The term "artificial intelligence" (AI) seems to have become a fixture in the mainstream investment lexicon. First-quarter earnings calls were dominated by corporate executives parroting buzzwords and illustrating visions of AI roadmaps. While AI applications present an exciting next frontier for technology, it is crucial that investors do not become disillusioned.
One company that saw its stock skyrocket after Q1 earnings was GitLab (GTLB 4.24%). Although GitLab's first-quarter results beat Wall Street expectations, there is little doubt that its plans for AI helped fuel a post-earnings surge of over 49%.
I'll admit it: I did not view GitLab as an AI play. However, after the company explained its roadmap, integrating AI made sense. In this article, I will analyze GitLab's business, explain how it can leverage AI as a catalyst, and help determine if the stock is a good buy right now.
How is the company performing?
Writing software code is similar to other types of projects. It usually consists of teams of people across an organization, requires several iterations before it's finalized, and often carries its share of bumps in the road along the way. GitLab is a software-as-a-service (SaaS) tool that helps organizations during the software development process. It allows you to save drafts of the new code, as well as keeps track of tickets for bugs and glitches that may occur when testing your new software.
Given the pace by which companies of all sizes are embracing digital transformation and leveraging data to make decisions, it should not be surprising to learn that GitLab's products and services are in high demand. Some of GitLab's customers include Goldman Sachs, Lockheed Martin, and Zillow.
For its first quarter of fiscal year 2024, which ended April 30, GitLab posted revenue of $127 million, up 45% year over year. Moreover, the company ended Q1 with over 7,400 customers, representing 43% growth year over year.
NASDAQ: GTLB
Key Data Points
Despite this impressive top-line growth, GitLab is still burning money. For the quarter that ended April 30, the company posted a net loss of roughly $52 million, basically double its net loss from the same period in the prior year.
As an investor, something that I keep in mind is to always analyze all three core financial statements. In the case of GitLab, looking at the income statement alone would likely lead you to believe the company's path to profitability is going in the wrong direction, which could lead to liquidity issues down the road. However, upon further analysis of the cash flow statement, investors can see that GitLab's cash from operating activities was negative $11 million in Q1, compared to negative $28 million for the quarter ended April 30, 2022.
Looking at cash burn from this purview shows a vastly different picture than the income statement. GitLab's use of cash in Q1 was far lower than it was a year ago, so the company's goal of achieving cash flow breakeven by fiscal year 2025 seems reasonable.
Image source: Getty Images.
How can AI bring the company to the next level?
GitLab's CEO, Sid Sijbrandij, began the earnings call eagerly explaining how the advent of AI and machine learning could serve as a bellwether for the company. He went on to say that GitLab delivered five new AI applications in Q1, and in the first couple of weeks in May, the company published an additional five use cases.
Have you ever noticed that when you type in a prompt on Google, the search engine often fills out the remainder of your question? Well, one of the more exciting opportunities for GitLab's AI ambitions is integrating the ability to help software developers write code in real time. The company rolled out a beta version of something it calls Code Suggestions.
Code Suggestions essentially monitors written code in real-time and prompts developers with suggestions. The end goal is to help organizations write and publish software more efficiently. The lucrative part of this type of technology is that as software is developed faster, companies can theoretically bring products to market more quickly, which would directly impact the top and bottom lines.
Should you buy the stock?
Given that GitLab is not yet profitable, valuing the business from a price-to-earnings (P/E) standpoint is not feasible. As of the time of this article, GitLab trades for 16.8 times its trailing-12-month sales (P/S).
To put GitLab's P/S multiple into perspective, it might be worth looking into a noteworthy acquisition. One of GitLab's closest competitors is a company called GitHub. In 2018, Microsoft acquired GitHub for $7.5 billion. At the time of the deal, the privately held GitHub was rumored to have between $200 million and $300 million in annual recurring revenue (ARR). This implies an estimated multiple of 30 times ARR at GitHub's midpoint. While a multiple of this size may seem egregious, Microsoft disclosed in October that GitHub reached $1 billion in ARR.
It is important to note that looking at GitLab's P/S ratio is not completely the same as looking at ARR. Oftentimes, SaaS businesses are comprised of recurring software licenses and non-recurring professional services. GitLab is no exception. However, its recurring revenue makes up about 87% of its total revenue base. The Microsoft acquisition of GitHub can still serve as a decent proxy for valuation because it highlights a couple of different things. First, the deal price underscores how much Big Tech emphasizes the development of software code. But second, and perhaps more importantly, it shows how quickly this type of tech can scale when given the right resources.
GitLab's current market capitalization is only $8 billion, and the company is on pace for roughly $540 million in revenue. It's impressive that GitLab is on pace to be roughly half the size of its closest peer, despite not having the same resources that Microsoft can provide. For investors looking to invest in AI, GitLab presents a compelling hedge to the more obvious Big Tech plays. The most prudent action is for investors to dollar-cost average into the stock over time and use the earnings call to assess if the company's AI vision is materializing.
