Alphabet Surpasses Expectations Driven by Robust Search and Cloud Performance

Alphabet’s GOOG third-quarter sales expanded 15% year over year to $88 billion. Strong advertising and cloud revenue growth spearheaded the firm’s top line. While investments in artificial intelligence continue, profitability improved, with EBIT margins expanding 450 basis points year over year to 32%.

Why it matters: A growing chorus of voices has sounded alarms about Alphabet’s search business as a mixture of antitrust concerns and the perception of Alphabet as an AI laggard has made investors pessimistic about future growth.

Contrary to the perception that Google Search is being disrupted, search advertising sales grew 12% year over year. Management’s commentary around AI Overviews was also encouraging, with AI Overviews leading to more user engagement and ad clicks.Also, we’re encouraged to see Google Cloud top our estimates again this quarter—segment sales rose 35% year over year to $11 billion. AI-related workloads continue to drive demand for Google Cloud, a trend we expect to continue in coming quarters.

The bottom line: We raise our fair value estimate for wide-moat Alphabet to $220 per share from $209, mainly due to the firm’s strong quarterly outperformance. We continue to view shares as moderately undervalued, even after accounting for the after-hours positive price reaction.

Using a sum-of-the-parts method, we arrive at a slightly higher fair value estimate of $237 for the firm, with Google Search accounting for nearly 50% of the firm’s total enterprise value under the SOTP valuation approach.The difference between our SOTP and DCF valuations also underscores the value, loosely $17 per share, that gets hidden within Alphabet’s conglomerate.

Coming up: Out of the three antitrust cases against Alphabet, we believe the case against Google Search is the most material one. We are expecting a US Department of Justice recommendation to the courts toward the end of 2024, which will likely include, among other remedies, the divestiture of Android and Chrome.

Alphabet Breakup Unlikely

We continue to view a breakup of Alphabet as a low-probability event. Our view, which is informed by a study of various legal opinions on the issue, is that the burden of proof required for a breakup is significantly higher than that required for other remedies. In other words, the DOJ needs to make an argument that no remedy, short of a breakup, will loosen Google Search’s dominance in the general search market. For a more detailed discussion on this topic, please refer to our Oct. 9 note, “Alphabet: DOJ Eyes Material Remedies in Antitrust Case, With Google Breakup Likely on the Table.”

Diving deeper into Alphabet’s results, total advertising sales rose 10% year over year to $66 billion. Along with aforementioned strength in search, YouTube ads also continued to grow at a solid clip, expanding 12% year over year to $9 billion. As mentioned above, we were encouraged by management’s commentary on AI Overviews and its positive impact on ad clicks and user engagement. With the firm rolling out monetization of AI Overviews in October, we remain confident that Google Search will be able to transfer its dominance of the general search market over to the AI-first search space.

Diving deeper into YouTube, while the company only provides data on the revenue Alphabet generates via YouTube ads, YouTube also has a large subscription business. We estimate that YouTube TV, YouTube Music, and YouTube Premium bring in north of $15 billion on an annual basis. Along with roughly $35 billion coming in from YouTube ads annually, that puts YouTube’s annual sales in the low-$50 billion range. Along with Google Cloud, we expect YouTube to become a larger part of the Alphabet story in the coming years and we model video advertising and subscription growth to remain strong over our five-year explicit forecast.

From a generative AI perspective, we like the tools YouTube is investing in to enable creators on YouTube to create videos, streamline editing, come up with suggestions on titles, and more. We expect these features to drum up more engaging content on YouTube, particularly for YouTube Shorts. From a monetization perspective, more user engagement is positively correlated with advertising sales on digital platforms, allowing YouTube to grow its advertising sales over time.

When analyzing some of Google Search’s AI-native search engine competitors, we see durable monetization as the most obvious long-term problem for these companies. Currently, AI search alternatives such as Perplexity or ChatGPT charge users a subscription fee for premium features, while also accommodating a large nonpaying user base. According to various estimates, more than 95% of ChatGPT’s and Perplexity’s users are nonpaying. The obvious issue with supporting such a large nonpaying user base is that running an AI search service isn’t cheap, and the compute costs associated with serving 95% of your customers for free far outstrip whatever subscription sales you bring in from the paying 5%.

Digital Advertising is the Key

We believe that in the long run, monetization through subscriptions is not a viable business model in search. A good example of this infeasibility is Neeva, an AI search engine that was founded by a former Google executive and was recently acquired by Snowflake after having failed to make any significant mark in the search market. The path to search monetization, in our opinion, runs through digital advertising.

As the likes of Perplexity and ChatGPT begin to monetize their nonpaying users via ads, they will have to build out teams and technologies they don’t currently have. These investments would include building ad-targeting algorithms, user-signal monitoring, ad auctions, and relationships with large advertisers, to name a few. While one could reasonably argue that these companies could leverage the technology and relationships built out by Microsoft Bing—provided that the executives at Microsoft agreed to such a deal that could also hurt Bing—it would still be an expensive endeavor, with future returns on these investments far from guaranteed.

The second issue with the “Google search disruption” thesis is Google Search’s continued innovations and product launches in the AI space. Let’s take the example of a normal user who has used Google Search throughout their digital life. If faced with the option of switching to another search engine or continuing to use Google, provided that Google had AI search functionality built into its search, we believe the user is more than likely to stick with Google, owing to intangible assets such as brand, and customer trust in the search quality that Google Search has built up over the years. In this example, the AI search engine would have to outmatch Google on either product capabilities, or additional features, which we think is highly unlikely over the long term.

Also, Alphabet is showing a greater proclivity to monetize AI-first search, with the firm now placing ads directly in AI Overviews and rolling out AI Overviews to more than a hundred countries after trying them out in the United States first. Ad agencies with a finite number of AI-first dollars to spend will likely show more interest in Google Search’s AI product versus a subscale competitor like Perplexity or even ChatGPT. This exercise, over time, can help Google Search better determine which ads to monetize in AI-first search, leading to an improved return on ad spending for advertisers and potentially resulting in a dynamic not dissimilar to what we see in general search right now.

Google Cloud a Long-Term Driver

Finally, we believe that despite the hype around chat-based AI-native search engines, the true value of generative AI search is likely to involve multimodal search. Multimodal search incorporates a variety of inputs, including photos and videos, and can answer questions on those inputs. Google’s Lens tool, which is primarily used for image inputs, has around 20 billion monthly searches, likely a multiple of the total number of queries a competitor such as ChatGPT fields. Moreover, multimodal searches open up new avenues of monetization, with management citing that roughly a fourth of Google Lens searches have commercial intent.

Moving over to Google Cloud, we were impressed by the solid growth Alphabet saw in this segment. Along with an accelerating top-line number, we also liked the the increasing profitability, with the unit’s operating margins expanding to 17% from 3% a year ago. We continue to view Google Cloud as a long-term value driver for Alphabet’s business and view it a long-term winner from an uptick in AI spending. Our view on Google Cloud’s competitive positioning, as it relates to the AI opportunity, stems from the principles of the commodification of complements.

In general, we view spending on generative AI and the public cloud as complementary goods. That is to say, as the price of former goes down, the demand for public cloud infrastructure should increase. We believe that public cloud vendors, including Alphabet, Amazon, and Microsoft, are all investing large sums of capital in training models while driving down their costs, with the express aim of making them cheap enough for customers to deploy them at scale. Evidence of this downward pricing pressure can be seen in Gemini, Alphabet’s large language model, with the firm driving down its cost per query by more than 90% over the last 18 months.

The return on these large capital investments should come in the form of increased public cloud spending, which we are seeing signs of already as we look at the recent growth trends for these public cloud vendors, including Alphabet’s Google Cloud.

The author or authors do not own shares in any securities mentioned in this article. Find out about Morningstar’s editorial policies.

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