well, what do you mean, Pat, kicks in? We’ve got all this stock market height, probably 1% of data center usage is generative AI right now. Also, it does-
Daniel Newman: Good point.
Patrick Moorhead: … machine learning. Generative AI doesn’t mean that analytics, machine learning and deep learning is going to go away. I like to look at these different methods as a bag of golf clubs. You wouldn’t just take one golf club out to do 18 holes, use your driver on the putting green or use your putter to drive with. Maybe after a few drinks, but not on a day where you’re actually trying to…
Daniel Newman: Ever seen Tin Cup when he played the entire round with his seven iron?
Patrick Moorhead: I did. That was pretty impressive.
Daniel Newman: It’d be harder to do that with a driver for sure.
Patrick Moorhead: A lot harder. Those aren’t going to go away here. So, I was impressed too. You mentioned something about their customer set and it is impressive how they can get their customers to say yes. Go to slide 26 of this quarter’s investor deck, and they’re huge companies and they’re not all born in the cloud. You have born in the cloud stuff like Uber, and then you have the, I call it brownfield companies with people like Home Depot. So, a pretty impressive crew here. Dan, we may or may not be going to one of their events coming up.
Daniel Newman: Rumor has it.
Patrick Moorhead: Rumor has it, The Six Five may be going to their big event.
Daniel Newman: Maybe.
Author Information
Daniel is the CEO of The Futurum Group. Living his life at the intersection of people and technology, Daniel works with the world’s largest technology brands exploring Digital Transformation and how it is influencing the enterprise.
From the leading edge of AI to global technology policy, Daniel makes the connections between business, people and tech that are required for companies to benefit most from their technology investments. Daniel is a top 5 globally ranked industry analyst and his ideas are regularly cited or shared in television appearances by CNBC, Bloomberg, Wall Street Journal and hundreds of other sites around the world.
A 7x Best-Selling Author including his most recent book “Human/Machine.” Daniel is also a Forbes and MarketWatch (Dow Jones) contributor.
An MBA and Former Graduate Adjunct Faculty, Daniel is an Austin Texas transplant after 40 years in Chicago. His speaking takes him around the world each year as he shares his vision of the role technology will play in our future.
