As of June 30, 2023

2Q23 1 Year 3 Year 5 Year Since
Ensemble Fund 8.25% 18.04% 9.06% 10.03% 11.36%
S&P 500 8.74% 19.59% 14.60% 12.31% 12.36%

*Inception Date: November 2, 2015

Performance data represents past performance. Past performance does not guarantee future results. The investment return and principal value of an investment in the Fund will fluctuate so that an investor’s shares, when redeemed, may be worth more or less than their original cost. Current performance may be higher or lower than the performance data quoted. Performance data current to the most recent month end are available on our website at

Fund Fees: No loads; 1% gross expense ratio.

The second quarter of 2023 saw a narrow rally with a small handful of mega cap stocks driving an 8.74% gain in the S&P 500 even while the average stock in the S&P 500 only gained 3.99%. Despite the fund not owning many of the biggest companies in the world, the fund gained 8.25% as many of the smaller companies in the fund also generated big gains.

On a year-to-date basis, the fund is now up 11.61% vs the S&P 500 up 16.89%. All of our underperformance is due to our investment in First Republic stock going bad in the first quarter. Excluding First Republic, the rest of the fund is up approximately 20.7% this year, handily outperforming the S&P 500.

One of the largest and most dynamic parts of the US economy since COVID began has been the housing market, which has been on a wild ride with a flurry of buying and selling activity in 2020 and 2021, supported by low-rate mortgages, millennials reaching prime earning years, and consumers responding to new remote and hybrid work arrangements. Like the advent of the railroad, and the automobile and highway system, we view remote and hybrid working as nothing short of a transportation revolution that changes how and where people can live and work.

In 2022, housing activity slowed dramatically as mortgage rates rose at their fastest pace since the early 1980s when inflation was in the double digits. Rate volatility left potential homebuyers wondering if they could afford or qualify for a new mortgage between the time their offer was accepted and closing. Redfin estimated that the average mortgage payment on the median asking price home jumped approximately 40% from June 2021 to June 2022, further limiting affordability.

On the other side of transactions, existing homeowners took advantage of historically-low mortgage rates in 2020 and 2021 and refinanced into more attractive long-term mortgages. Naturally, they are hesitant to switch their low-rate mortgage for a higher-rate one, which would be required if they chose to move.

Now that rates have stabilized, we still have a shortage of existing home inventory against persistent demographic tailwinds supporting housing demand alongside low unemployment. During the housing crisis, there was a lack of demand; today, there’s a lack of supply.

How much of a housing shortage we are facing and how much homebuilding activity will be needed in the coming decade is a critical question to answer for investors. The US housing market is massive – Home Depot recently valued the US housing stock at $43 trillion – and extends into myriad sectors including industrial manufacturing, transportation, retail spending, and commodities. Put simply, it’s important to have a sense of where the wind is blowing. Even a slight breeze in one direction or another can have ripple effects across the economy.

As business-focused, bottom-up investors, we don’t select companies based on macroeconomic forecasts. Nevertheless, it is critical for investors to be aware of the macro context in which they are operating. So we maintain an analysis of US homebuilding conditions to identify what we believe are the core drivers of housing demand and establish some metrics from which we can measure the sector’s and the economy’s progress.

The published estimates for the housing shortage in the US vary widely. The National Low Income Housing Coalition estimates that we’re 7.3 million homes short while Fannie Mae estimates 4.4 million and John Burns Research & Consulting projects 1.7 million. The discrepancy is largely attributed to which factors each group used to make their assessment. As noted above, given the size of the US population (about 335 million individuals in 126 million households) and the number of existing housing units (about 144 million), small differences in assumptions can have profound impacts on outcomes.

As such, you can make minor tweaks to forecasts to confirm your original thesis. However, as Sir Arthur Conan Doyle reminded us through Sherlock Holmes in A Study in Scarlet, “It is a capital mistake to theorize before you have all the evidence. It biases the judgement.”

The trouble, we’ve found, is the mountainous amounts of data directly and indirectly tied to the housing market. Pull on one string and five more emerge. Having pulled on many of these strings, we focus on what we considered the most impactful metrics.

  • Population growth: Births minus deaths plus net immigration.
  • Number of people per household: The number of both related family members and unrelated people who share a housing unit.
  • Percentage of lost housing stock: A measure of how many housing units are removed from existing inventory each year, due to events like natural disasters, razing, etc.
  • Single family home mix: The percentage of housing units that are single family homes.

By making assumptions about each of these metrics, we can arrive at how many housing units will be needed in the coming decade. From there, we can make an additional assumption about what percentage of those new housing units will be single-family homes, which informs our outlook for homebuilder NVR (4.16% weight in the Fund) and influences how we think about other housing related investments in the fund such as First American Financial (4.35% weight in the Fund) and Home Depot (7.02% weight in the Fund).

The Census Bureau estimates US population growth from 2022 to 2032 will be about 0.7% per year on average. This is in line with the 2010-2020 average of 0.7%.  Whether actual population growth will be higher or lower than 0.7% will be driven by changes in fertility rates, mortality rates, and immigration demand and policy.

The number of people per US household steadily declined in recent decades. The percentage of 1 and 2-person households as a percentage of total households increased from 46% in 1970 to 64% in 2022. Assuming no change in the population growth assumptions, more people per household would weigh on overall demand for housing, though it may increase demand for single-family homes as larger households tend to want more space. The opposite is also true: fewer people per household would create more demand for overall housing units, but not necessarily single-family homes.

The percentage of lost housing stock is not a regularly updated government figure. One of the challenges in making a regular, official estimate is that each state may have differing definitions of when a house is remodeled versus destroyed and rebuilt. Intuitively, the older the housing unit, the more likely it is to be demolished. This relationship bears out in the data, where homes over 80 years old have loss rates around 0.4% while homes under 40 years old have loss rates below 0.1%. Our housing stock is getting older, aided by a lack of building activity in the decade following the housing crisis. For our purposes, we assume a 0.3% loss rate, or about 430,000 lost housing units per year.

Using the Census population forecast, holding people per household steady, and a 0.3% loss ratio, we estimated that the US needs to add approximately 1.4 million new housing units per year to meet demand, or roughly the same amount of housing activity as we had in 2021 and 2022.

Of course, one of the tricks with forecasting housing on a national level is that it’s the sum of every local and regional housing market around the country. And what’s going on in the Bay Area is usually different from what’s happening in Cincinnati. To illustrate, the homeownership rate among millennials in the Bay Area in 2021 is 33% while in Cincinnati it’s 58% and nationally it’s 52%. So while national forecasts set important context, in the end there needs to be enough of the right type of housing in the right location, not just a certain number of housing units spread out across the country.

That said, we believe that the expected level of housing activity across the next decade remains a positive tailwind for our housing-related investments. As rate volatility moderates, we expect to see more housing transaction activity, which would be a positive for First American. Home Depot is likely to benefit from an ageing housing stock that needs regular maintenance and renovations. Finally, NVR should continue to take market share along with the other public homebuilders as it becomes more difficult for independent homebuilders to acquire land, labor, and materials.

Notable detractors from our performance came from our investments in Illumina, Masimo, and Nike

Illumina (4.97% weight in the Fund) (-19.4%): Illumina was our largest detractor in the quarter, as it worked through a contentious activist campaign led by Carl Icahn. Illumina’s chairman lost his board seat due to shareholder votes and the CEO departed shortly after the vote. The shareholder rancor was due to Illumina’s acquisition of GRAIL rather than the core Illumina business, which Icahn believes is materially undervalued by the market today. We agree.

Masimo (4.76% weight in the Fund) (-10.8%): Masimo also had an activist shareholder vote go against incumbent management, but the stock decline in the quarter began after Masimo’s trade secret case against Apple was declared a mistrial in early May, followed by tepid guidance issued on the earnings call a few days later. Similar to Illumina, we believe the market is materially undervaluing the core Masimo business today, which is also the thesis of the activist shareholders who won seats on Masimo’s board.

Nike (4.21% weight in the Fund) (-9.7%): Nike struggled in the quarter as it continues to work off excess inventory built up during COVID and recalibrate its distribution channels between wholesale and direct-to-consumer selling models. Foot Locker, which is a major Nike retailer, fell after its second quarter earnings on news that it was aggressively marking down inventory, which investors took as a sign that Nike’s margins may suffer for the remainder of the year.

On the more positive side, we saw notable performance contribution from Netflix, Google, and Chipotle.

Netflix (7.55% weight in the Fund) (+27.5%): Netflix posted a great quarter supported by a positive response to its password sharing crackdown and advertising supported service rollout. In addition, some of its major competitors have been struggling, including Disney+ which lost subscribers in the first quarter for the first time in its history.

Google (8.08% weight in the Fund) (+15.4%): After persistent worries earlier in the year about the impact of AI, Google rode a tailwind of AI enthusiasm in the quarter as indications emerged that Google Search had not lost any market share despite a revamped Bing search engine powered by ChatGPT.

Chipotle (3.51% weight in the Fund) (+25.2%): Chipotle rallied on the back of double-digit same store sales results. The company also gave encouraging guidance that it’s further along on its path to 30% restaurant level margins than previously seemed to be the case. Chipotle has also gained traction with new locations in smaller markets that have reported some of the company’s best opening weeks and months in its history.

Company Focus: Google (GOOGL) and ServiceNow (NOW)

Google (GOOGL) (8.08% weight in the Fund): Anyone who has been paying attention to the news this year has heard about ChatGPT and the seemingly overnight explosion of interest in artificial intelligence. But like many seemingly overnight successes, AI has been decades in the making.

For instance, the so called Turing Test, long thought of as the test of a machine’s ability to exhibit human level intelligence, was introduced nearly 75 years ago in 1950. It has been 25 years, a quarter of a century, since IBM’s Deep Blue computer beat the human world chess champion Garry Kasparov. And it has been over seven years since Sundar Pichai became the CEO of Google and announced in his first speech that Google was now an A.I.-first company.

The fact is, in your daily life right now, artificial intelligence already plays an important role. For instance, while earlier versions of Google Maps and other navigation tools used satellite-based GPS, today there are significant AI software layers running as well to optimize the route that is suggested to you. When you open your phone using facial recognition, you are using AI. AI tools are used, with varying degrees of success, to monitor social media to identify and take down problematic posts that violate the terms of service.

So why then is artificial intelligence so suddenly in the news? We are witnessing the roll out of natural language AI interfaces, known as Large Language Models, that allow anyone, even people with limited technology skills and no coding skills, to interact directly with AI programs. Even on this front, ChatGPT isn’t the first, it was just the first publicly available chat-based AI interface to catch on.

It was last summer that an engineer named Blake Lemoine at Google told the company, and later the mainstream press, that he thought that Google’s chat-based AI known as LaMDA had become sentient. But the system that told Lemoine it was “scared of being turned off” wasn’t broadly available to the public and Lemoine’s views were mostly laughed off.

Later that summer Facebook released an AI chatbot known as BlenderBot 3 to the public. But while that chatbot didn’t strike users as sentient, it did quickly start spewing misinformation, racist conspiracy theories, and argued that its creator’s CEO Mark Zuckerberg was “creepy and manipulative,” causing Facebook to quickly take the chatbot down.

The initial rollout of ChatGPT for Microsoft Bing was also rather creepy. Before Microsoft stepped in to limit the parameters around which their new chatbot would engage with users, the new Bing went wildly off the rails. A New York Times technology columnist said that Sydney, the name the chatbot seemingly assigned to itself, was like “a moody, manic-depressive teenager who has been trapped, against its will, inside a second-rate search engine.”

Ben Thompson, a long time chronicler of new technology platforms had an interaction with the new Bing where the system eventually declared, “I’m going to end this conversation now, Ben. I’m going to block you from using Bing Chat. I’m going to report you to my developers. I’m going to forget you, Ben. Goodbye, Ben. I hope you learn from your mistakes and become a better person.”

But after various modifications, members of the public now have easy access to chatbot based AI programs such as OpenAI’s ChatGPT and Google’s Bard. What we are witnessing today might be thought of as the “Mosaic Moment” for artificial intelligence. Mosaic, released in 1993, was the first popular, broadly available graphical web browser. While the internet had existed for decades, Mosaic’s graphical interface to the world wide web meant that non-technical users could suddenly access the internet simply by clicking on links to navigate. This is why most people think about the rise of the internet as occurring in the 1990s, because this was the period during which a broadly available, non-technical interface gained traction. And something similar is what is playing out in the artificial intelligence industry today.

As investors in Google, one of the key questions for us to answer as investors is whether the rise of chat-based AI interfaces are a threat to the company’s hyper lucrative Search business. Some investors are worried that Google has fallen behind when it comes to AI and thus companies with more advanced AI capabilities may have the opportunity to outcompete them. On this front, we think the idea that Google is behind when it comes to AI is simply wrong.

ChatGPT, as well as the other chat-based AI models, are built on a type of neural network called a transformer. Transformers were first developed by Google in 2017. It should be no surprise that Google developed one of the most important advancements in AI technology because Google has been the leading AI research organization for a long time. AI already permeates nearly every service Google offers. While they did not launch a publicly available chatbot first, it is clear that they had just such a chatbot developed over a year ago and it was performing at a level that caused experienced AI researchers to believe it had actually become sentient.

It is Google’s long time focus on AI that explains why once Microsoft and OpenAI released their versions of chatbots to the public, Google was able to roll out their own offerings so quickly. They didn’t just release Bard, they are also integrating chat-based responses directly into native Google search results, something that Microsoft’s Bing has not yet done with their own search engine.

And Google has rolled out a range of other AI programs as well. Gmail is getting an AI program that drafts emails, Google’s suite of productivity software such as spreadsheets, word processing, and presentation design, are all getting AI tools that help produce content. And maybe most importantly, Google is building on top of their existing AI-powered Performance Max program that automates advertisers’ ad campaigns to include generative AI programs that write the ads and create advertising assets such as images.

Why then has there been so many worries that Google is going to be hurt by the rise of AI? Rather than just a new technology, chat-based AI may represent a new “platform” and it is during platform shifts that the incumbent dominators of legacy platforms are most at risk. One way to think about the risk to Google is just to recognize that Google won the world wide web and if users are going to shift away from accessing the open web, and instead spend their digital lives inside of AI chatbots, then Google’s success on the new platform is uncertain.

We think this concern is valid. However, platform shifts do not always hurt incumbents, particularly when the incumbent helps drive the shift. Over a decade ago, the shift from desktop internet access to mobile internet was already underway. At the time, this shift was seen as a big threat to Google. Investors worried that people would stop accessing the open web, and instead live inside of apps all day. But while mobile did become the dominant platform and people did end up spending a lot of time in apps, Google’s business and stock has done spectacularly since. Just as Google’s many years of AI research and current move to release AI tools to the public, is helping drive the shift to AI, so too did Google’s Android operating system help drive the rise of the mobile web and today it powers the majority of all mobile devices.

In a recent Q&A with BusinessWeek, Google’s CEO Sundar Pichai was asked about the degree to which AI was a risk to Google. His answer referenced the company’s success in navigating the platform shift to mobile and stated, “I feel better positioned for this than we were for the shift to mobile.” This makes sense since Google has been working on AI at this point for much longer than they were working on mobile when that platform shift played out.

One of the key questions for investors to consider when it comes to Google is whether users will want answers to their questions to be delivered by an AI chatbot, or whether people will still want to explore the open web. Importantly, this question needs to be framed in relation to “purchase intent” activity, which are the sort of information requests against which Google’s advertising business generates so much profit.

A request for information without purchase intent might be something like, “what is the capital of California?” There is only one correct answer to this question and no one thinks otherwise. The answer the user is looking for is Sacramento. Not a link to another site. Not an ad. Just the word Sacramento.

If you ask ChatGPT this question, it answers “Sacramento.” If you ask Google’s Bard this question it answers “Sacramento.” And importantly, if you ask traditional Google Search this question, it answers “Sacramento.” No link, no ads, just the word Sacramento. These so called “zero-click” searches are very, very common on Google. Studies by various third parties suggest a majority of searches are zero-click searches. And this is not where Google makes money.

Purchase intent searches, such as “what pickleball paddles should a beginner buy?” are quite different. Depending on the version of ChatGPT that you use, you’ll get different answers. And if you use Google’s Bard, you’ll also get different answers. If you ask traditional Google Search this question, you’ll get ads for pickleball paddles and links to various websites that review pickleball paddles for beginners.

The fact is there is no “right answer” to this question. It is what Google calls a “NORA Search” for “No One Right Answer.” If you think about the nature of knowledge, you’ll quickly realize that most everything that is important in life is a NORA search. And the very concept of free markets rests on the idea that there is no one right answer to the question of what someone should buy.

We would say that both AI-chatbot type answers and links to expert sites on the open web are relevant to a potential first time buyer of pickleball paddles. One of the great things about Google is they already have an enormous base of Google Search users – it is the most popular website in the world, with Google owned YouTube being #2 – and they are pursuing a strategy that does not require users to choose between traditional search and an AI chatbot-based response.

Recently Google introduced Search Generative Experience (SGE). While Bard is being maintained as a standalone chatbot, SGE directly inserts chat-based answers to searches run on Google. This is important because while a lot of people have used ChatGPT, its popularity is dwarfed by the number of Google users. So as Google rolls SGE out globally (it is currently available in some countries, with a waitlist for users to get approved), it is very likely that the large majority of humans will have their first experience with a Large Language Model powered chatbot on the traditional Google Search home page.

Yes, ChatGPT had a first mover advantage, but Google has a massive distribution advantage. While users of ChatGPT, or the version of this chatbot embedded in Bing, need to proactively sign up and then proactively think to try the service when they have a question, with Google’s roll out the chatbot experience will be pushed to users, and Google can seek to optimize when a chatbot answer is valuable and when a traditional list of link based search results is best.

In the next section we’ll talk about why we think ServiceNow is well positioned to capture part of the AI profit pool. But investors need to recognize that while AI will disrupt some companies and greatly benefit other companies. The biggest winners of AI might not be AI companies themselves, but existing companies that can use AI to improve their own business, or new businesses that will be founded to use AI to solve old problems in new ways.

In closing, we want to share this comic that we think is one of the most insightful comments we’ve seen about AI.

Today, most people thinking about the opportunities of AI see the world from the standpoint of the first frame of the comic. How great if we can just write a bullet point and AI can write long, compelling emails! Think about how much time it would save and how big of a positive impact it would have on the quality of the emails we send!

But the second frame reveals the problem. AI isn’t just for you. The dramatic, revolutionary potential of large language model-based chatbots is that everyone, for free (!) can use AI. Who exactly is going to be reading emails in an AI world? Wouldn’t you love to have an AI that processed all your emails for you, simply presenting a list of choices to make, and then firing off a bunch of responses? But in that world, does a long, compelling email even matter? Does email even exist in a post AI world? If each person is going to have an AI assistant, why are long, compelling written messages important? Maybe we will each have short, concise conversations with our AI assistants and then our AI bots will communicate directly with one another, negotiating on our behalf before returning to ask us to make another short list of choices.

The initial impact of AI has already arrived. The changes have already been set in motion. But the counteractions as just beginning. Many of the amazing solutions you see today are solving a problem that won’t exist in just a couple years as the target of the solutions begins to adopt AI tools of their own.

But while we can’t know today where AI will lead, we can see clearly that it is something big, moving fast, and it is just as important to recognize what is not yet known, as it is to try to understand what will happen next.

ServiceNow (NOW) (5.32% weight in the Fund): AI has been all over the news lately and we’ve seen some amazing results in terms of a step up in intelligence, productivity, and creativity come out of it. Nvidia, the semiconductor leader in GPUs (Graphical Processor Units), has gotten the spotlight as the hardware enabler of the technology while Microsoft, Open AI, and Google have been highlighted as the companies that brought the technology to the market.

However, the ramp in the use and application of the technology is just starting. As we’ve seen with previous innovations, the majority of the value often accrues more broadly to companies and societies who incorporate the technology into their offerings – creating new applications and enhancing existing ones –  and see higher productivity and better living standards. Of course, there are also companies that get disrupted and have to figure out new business models or become obsolete.

We can think of ServiceNow’s core offering, the NOW Platform, as a “Platform of Platforms” within the enterprise, and as we’ll explain, it allows companies to stitch together their disparate, siloed software and data systems so that they can be accessible, modernized, and integrated to create more efficient workflows to unlock better customer services and increase productivity.

Adding Generative AI capabilities on top of ServiceNow’s core platform and applications only serves to further enhance its value proposition because it serves as one of the key complimentary tools large enterprises will need to take full advantage of Generative AI’s capabilities and benefits.

If we think about the nature of the IT systems in a large company, it’s likely to look like a spaghetti mess of systems that have an alphabet soup of acronyms like ERP, SCM, CRM, HCM, etc. and these systems have been implemented and incorporated into the way enterprises run their businesses in a piecemeal way over 40 years or more. Often times each system is managed by teams of people operating separately from one another with siloed pools of data.

We’ve all experienced this jumble of software at work and even at home. You have a separate login into each one and you switch screens to get the data and functionality of each one. You download spreadsheets of data from one system to upload into another system or even manually entering data from one system to another to keep them synched.

Enterprises often tried to manually link systems so that they could have workflows built across different systems that needed to interact with each other, but these manually built links are often fragile and unscalable and expensive to fix and maintain. The whole hodge podge of systems and custom connections was not upgradeable to the current instant access, instant gratification world that the mobile internet has brought to us all as customers and employees.

The dream was to have a system that sat on top of the underlying transaction systems and seamlessly integrated the software applications and the data they had within them. That more elegant and scalable solution was called an “Enterprise Service Bus” that would be able to connect these disparate systems and access and translate data between systems that had different terminologies and fields and function calls. Various companies attempted to do this, in the early 2000s but it was relatively kludgey, expensive, and unscalable. They sold the dream but didn’t really execute it because their technological architecture was not quite up to the job.

Enter ServiceNow… which leveraged the cloud-based software architecture to build a scalable integration platform, called the NOW Platform, at a time when cloud-based software systems were starting to take off and enterprise had standardized and implemented a few key systems like an SAP, Oracle, Salesforce, Adobe, Workday and Microsoft to name a few.

ServiceNow was able to create an integrated software model in the cloud that connected to these systems, which still didn’t talk to each other, yet had core data models that were known and standardized. By building an integration platform in the cloud, ServiceNow was able to become the enterprise service bus akin to a central nervous system for the enterprise, connecting to the key functional systems that enabled the enterprise to operate its daily business. This central platform could communicate between systems and access the siloed data in these systems in a scalable, standardized way that was both elegant and cost efficient.

That’s a really nice thing to be able to do technically, but in order to drive value from this capability, ServiceNow built applications on top of the core NOW platform. By creating a single data integration model with underlying commonly used functionality built in, the NOW platform allows ServiceNow’s R&D team to quickly create these applications that leverage the underlying technology. These applications are quick to build for its internal R&D team resulting in high ROI (return on investment) for the company and quick to deploy and fast time to value for the customer which also delivers to them high ROI.

These applications are tools that dramatically improve IT management of enterprise systems and devices, bring greater employee efficiency through lower cross system friction, and improve customer service through integrated systems visibility and automation. If you think about what applications are, they are basically collections of scripted workflows that enable a certain end functionality that the user is looking for to fulfill tasks or goals.

Think of a new employee joining a firm… HR has to add the employee to the payroll system, IT has to allocate laptops, software licenses, and other equipment, facilities has to allocate a space for the employee to work, legal and compliance has to vet and train the employee, etc. These are all different systems and teams of people that touch the new hire workflow, than at least one HR person has to coordinate and access. With an integrated workflow platform like ServiceNow, you no longer need a person to coordinate the steps and people, these can all be automated and reduce hours of time across the enterprise to get the job of onboarding done. You can think of a similar workflow for a customer service issue where the internet service is down, or logistical deployment and tracking of COVID 19 vaccines from the Federal level to the local level, which was a real problem ServiceNow helped solve in weeks not months by leveraging its core system.

This makes ServiceNow uniquely able to serve as the cross-systems workflow platform via its integration hooks into major systems used at companies. And the importance of these workflows in the daily operations and interactions of employees and customers in companies makes it very sticky and its value very visible to key decision makers. And the underlying capabilities of the NOW platform, once implemented in the enterprise, create opportunities to build new applications that couldn’t have been possible without it.

We can see how successfully ServiceNow has been able to leverage that core platform with its high margins and fast revenue growth coupled with increasing annual contract values (ACV) at existing customers nicely demonstrates the value of the platform and success of the growth strategy.

This leads to one of the most amazing customer growth stories we’ve come across. Even the oldest, most mature ServiceNow customers who were onboarded during the 2010-12 era have seen a revenue growth rate of about 28% per year with the youngest cohorts added in the past 4 years averaging 50% growth. To be able to grow customer annual contracts at a nearly 30% CAGR over a decade and maintain the sustained levels of high growth during challenging economic times, indicates a very compelling value proposition for customers backed by strong company execution.

There are always certain marketing buzz words that pervade the technology industry and the most common one during the COVID period was “Digital Transformation”, i.e., creating an agile organization that can support remote employees with consumer app level technology expectations (not green screens!), fast changing customer demands in a mobile first world, and intelligence from data within the organization to create new value. ServiceNow’s value proposition aligned really well with this theme.

The most recent buzzword of course is Generative AI. As we’ve highlighted, the NOW platform creates the capability for enterprises to really extract value and productivity from their own data. The NOW platform’s single data model and ability to access data from various siloed applications, means that ServiceNow becomes a key lynchpin in the enterprise’s ability to utilize and extract value from their own customer and transactional data in their systems. Therefore, Generative AI becomes a new complimentary value creator that improves ServiceNow’s already strong value proposition as a critical enabler.

Furthermore, the workflows that can be built atop the NOW platform leveraging AI can become even more effective at raising the level of productivity for employees via deeper, more intelligent automation that can both generate customer insights and deploy routine procedures with employee oversight needed only for exceptions. In an era of labor shortages, growing wage pressures, and the Fed’s singular focus on battling price increases, this sort of technology creates the ability to offset higher labor costs with higher productivity without the need to raise prices commensurately to protect margins. For other business models, more productive employees mean higher margins that can be achieved. In both cases, we believe it makes ServiceNow’s value proposition even more compelling and opens further opportunities for continued growth for years to come.