In our previous piece, Miners, Shovel Shops, and the Generative AI Gold Rush of ’23, we discussed the massive opportunity that is generative AI and our view of the value of the “shovel shops” in the early stages of a “gold rush of opportunities”. Here, we discuss several of the other major trends in software – in addition to what we believe is an attractive opportunity in generative AI on which everyone is focused — and articulate our unique take on each.
Digital Transformation.
Everybody is familiar with the term, but all struggle to provide a good definition. Coming at it from a software-based perspective and using the lingo of economics, we define digital transformation simply as the substitution of software for both labor and capital in the means of production. This stands in contrast tosoftware used in the management of the means of production, which, from the 1970’s through the ‘00’s, has characterized most enterprise software falling into one of the six “system of record” categories: Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Human Capital Management (HCM), Supply Chain Management (SCM), Information Technology Systems Management (ITSM), and Business Intelligence (BI). These areas will continue to see growth, but they mostly fall outside of what we define as digital transformation.
Though treated similarly from an accounting perspective, software is different from physical capital, and its direct substitution in the means of production can quickly catalyze completely new ways of adding value and doing business, while deeply disrupting existing industries and business models in a way and at a velocity that is not typically encountered. As part of this transformation, business innovation now primarily occurs as part of a software development project. This is why the most innovative companies are increasingly software-based, specifically cloud-based, even if their products are not. At this point, some industries have become more software-based than others, but digital transformation has touched all industries, everywhere. As a trend, digital transformation has been around for over a decade, but it will continue to unfold for many more years as Moore’s Law continues to make software more powerful and ubiquitous.
Sometimes digital transformation is directly investable: Uber in local transport, AirBnB in lodging, Amazon in retail, Zillow in real estate, etc. However, we see its derivative, the rise of meta-software, as yielding equally attractive growth and return potential but with less stock selection risk. Regardless of the degree of direct “investability”, we think digital transformation is a crucial overarching trend to understand in order to invest in software companies and software-based businesses. And we think it is critical to get into the weeds and read case studies about digital transformation projects, both the successes and the failures, in order to understand the process of digital transformation more deeply as it is key to the future of business itself.
The Rise of Meta-Software.
This is an overarching long-term trend that is a derivative of digital transformation. First, we define meta-software here as software that helps develop, secure, and operate (i.e., DevSecOps) other software. The idea is that, as software substitutes directly in the means of production, not just the management of the means of production, software becomes much more mission critical. With software functioning as the means of production, there is no turning back. There is no “man-config” mode. The performance of the software equates to the performance of the business. The classic example here is Uber — a pure software-based business — without its software, Uber’s business wouldn’t default back to being a nationwide telephone operator and taxi dispatch service because that business does not work with humans at scale. So, Uber invests heavily in meta-software to run its software and its business.
While Uber and fintech and many other areas are the attention-grabbing stars of this process, it is happening now everywhere. This process of software substitution is universal throughout businesses and organizations at different scales, geographies, etc. The rise of meta-software posits that the more mission critical your software, the more (and better) software you’ll need to handle that software. Think of it as IT software tools finally having their day in the sun, a trend that we think is much bigger and longer term than expected. Among the biggest meta-software companies: GitLab, Atlassian, DataDog, New Relic, Dynatrace, and ServiceNow.
Increasingly Profitable Cloud Even as Topline Slows.
According to market research firm IDC, cloud subscriptions approached 40% of total enterprise software revenue in 2022, and we expect that figure to hit 50% in 2024. This means that, while the enterprise cloud migration trend still has legs, it has already hit an inflection point in its adoption, and we expect cloud subscription revenue growth to continue to decelerate from ~19% Y/Y growth in 2022 to mid-teens through 2026. We have already seen the revenue growth rates of cloud providers — including infrastructure, platform, and cloud-based applications – decelerate significantly since 2021. As topline growth slows, we believe cloud-based providers will begin to realize considerably more operational leverage and expand operating margins more than most expect.
First, as part of our “No More Mr. Nice Guy” trend that we are seeing now for many companies with oligopolistic market positions, we believe that most cloud-based providers will hold the line on pricing, starting with the infrastructure and platform providers, AWS (Amazon Web Services), Microsoft Azure, and GCS (Google Cloud Services). These infrastructure providers pass a portion of this leverage on to their largest customers, the app-layer providers (e.g., Salesforce, SAP, Netflix, ServiceNow, Zoom, Workday, Atlassian, etc.), most of which should be able to exercise sufficient pricing power with their customers, resulting in considerable realization of operating leverage, margin expansion, and faster EPS growth for some time compared to revenue growth.
Second, there are two sources of this operating leverage on the cost side: 1. simply the sheer economies-of-scale generated now as the total operating cost of providing some cloud-based service does not have to increase nearly at the pace of revenue growth, especially for those providers with most of their initial development expenses behind them; 2. Moore’s Law continues to cut in half the capex needed every 2.5 years by the infrastructure providers (e.g., AWS, Azure, GCS) to deliver the existing level of services for existing customers and does the same for COGS for the cloud-based application (i.e., Software-as-a-Service or SaaS) providers (e.g., Salesforce.com, Netflix, ServiceNow, Uber, etc.) where the cloud infrastructure service is an ongoing cost of providing their app-layer services.
Since the 1980’s, we have observed that operating margin for traditional software companies with a business model based on license sales and maintenance contracts tops out at around 40%. This occurs because, as a traditional software company matures, the business becomes increasingly labor-intensive as it needs to support a larger and larger installed base with more previous versions running in increasingly diverse information technology environments. This situation increases the interoperability and process challenges, and it requires more support “bodies” even after growth levels off. This stands in contrast to cloud deployment where a singular copy or just a few copies of an app run in a consolidated and centrally accessible “place”, even if that place is virtualized, i.e., a software “instance” that is itself running on an instance provided by a cloud infrastructure provider. Even if the software company charges a healthy premium for that labor as a service, IT services labor can only be marked up so much, especially with wages on the rise, keeping operating margin expansion beyond 40% in check.
In contrast, cloud-based business models start off with low, often negative operative margins before the solution gains broad adoption but, unlike a license/maintenance-based model, there is no real brake on cloud operating margin expansion until you hit 70%, which could potentially attract the attention of regulators like the Federal Trade Commission (FTC) in the U.S. Unlike the traditional software business model, cloud does not become more labor intensive as growth slows; it becomes less so. If you want to see what a mature cloud-based app provider looks like margin-wise, we think both Visa and Verisign are good guides, having consistently maintained 60%+ operating margin for many years. While 60% operating margin might be a stretch right now, we think it is plausible that operating margin for cloud-based application providers like Salesforce, ServiceNow, and FICO to exceed 50% before 2030, others might get there faster.
Real-Time 3D rendering for fun and profit.
Some call it the metaverse, others call it Roblox, and still others just point to the millions of people playing Call of Duty (CoD) online. Regardless of the name, we are seeing substantial growth in software that performs interactive 3D rendering in real-time to generate a shared experience for multiple participants over a network. Today, this software is used for entertainment, at least among consumers (e.g., the game platforms like Roblox and CoD). Now that Moore’s Law has made such real-time rendering possible, it will only continue to improve the experience and reduce the cost of providing it from here. We may hear about the latest and greatest online game experience because of a new Roblox game, the latest CoD title, or a completely different platform that can now leverage much of the coding that makes 3D rendering look and feel much more real now.
Regardless of the specifics of the manifestation, RT3D-based games and similar interactive content will only get more compellingly, immersive, and realistic with new paradigm-defining hits being around the corner. If the present use cases related to 1st-person shooter entertainment do not float your boat, there’s always sex. Sex not only sells, it has also played an often unspoken role in catalyzing the adoption of new technologies, devices, and software, including the home VCR and web video.
While we think Roblox is a clear beneficiary of this RT3D trend, there is significant execution risk, in our view. Microsoft, which we believe to already be a major likely winner from generative AI, probably wins again with RT3D due to its pending but likely successful acquisition of Activision-Blizzard, developer of the ever-expanding CoD franchise of games.
Meanwhile, many of the fundamental software code building blocks for RT3D apps and games are now quite stable, with Moore’s Law-driven performance improving rapidly enough to be employed in other areas, such as 3D physical world simulations of new product designs or more accurate simulations of, well… anything. Whatever it is, as long as we can gather sufficient data on it, a process that’s increasingly also easy given the massive cost reductions we have seen in increasingly powerful digital sensors, then we can simulate it and manipulate that simulation using design software. As that simulation – whether it’s a digital twin of a skyscraper’s air-conditioning unit or traffic on the interstate — becomes more accurate, its value both increases and extends to additional use cases. There are several businesses that benefit from the adoption of RT3D rendering and physical world simulation and design software, among them: Ansys, PTC, Dassault Systems, and Autodesk.
Handling the Insane Complexity of Chip Design.
While long thought of as a niche, the software employed by chip designers to automate the layout and testing of transistors and circuits on processors, known as Electronic Design Automation (EDA) software, as well as provide chip design IP for a growing number of functions, is entering a renaissance driven by the sheer complexity of chips now, with the latest CPU’s and GPU’s soon to feature more than 100 billion transistors. Further, server chips are being increasingly designed as part of a stack complete with CPU, memory, solid state storage, and GPU or FGPA chips to optimize performance for highly memory consumptive applications, specifically, generative AI. These 3D Integrated Circuits, or 3D IC’s as they are called, introduce additional complexities in terms of heat dissipation and a growing number of chip-to-chip connections, complexities that increasingly require software to solve. Interestingly, those design problems, which have become mind bogglingly complicated, are now being solved by software employing generative AI and running on the latest, most powerful AI-optimized processors in the world. So now, we have AI-optimized processors used to design even more powerful AI processors in a mutually reinforcing feedback loop. From an investor’s perspective, this kind of autocatalysis is a powerfully attractive force if you can find it.
In addition, the EDA industry, as we see it, is now much more attractive. The consolidation among the fabless chip companies has abated and hyperscale operators like AWS, Azure, and GCS have emerged as major customers with a growing number of chip design projects, not to mention device makers like Apple and Sony forgoing off-the-shelf chips and designing their own. The primary beneficiaries of this trend are Synopsys and Cadence Design Systems, part of a three-company oligopoly in EDA.
