Current thoughts on the markets
In prior letters, we have explained why economic and market narratives will continue to oscillate much more frequently than before. It doesn’t always mean that markets will go up first and go down next, it mostly means that the narratives and factors that drive markets will dramatically change from one period to the next. The biggest change in market narrative over the last 18 months has been AI, specifically AI-related capital investments, which has for the most part kept an otherwise fragile economy going.
In our opinion, the AI capex narrative is currently reaching its high point, and we think it is unlikely to live up to its expectations in 2025. We have previously written about that the missing ROI link from most of the AI investments. A few months later, the ROI debate is starting to pick up steam. Commentary from Google’s CEO (that their framework has been that cost of missing out on AI investment is higher than the cost of overspending) and Microsoft’s CFO (AI infrastructure ROI will play out over at 15-year+ period) resonate with us and further provides evidence that the current level of infrastructure spending is unlikely to accelerate further.
It is important to recall that capital spending is cyclical by nature and not meant to recur every year. In current analyst models of AI beneficiaries, forecasts largely anticipate that these investments are in fact recurring. As we have highlighted in our past letters, this high level of spending can only be made by the Mag-7 companies and we believe it is highly dependent upon their stock prices outperforming.
Looking at the chart below, which highlights the Mag-7 as a % of the S&P 500, we do not think the historical pace of increase can continue. For example, between 2012-2016, the percent growth – of the Mag-7’s S&P ‘market share’ gain – was 1 ppt/year, however, between 2016-2024, the percent gains accelerated to about 3 ppts/year. To us, this new rate looks unsustainable as it would imply the Mag-7 will become two-thirds of the S&P within the next 10 years.
There are societal implications to these concentrations as well. Consider consumer choice, security of data and information, and the ability to sway populations through misinformation in any one direction. We don’t think that society can afford to have the Mag-7 control most of the economic benefit of digital transformation and impact social changes at such large scale they are operating at. Market forces will likely not allow this to happen.
AI Will Not Kill Software
In recent discussions, there has been a prevalent narrative among well-known VCs and industry pundits regarding the perceived obsolescence of software companies due to the advent of AI. The argument posits that software lacks a defensible moat as AI possesses the capability to effortlessly generate code. We disagree.
Historically, similar prognostications were made with the rise of open-source software, yet the outcome was contrary to the predictions. Today, it is commonplace for companies to offer an open-source version of their commercially available software, and there are widely available libraries and environments to download and leverage free software that a customer wants. However, the intrinsic value of a software enterprise extends beyond mere code generation. In fact, the advent of open-source served to amplify this value. The needs for interoperability, support, and service will persist irrespective of the ease with which one can instantiate a C++ or Java module.
The essence of a software company's worth is anchored in its integrations with the existing enterprise tech stack, the interoperability between various workflows, and the capacity to provide timely support to customers. Additionally, the customization of software to meet the unique requirements of each client is a significant value driver.
In the near term, we do see the seat-based software vendors getting marginally hurt as AI helps improve productivity and reduce hiring of new software programmers. However, as many of the current AI related trials come towards full scale adoption, the needs for hiring new engineers will rise again. Over the last decade, the emergence of cloud platforms has been a pivotal force in accelerating software adoption, and similarly, we believe the emergence of AI platforms could serve as a catalyst for further software adoption within enterprises. This implies that software companies stand to gain during the next phase of AI adoption, once the foundational models and infrastructure of AI reach maturity during the initial wave.
The most transformative aspect of AI lies in its democratization of computer interaction, making it accessible to the average individual. As interactions between users and computers proliferate, there will be increased reliance on new software tools, along with the demand for sophisticated workflows, interoperability, and customization, which is poised to escalate.
We are confident that the trajectory of the software industry is not one of decline but of renewed importance and integration within the fabric of the IT ecosystem.
Incoming Slowdown of AI Capex Will Provide Room for Software Investments
Mag-7 executives and many prominent VCs have done a disservice to society by creating so much hype around AI. Though exciting, technologies like these need time to mature and evolve. Of course, it will only be known in the future, but we feel that you cannot accelerate tech adoption by brute force (in this case, massive capex investments). In fact, in this case, the entire market and economy is unlikely to make the fast leap forward as the constant discussion around NVDA’s stock may have everyone believe.
As we look out in the near term, the two elements that can anticipate a slowdown in AI capex investment are going to come either from the large model companies or from the enterprise customers. The early adopters and the model companies (who have an inherent interest in selling the latest and greatest) will likely continue to push the envelope but our discussions with the mainstream companies are highlighting that most have not crossed the chasm yet, beyond a mandatory trial/concept phase. As the noise gets louder around ROI, and datapoints scarce, there will be even less airwaves/momentum to sustain the hype Sam and Jensen have worked so hard to maintain. As an example, the joke has now become that if you reference the popular Klarna example on the call center cost savings using AI, it will almost be construed as a negative datapoint (because so many people continue to cite the same example, and there aren’t a lot other use cases to cite).
In order to generate substantial ROI from AI, more investments will shift towards the software/application layer vs. the infrastructure layer.