Most days I come into the office working on our blueprint map of initial ideas, Long and Short, that we sketch out in our quarterly new idea generation review. Typically, these are unique ideas that we pursue with research, deepen our knowledge, and as much as possible, develop into fully built Shorts and Longs.
But sometimes, after reviewing many companies, some themes filter through and I have to get ‘em down on paper. Here goes:
3 Vectors For Finding Tech Shorts:
- Taking Price – When unit adoption flattens, take price. We see this business model item recurring now in a wider and wider list of the companies we review. It is probably a reflection of companies reaching a point of flattening on their unit adoption curves, and making up growth shortfalls with price extension. Price increases do push revenue, gross margins, and FCF% higher, but as signals of an adoption curve, they imply slower growth ahead and multiples contract. When reviewed individually it can become a Short thesis on a stock. When broadly, it is potentially a signal across the landscape.
- Period of 3 Enterprise IT architectures won’t last – Typically, enterprises run one architecture, except around a pivot, in which case for a period they run two. Today enterprises are running 3 architectures in total: on premise, hybrid/private, and cloud. We consider Hybrid its own architecture today because the end goal of the architecture is different than cloud, as both are competing for control of workloads and data. The subset of vendors in Hybrid differ from Cloud vendors, and the technology architectures and scale factors differ as well. Today, enterprise IT dollars (capex and opex) fund these multiple structures. How does it resolve? In our view: cloud has the greatest scale factors and arguably the largest dollars of investment behind it. So, where is the Short? On-premise IT vendors have benefited from strong macroeconomic conditions that have forced companies to proverbially ‘keep the lights on’ for their existing architectures while they prepare for transformation. Exposed companies in that area will see cyclical growth signals falter. The growth rate of Hybrid architectures will also probably slow as companies look for more efficient structures.
- Short term data analytics cycle is overbuilt – Since 2016, major software companies have been building analytics tools into their applications. Once a localized division of tech, the domain of Analytics spread throughout the enterprise across a wide array of tools. Analytics requires CPU + memory…and analytics at scale = ML, and ML with a business purpose ROI is often called AI…Competition has been religious and spending in this area has provided a cherry on top of a great tech cycle, but it really coalesced into an accelerated period in 2H17 and at this point we are looking at 2H18 hitting some impossible comparisons as the digestion phase settles in. ML and AI are definitely bullish for Technology…but on a 25-year scale. Today, it might mostly be categorized as solution looking for a problem. Why digestion phase? The ROI for ML-aaS (read: “Machine Learning as-a-Service”) for example, is still in development as companies try to figure out how to use this new technology. So, while this area might be the 25-year bull case on Tech, it is a product cycle that will take time to yield real results beyond the hype period. Analytics are also mainly backward looking. Companies looking to run their companies based on ‘data’ may err by prioritizing the value of hard data over the softer category of ‘vision’. The over-reliance on ‘data’ will hurt.
What would you be Long here?
Ami Joseph
Managing Director
Yosef Vaitsblit
Analyst
