Alphabet, Amazon, Microsoft, Meta - all of them announced their quarterly earnings in the last couple of weeks. Net profit margins dropped across the board, almost uniformly. The market analysts anyway had limited expectations - One indication of this is that on the day of Meta's earnings call, its stock price jumped ~20% because the revenue did not fall as expected.
Although these platforms are taking steps to cut costs in order to rationalize operational and capital expenses, their path ahead to maintain their distribution moats is not as straightforward. The key dilemma here is how to take advantage of some of the generation-defining opportunities that may arise in the near future - which by all means can be expensive in the short term. Most of us are aware about the expensive play-books that these companies have run to ensure their stronghold over distribution.
For eg. Alphabet pays Apple a whopping ~US$20B annually to ensure that Google Chrome is the default browser on Apple devices.
Microsoft executes its bundling strategy with finesse, making high-quality companies seem like features in comparison to its suite of products (Remember Slack vs. Teams - Microsoft won that war a long time ago - They offered the product almost for free!)
These companies encounter a diverse array of opportunities (as well as challenges), ranging from potential disruptions in the search business model to the emergence of lucrative acquisition prospects. To maintain their competitiveness, especially in controlling distribution, these platforms must remain adaptable and constantly search for new ways to innovate and expand. However, this process is likely to be costly. Moreover, the financial performance of these companies is currently lackluster. Figures relating to revenue growth and profitability are less than impressive, which exacerbates this dilemma.
Alphabet
In Q4'22, Alphabet's revenue increased by 1% YoY. However, the company's overall net profit margin decreased significantly from ~27% to ~18%. The Search business that represents ~56% of the revenue saw a decline of ~2% YoY. The losses from other bets continued to increase. These bets included initiatives such as Deepmind. In an effort to control costs, the Alphabet team announced a reduction of approximately 12,000 jobs and a change in the useful life of their servers and network equipment costs. In January 2023, Alphabet completed an assessment of the useful lives of their servers and network equipment. This resulted in a change in the estimated useful life of their servers and certain network equipment to six years (from five years). They expect this change to reduce depreciation by approximately $3.4 billion for the full fiscal year 2023 for assets in service as of December 31, 2022. The reduction will be recorded primarily in the cost of revenues and research and development (R&D) expenses. Google indicated that it will take time to execute on cost rationalization, more clearly seen in 2024 (than in 2023).
Meta
Meta's revenue for Q4 '22 declined by ~4%, and its net profit margin dropped from ~32% to ~14%. To optimise costs, Meta has lowered its Capex guide for 2023 from US$34-37B to US$30-33B. This reduction in capex reflects their updated plans for lower data center construction spending as they shift to a new, more cost-efficient data center architecture that can support both AI and non-AI workloads. The current capex is due to building out AI infrastructure, and future capital expenditure outlook will depend on the ROI of the AI projects. The best manifestation of the dilemma (of either being the Best Technology Platform vs the Most Efficient Technology Platform), which we will discuss later in the essay, is represented through Zuck's statement below.
“And I do want to continue to emphasize the dual goals here of making the company a better technology company and increasing our profitability.
I think it's also really important to focus on the first one of just making it a better company because that way, even if we outperform our business goals this year, I just want to communicate, especially the people inside the company that we're going to stick with this”
Amazon (AWS)
AWS experienced a significant decline in year-over-year (YoY) growth, dropping from ~40% in Q1 to ~20% in Q4'22. The margin profile (net profit margin) also significantly degraded from ~30% to ~24%. While one of the core values of cloud solutions is their ability to scale up or down as needed, this level of cyclicality is sort of unprecedented, resulting in a high deceleration of growth. One particularly noteworthy metric is the incremental operating margin, which has been shockingly dropping. In Q4 '22, incremental operating expenses exceeded incremental revenue, indicating that AWS may have overestimated demand. Andy Jassy - Amazon’s CEO - also reiterated the same thing in his defence during the earnings call.
“It’s one of the advantages that we’ve talked about since we launched AWS in 2006 of the cloud, which is that when it turns out you have a lot more demand than you anticipated, you can seamlessly scale up. But if it turns out that you don’t need as much demand as you had, you can give it back to us and stop paying for it. And that elasticity is very unusual. It’s something you can’t do on-premises, which is one of the many reasons why the cloud and AWS are very effective for customers.”
While Capex intensity has nearly doubled over the past five years, it is difficult to draw any conclusions from this, as much of it may be due to other investments whose economics are difficult to ascertain.
I think at this point it’s safe to say that most of the large platforms are facing efficiency challenges, and these are likely to persist. So what exactly is the conundrum?
The potential imploding dynamics of Search Business | Distribution vs Margins
All of us are fairly attuned with Microsoft’s partnership with Open AI to integrate Chat GPT (and its other products) into its gamut of offerings including Bing. As we had discussed earlier, the search business represents roughly ~56% of Alphabet’s revenue and even limited change in share could lead to increase in Microsoft’s Bing revenue by a couple of billions of dollars. Most of the people have seen the tweet about Satya Nadella talk about making the Gorilla (aka Google) dance. However, as analysts of markets and businesses, we should be aware that this will have a significant impact on gross margins (There’s a reason we don’t quite often see a Gorilla dance, coz it’s freakin expensive!). This is not just for Microsoft, but also for Alphabet if it chooses to integrate ChatGPT, like LLM, into its search capabilities. The real choice here (for platforms such as Google or Microsoft) is to prioritise either “Defending the Distribution”, which may adversely impact the margin dynamics in the short to medium term, or “Focusing on protecting the Margin Dynamics”. The reality is that both goals cannot be achieved within a short time frame.
The process of feeding data points into a machine-learning model to generate an output is known as Machine Learning Inference. However, the cost of inference far exceeds the cost of training a machine learning model. In the case of ChatGPT, the inference costs exceed the training costs on a weekly basis. Dylan Patel has done a great job of estimating the cost of integrating chatGPT-like LLMs into search. He estimates that doing so (as it is) would cost anywhere north of US$30B. To put things in perspective, Google's net annual income would drop from US$55.5B to US$19.5B. Obviously, this is not going to happen, but it does imply that the gross margins of the Search business are likely to be impacted in the short to medium term. There was a slight hint of exorbitant inference costs for ChatGPT when Elon Musk asked about the cost of running ChatGPT and Sam Altman, CEO of OpenAI, responded by saying a couple of cents per chat.
Google runs approximately 320,000 searches per second. In 2022, its search business segment had a revenue of ~US$162 billion. This means that, on average, each query generates 1.61 cents in revenue. Google incurs huge expenses for computing, web crawling, model deployment, and other services. Google's services business unit enjoys a relatively high operating margin of ~34%. When this margin is applied on a per-query basis, the average cost to run a query is ~1.06 cents. Therefore, the cost of a query with an LLM must be significantly less than 0.5 cents; otherwise, the search business will become unprofitable. Implementing an LLM hastily would increase this cost to 1.42 cents per query, thereby completely altering the margin dynamics for the search business. On top of this, basis Dylan’s model, deploying ChatGPT into Google would require ~512,820 A100 servers with a total of ~4.1M A100 GPUs. The cost of these servers and networking equipment alone exceeds ~US$100B. In hindsight, Satya Nadella's (below) statement during the earnings call makes a lot of sense.
Obviously, one can argue that not all searches are suitable for a chat interface (One may want recommendations in image forms if they are searching about touristy places!). However, the focus is not on estimating the exact potential margin impact but rather on how to protect the margin profile / net income. Companies such as Alphabet will need to react to ensure that the margins of their search businesses are not affected in the long term. Below is a basic scenario analysis of the impact on gross margin based on the percentage of searches conducted through a chat format.
There is another issue to consider: as search quality improves, there is a probability that search volume may decrease. This is because LLMs may be able to provide exact responses to user queries, which could potentially reduce the number of search queries in the first place. Additionally, deploying ads in a chat format is very challenging. With so many issues, it is not surprising that Google has been cautious in its launch of Bard, its own LLM-powered search engine. Bard is built on a significantly lightweight version of LaMDA and requires much less computing power - hence, lower inference costs. This allows them to protect their margins, as they cannot afford to deploy really large models that would significantly erode their search earnings. There are two implications of going with this decision (of going with the lightweight model)
Lower Latency (i.e. coming with output quickly, where Google has an advantage): Most LLMs experience significant latency in generating output. Models trained on larger batch sizes, while still maintaining efficient GPU utilization, are likely to suffer from even greater latency. Google’s Bard would likely have an advantage in this regard - compared with ChatGPT.
Comprehensiveness of Answers to Queries (where Google is at a disadvantage): Sequence length refers to the amount of context that an LLM can analyze. For instance, ChatGPT’s architecture allows it to review up to 2000 to 4000 words to build the context. Based on all estimates, the sequence length of Bard is approximately half of it, which implies that it is not as comprehensive.
In summary, Google has an advantage in quickly providing answers, but Microsoft Bing's ability to process more information potentially makes it more comprehensive (doesn’t mean it’s more accurate!). Moving forward, Google's goal is to provide highly accurate search results, process more information, and deliver answers with limited latency, all while maintaining healthy gross margins. The jury is out on how long it will take Google to achieve all of these goals, particularly given that some of these goals are significantly divergent. Having said that It’s never a good idea to underestimate Google - My sense is that there will be margin pressure and an increase in capex commitments in the short to medium term. However, claiming that Microsoft has completely changed the game seems far-fetched.
The end of zero interest rates creates opportunities for acquisition for Platforms
When it comes to cloud, most of the 1st phase focused on migrating workloads to the cloud, Phase 2 is now all about managing the cloud operations - securing, operating, and managing the cloud to make it more efficient. As spending becomes more rationalized, enterprise clients may try to consolidate their software vendors. Large hyperscalers, such as AWS, GCP, and Azure, may pose significant platform risks for companies built on top of them. The mentality (of hyperscalers) to cater to 80% of problems of 80% of the customers gave rise to really large software companies. The hyperscalers never bothered due to the inherent tailwinds towards cloud migration (even if that meant leaving large value pools to relatively smaller companies). However, the shift towards the end of zero interest rates has the potential to change the ball game.
Long-term valuations are a function of discounted rates. When interest rates rise, discounted rates increase, resulting in lower potential valuations. Most SaaS companies rely on the services of cloud providers such as AWS, GCP and Azure. Companies without a product-market fit are likely to close, while those that have one are likely be asked about the size of the market opportunity. All these factors combined together do present a lucrative opportunity for these large platforms for high-quality acquisition prospects (at a much sober price). If platform risks are indeed real, then this maybe one of the years in which it can play out.
Demand side softness for small and large companies is taking many forms. For instance, software sales cycles are now longer, and more approvals are required to close deals. Discounts are also becoming more prevalent, and payment terms are more flexible, which will negatively impact cash collections. For software companies with usage-based models, customers are trying to ration usage. Companies, which now have less capital and are operating at a higher cost, will need to either rationalize costs and "do more with less" across the board in order to survive or get acquired.
Case Studies on Dropping Valuations
At this point, I would be stating the obvious if I were to explain the chart below, which shows the percentage decline in NTM revenue multiples of public software companies. But even if we were to take Snowflake as case study, which by all means was a darling of public markets and trades at the highest multiple of any SaaS company - with ~US$1.5B of ARR, growing at ~102% YoY at that scale, Net Dollar Retention Rate of ~178%, Magic Number of 1.3, and Rule of 40 of ~109% - the multiples are down by ~73% from the peak.
Miro is another interesting case study - As of October 2022, the company has raised $476 million in cumulative funding from firms such as Accel, Atlassian, and Salesforce Ventures. Their Series C funding resulted in a valuation of $17.5 billion, which represents a multiple of 58x their 2021 revenue of approximately US$300 million. In the public markets, collaboration companies such as Zoom, Asana, and Monday.com have seen their revenue multiples collapse from a high of 119x, 89x, and 84x, respectively, to their current levels of 3.5x, 6.5x, and 5.7x as of October 2022. If Miro starts to experience a slowdown in growth similar to what other remote work companies have experienced, its high valuation will be called into question.
Good Tooling is No Longer Enough: Enterprises Need a Bundled Strategy
Battery recently concluded a survey of top CXOs across large enterprises to understand the spending priorities of companies and one of the interesting takeaways was that while bottoms-up technology adoption is still happening, there are some interesting nuances - Although the hurdle to enter the enterprise sandbox is relatively low, it is considerably more difficult for new technologies to expand into the production environment.
~75% of the enterprises allow developers to self select the tools for dev/test environments
But only ~14% allow such procurement in the production environment
Moreover, most CXOs are interested in streamlining vendor usage rather than reducing headcount. Those companies that can execute the bundled strategy well may have a strong advantage - In cases like these, platforms would have an undue advantage over companies that specialize in specific problem-solving.
Based on the observed behavior, I anticipate that following outcomes are likely to occur in one way or another. We will revisit this at the end of the year to see if any of these events have occurred.
I believe that the Gross Margins and Operating Margins of these platforms will be negatively affected in the next two to three quarters before stabilizing (especially for companies such as Alphabet). While the impact may not be as significant as the math we did above, there is likely to be some downward pressure on margins. The improvement in margins over the long term will be driven by both hardware and software improvements. While Google’s TPU (hardware) is generally considered better for inference workloads and can reduce the cost significantly, software improvements such as Sparsity, Pruning, Confident Adaptive Learning Model (CaLM) can lead to additional improvement by magnitudes. Unlike many market commentators, I personally believe that Alphabet's response to Microsoft so far has been measured in a good way. With the pressure to rapidly roll out products, companies are forgetting to perform basic due diligence on the output that their models produce. In contrast, Alphabet's response has been more responsible. It’s another story that Google had a PR disaster (and Microsoft did not) due to erroneous outputs of their Models.
Yes, the tailwinds for lucrative acquisitions exist, however these platforms - and even large companies - are likely to be cautious with their strategies, given that many have revised their forward-looking capex estimates downward. I believe they will wait for efficiency metrics to kick-in before prioritizing acquisitions in categories where they believe large value pools can be accrued (and enterprise clients are looking to bolster their spending) and consider those categories as part of their future growth strategy. For eg. Acquisition of a Cybersecurity company by a Data Observability Platform such as Datadog would make a lot of sense given the whole shift left movement of Cybersecurity during the software development process. Below are a couple of areas, where CXOs of large enterprises want to spend a significant amount of budget and resources.
Additionally, companies built on top of hyperscalers, such as AWS, GCP, and Microsoft Azure, may collaborate on co-selling engagements together. For example, Elastic, a data observability platform, has been strengthening its ties with cloud providers. Following is the excerpt from its earnings call touching the same subject
“Like other independent software companies, Elastic has been deepening their relationships with the hyperscalers recently. A few years back, this was not the case, as the hyperscalers (particularly AWS) rolled their own versions of popular open source projects. Partially due to licensing changes, but primarily due to customer preferences, the hyperscalers have reversed their competitive positioning with Elastic and now embrace the Elastic sales team in co-selling engagements. Elastic has forged strong partnerships with AWS, Azure and GCP at this point.”
Please feel free to reach out if you would like to discuss anything. I will try to respond within 48 hours.
It goes without saying that all opinions expressed are personal! 🙂