Pitch Summary:
To illustrate our approach to navigating these uncertainties, we turn to our recent investment in Google (Alphabet), which exemplifies balancing innovation risks with established strengths. Google is grappling with an Innovator's Dilemma as it protects its $200 billion search business from a significant technological shift. To put it plainly, Google Search's primary purpose is to act as a 'match-maker', guiding users to the best so...
Pitch Summary:
To illustrate our approach to navigating these uncertainties, we turn to our recent investment in Google (Alphabet), which exemplifies balancing innovation risks with established strengths. Google is grappling with an Innovator's Dilemma as it protects its $200 billion search business from a significant technological shift. To put it plainly, Google Search's primary purpose is to act as a 'match-maker', guiding users to the best source for their query on the open web. However, artificial intelligence is changing this role, with AI handling much of the searching, synthesis, and answering for the user, reducing the need to visit destination websites to gather information. A natural tension is emerging. Humans naturally gravitate towards the path of least resistance, increasingly depending on AI to undertake cognitive tasks for them. This development poses challenges for content providers and for Google itself, which derives advertising revenue from these interactions. Our bet is that Google is taking appropriate steps to address this Innovator's Dilemma: 1) Create a world-class foundational Large Language Model (LLM) 2) Differentiate on other vectors, such as cost and distribution 3) Value Accrues at the Application Layer Considering the 'air pocket' risks we've identified, we view Google as a key enabler for advancing and participating in the spread of AI. If Google can protect or at least maintain its core search business (a big 'if'), we see significant upside for relatively limited risk, given the valuation multiple we paid. It is worth acknowledging that Google's AI journey, as widely documented, has encountered numerous setbacks. We have added our voice to the critiques of their development efforts, even though they pioneered the transformative technology that now threatens disruption. Yet, despite the wobbly start, Google has made an intensive effort to improve its models to become highly competitive. Our journey of researching the Google opportunity began when we started to use the Gemini models for our internal research agents. Google has exceeded our expectations by releasing Veo 3 for video generation (with audio!) and Genie 3, which generates dynamic worlds on the fly. Why do these matter? Both developments strongly suggest that their models are grasping real-world physics. We are inclined to view this as evidence of their data advantage, particularly from YouTube, in comprehending vision and audio, thereby offering substantial potential in the long-term Physical AI domain (remember Robots?). Regrettably, we do not believe the model alone will resolve the Innovator's Dilemma, as we anticipate the foundational layer will ultimately become commoditised. Google's opportunity lies in de-commoditising other segments of the value chain, enabling it to compete effectively in this emerging technological epoch. Google is distinguishing itself through vectors such as infrastructure costs and distribution. While much of the AI infrastructure narrative is on NVIDIA GPUs, Google's custom AI chips, called Tensor Processing Units (TPUs), are highly competitive and don't carry NVIDIA's 80% margins. Since 2016, Google has already produced six generations of TPUs and will shortly add its 7th generation (Ironwood) TPU. There are significant advantages for Google that they can extract performance and cost gains by having their custom hardware and then an extensive internal and external captive customer base to scale their development. Combined, they offer a cost-effective, high-quality, and low-latency product that sets them apart from other hyperscalers in terms of intelligence-per-dollar cost. Estimates suggest TPUs can reduce operational costs by 20 to 30 per cent compared to equivalent GPU setups. We would not discount the possibility of Google marketing its TPUs to external clients, given NVIDIA's commanding market share and elevated gross margins. Google's $200 billion core advertising business is still vulnerable to significant disruption. This is perhaps the greatest threat to our hypothesis. That said, given Google's existing strengths – a cost-effective, standardised LLM model combined with seven platforms (each with two billion monthly active users) – it's well-placed to capture value by integrating intelligence into its established distribution channels. Imagine email services offering personal AI agents, YouTube serving up personalised ads, or Chrome, Google Maps, and Android all enhanced with AI capabilities. Regarding the core business, it's becoming increasingly clear that the search product will shift from helping users search and navigate websites (where revenue comes from click-through rates) to keeping users engaged within the Google interface (focusing on engagement time and Lifetime Value (LTV)). Google will interact with the outside world on users' behalf through agents – for example, creating a holiday itinerary based on insights from Gmail data and booking flights and accommodation. The revenue stream may involve taking a cut from each transacting provider. Most intriguingly, Google is in a favourable position due to its longstanding search operations on the open web. LLM-based search has become crucial to AI platforms to ensure responses stay up-to-date beyond the model's training data. However, content providers are resisting this shift, as traffic no longer reaches their sites for monetisation; instead, information is generated by LLM models. Yet Google already indexes this information for its search engine, placing it in a privileged position to deliver superior web-search responses. That noted, elements of our thesis carry risks. Google awaits a ruling on remedies in the antitrust action initiated by the US Department of Justice concerning its search monopoly, with a decision anticipated imminently at the end of August. This could necessitate divestitures from specific business segments. Additionally, in April 2025, a court ruled against Google in a separate AdTech antitrust case, finding it had monopolised open-web digital advertising markets. And then there is the valuation-compression risk that occurred in China's Baidu, which experienced a platform shift as open-web activity consolidated onto Tencent's Weixin platform (Tencent being a portfolio holding).
BSD Analysis:
SaltLight Capital presents a compelling bull case for Google amid the AI transformation, viewing the company as navigating the classic innovator's dilemma while maintaining competitive advantages. The manager's thesis centers on Google's ability to protect its $200 billion search business through three strategic pillars: developing world-class LLM capabilities, differentiating on cost and distribution, and capturing value at the application layer. The fund highlights Google's proprietary TPU chips as a significant cost advantage, potentially reducing operational expenses by 20-30% versus NVIDIA GPUs while offering opportunities for external monetization. Google's seven platforms with 2+ billion monthly active users each provide unmatched distribution for AI integration, from personalized YouTube ads to AI-enhanced Gmail and Maps. The manager sees the search business evolving from click-through revenue to engagement-based models where Google acts as an intermediary for transactions. However, the investment faces material risks including pending DOJ antitrust remedies, AdTech monopolization rulings, and potential platform compression similar to Baidu's experience in China.