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After over a year of headwinds from customer workload optimization, it appears the hyperscalers are finally reaching a point where future growth will be driven by a more normalized expansion cycle. All three hyperscalers delivered annual growth in Q4 that was either equal to or above the prior quarter. Sequential revenue growth rates for AWS and GCP even accelerated.

With cloud workload utilization returning to being driven by new applications and the expansion of existing ones, software and data infrastructure investors can breathe a small sigh of relief. The strong headwind of workload optimization is yielding to the renewed secular tailwind of cloud migration and digital transformation projects. Lest we get too excited, though, it still isn’t clear what the post-Covid, steady state revenue growth rate will be. It’s likely that the natural growth rate has moderated from the rush to the cloud in 2020, forced by work from home and interruption of physical channels.

Making the recovery picture more complicated, AI initiatives are introducing a new demand tailwind for cloud infrastructure. Just as the obfuscation from workload optimization abates, IT enterprise spend appears to be shifting towards capitalizing on new AI-driven capabilities. Enterprises are delivering tangible workforce efficiencies, productivity improvements and better customer experiences by applying foundational models to their own data troves and business processes.

Whether this AI investment represents incremental budget or is being pulled from existing digital transformation work remains to be seen. If there has been borrowing for AI, that funding may be reset in 2024 as updated annual budgets are rolled out. Additionally, as worker productivity prototypes transition into full scale deployment across the employee base, realized cost savings can be shifted back into budgets for further investment.

The recent earnings results from the hyperscalers provide some hints. The market is certainly interpreting the re-acceleration of revenue growth as a positive signal for software and data infrastructure companies across the board. Stocks for these companies were up significantly the day after Amazon’s earnings (in spite of a strong Jobs report). In the time since I started this post, we have received better than expected earnings reports from CFLT and NET, resulting in outsized jumps in those stocks.

Overall, I think the hyperscaler results portend well for the basket of software and data infrastructure companies going forward. The big hindrance over the past year has been pressure from cost cutting and delayed expansion of utilization. Further, the reset phase for digital natives that spent big during Covid has likely reached its end, with investment picking up again going forward. Additionally, start-up investment (outside of AI) may re-emerge later this year or into 2025, which will bring another tailwind of infrastructure spend to the cloud providers.

In this post, I review the Q4 earnings reports from Microsoft, Google and Amazon with particular focus on their cloud divisions. I also digest commentary on their AI initiatives and speculate what this might mean for budget allocations. As we have already seen, the activity on the hyperscalers has implications for supporting software and data infrastructure providers.

I want to thank everyone for your inquiries over the past couple of months. I took a break from regular blog posts, in order to focus on a few side projects. These included new research areas, doubling down on angel investing and another guest lecture for the London Business School. A common thread within these pursuits was exploration of current trends associated with AI. Without stating the obvious, AI is touching every industry and will have huge ramifications for software infrastructure going forward. This trend is too large to treat as an extension of the existing growth vector for software, which has largely projected through the rise of the Internet and mobile device proliferation.

While today’s AI is the outcome of years of steady, but familiar, efforts in machine learning, it has become significantly more accelerated, broad-reaching and impactful. We are entering an exciting period where software is inflecting from helping humans retrieve information more quickly (traditional Internet) to actually performing tasks on our behalf (generative AI, co-pilots and ultimately autonomy). This shift dramatically alters the value proposition and economic benefit, particularly as AI-driven systems start to perform work that has traditionally been addressed by salaried humans.

The ramifications will be far-reaching for society and naturally create a number of investment opportunities. I am considering these within the context and through the lens of software infrastructure. At the simplest level, AI training and operations will drive a step-change in the consumption of compute and data. The same technology functions that benefitted from the growth of mobile devices will see increased demand driven from enablement of new AI services, except that the magnitude will likely be 10x the influence of mobile.

As it relates to this blog, I plan to continue the same scope of coverage, but will account for the impact of AI on software development and infrastructure. Look for upcoming posts on the business implications for the companies that provide data infrastructure, application delivery, security, observability and core hosting. It is an exciting time to be in the software space – even surpassing the acceleration we experienced during Covid.

Prior to Q2 earnings, I had a thesis that macro-induced pressure on enterprise IT budgets would result in consolidation of point solutions, favoring platform providers with a suite of product offerings. The idea is that technology budget owners (CIO, CTO, CISO) look for opportunities to reduce costs by eliminating redundant services and trying to concentrate spend onto fewer vendor platforms. This consolidation usually results in savings through volume discounts, fewer contracts and less operational overhead. It can also allow staff to be repurposed, who may have been dedicated to managing a particular vendor point solution.

The scope of consolidation can extend to open source projects or DIY internal solutions as well. These efforts are often appealing on the surface – the software license is free. However, they require commitment of the most expensive resource in an IT organization, namely engineers, to make them work. Faced with hiring freezes, IT leaders can repurpose software engineers who were managing these internal efforts to fill staffing needs supporting applications tied to revenue. They can then replace the open source service with a commercial managed service, often reducing cost. A fully loaded software engineer expense can reach $200-300k per year or more (considering total package of salary, benefits, office support, SBC, etc.). It doesn’t take many engineers to cover the cost of a commercial product.

As quarterly earnings season is beginning for software infrastructure companies, I like to monitor the results of providers in adjacent categories for signals about the overall demand environment for the stocks that I currently own. While not a direct replacement, these related software companies can reveal how enterprise spending is trending and raise any warning flags or surface potential tailwinds. Additionally, in the segments in which smaller providers compete with the hyperscalers, we can understand where the large public cloud vendors are consolidating share and where spending trends are benefiting all companies. The reports thus far have been instructive, showing that enterprise spend on software and cloud is slowing, but seems to be surprisingly resilient given the macro backdrop.

In this post, I will go through the take-aways of interest from F5, ServiceNow and Five9. For these posts, I won’t rehash all the performance for a particular company, as some include categories of IT spend that aren’t relevant. Rather, I will summarize and cherry-pick the items that are applicable for software infrastructure and security providers. This supplements a prior post I published recently reviewing the performance of the hyperscalers in Q2.