Baron Technology Fund | Q2 2024

During the second quarter, Baron Technology Fund® (the Fund) rose 7.09% (Institutional Shares), underperforming the MSCI ACWI Information Technology Index (the Benchmark), which increased 11.38%. The Fund outperformed the broader S&P 500 Index, which rose 4.28%. For the first half of 2024, the Fund posted solid gains, increasing 22.86%, slightly underperforming the Benchmark, which rose 24.80%, but materially beating the S&P 500 Index, which appreciated 15.29%.

U.S. equities endured a slow start to the quarter before rising steadily in May and June. Early market weakness was attributed to heightened concerns about inflation, the pace of anticipated Federal Reserve rate cuts, and rising geopolitical tensions in the Middle East. The sell-off proved short lived, however, with the NASDAQ Composite and S&P 500 Indexes hitting new all-time highs on several occasions over the remainder of the quarter, supported by better-than-expected corporate earnings and mixed economic data suggesting inflation continues to moderate.

The Magnificent Seven and AI remained the dominant drivers of market returns. The Magnificent Seven group, which consists of Alphabet Inc., Amazon.com, Inc., Apple Inc., Meta Platforms, Inc., Microsoft Corporation, NVIDIA Corporation, and Tesla, Inc., appreciated 16.9% for the quarter, accounting for all the gains in the NASDAQ Composite and the S&P 500 Indexes. For the first half of 2024, the group rose 32.3%. We believe the Magnificent Seven’s dominance stems from a perfect storm-like combination of factors, particularly: (1) a market environment still wrestling with macroeconomic, geopolitical, and political uncertainties, where apprehensive investors buy the perceived obvious winners and safest stocks first; and (2) the recognition that AI is the most powerful technology platform shift and secular growth driver since the advent of the internet itself, and that in this AI, cloud-connected, digital-first world, the strong tend to be best positioned to capitalize on these trends and become even stronger. More on that below.

Fund performance was a mixed bag for the quarter, and we underperformed the Benchmark during the period. Our overweight positions in AI stalwarts Broadcom Inc. and Taiwan Semiconductor Manufacturing Company Limited contributed to the Fund’s relative performance, as their stocks rose 22% and 28%, respectively. Our overweight position in Spotify Technology S.A., the world’s most popular music and audio streaming service, was another contributor to relative performance, as its stock rose 19%.

The Fund’s chief relative detractor was Apple Inc., even though it was a meaningful contributor to absolute performance, as we added to our Apple position significantly during the period. We bought Apple well, but in 20/20 hindsight we didn’t buy enough. Because Apple has an oversized weight in the Benchmark (its average weight was 15.7% for the period), when Apple’s stock outperforms (it appreciated 23.0%), it has generally been a headwind to relative performance. Our Apple underweight accounted for 33% of our relative underperformance for the period. The Fund’s investment in CoStar Group, Inc., a leader in real estate information and marketing services, performed poorly during the quarter (down 23.4%), and our overweight position in CoStar accounted for another 31% of our relative underperformance.

We read a lot and listen to a lot of technology and market-related podcasts, and the two questions we keep confronting are (1) Are we in an AI bubble? and (2) Is software dead? We’d like to provide our investors with a summary of our take on these issues.

AI hype

We do not dispute that there is some hype around AI and the perceived AI winners. History teaches that there is always a hype cycle around significant technology disruptions–initial euphoria, a short period of doubts and questions regarding the significance of the new technology, and then the measured reality of the impact the platform shift is having. We have communicated in these letters and Baron Insights publications that we believe AI is the most significant advancement and technological platform shift impacting our now-digital world since the advent of the internet itself in the mid-1990s, some 30 years ago.

We have been experiencing almost a classic hype cycle over the last 18 months following the ChatGPT moment in late November 2022. Most of this time has been a period of euphoria prompted by the initial introduction and adoption of AI consumer and business applications, announcements and public data releases regarding the improvements in large language models (LLMs), as well as the historic inflection in sales of GPUs,1 otherwise known as accelerated computing chips, as reflected in the financial results of companies like NVIDIA and Broadcom Inc. More recently, however, we’ve entered the period of doubts and questioning, some of which is real and normal in the first stages of a new paradigm, and some of which is prompted by short sellers. Given the explosive returns of NVIDIA and other AI leaders, AI bears and fear mongers have been comparing the current AI market winners with the internet bubble of the late 1990s/early 2000s, and NVIDIA’s stock move today with Cisco’s back then. First, while many stocks were trading at nosebleed valuations and on made up metrics (such as price per eyeballs) before the bursting of the internet bubble, as we’ve said many times, the internet proved to transform our world and create the digital age we are now living in. Second, while NVIDIA’s stock price inflection has been nothing short of unprecedented for a company of its size, it was fueled almost entirely by explosive growth in revenues, earnings, and cash flows– not multiple expansion. Over the last 12 months, NVIDIA’s stock has effectively tripled, but its forward P/E multiple has remained essentially flat, because NVIDIA blew away Wall Street expectations despite being covered by over 60 sell-side analysts, who have increased their forward projections every single quarter. In my career, the only comparative analogue is when Apple first introduced the iPhone and stunned Wall Street with its growth. In contrast, most of Cisco’s move in the late 1990s was due to multiple expansion. At its peak, Cisco traded at a P/E ratio over 130 times, more than quadruple its five-year average of 37 times. At the end of the second quarter, NVIDIA traded at a P/E ratio of 40 times, equal to its five-year average, and at a P/E to growth (or PEG) ratio for 2025 of 0.8 times, as consensus expectations are for NVIDIA to grow earnings per share 40% next year.2

Moreover, investor concerns have arisen about the financial impact AI is having and whether surging capital expenditures (capex) across the technology landscape, particularly the large cloud players (Microsoft, Google, Amazon, and Meta), known as the hyperscalers, will be justified and earn reasonable returns on invested capital (ROIC). First, the adoption and penetration of new technology typically traces a classic S-curve–or more precisely, in our view, a series of S-curves or phases. For at least the past year and a half, we’ve been in what might be called the AI infrastructure- build phase – building the AI factories, as NVIDIA CEO Jensen Huang has articulated it,3 and this phase has been dominated by the infrastructure- layer players – the accelerated computing chips suppliers like NVIDIA and Broadcom, as well as data center, cloud infrastructure and energy companies. The hyperscalers, other enterprises, and sovereign entities investing ahead understand that if you want to be in the AI game, you must invest now – build the infrastructure, build the factories – or else you’ll find yourselves disrupted on the sidelines or playing catch up in the biggest game, the most important race in a technology generation. Only those who invest today even have the chance to be the winners of the future.

While AI technology is new, the investment paradigm is not – upfront investments followed by long-term returns. All AI services of the future will require an AI factory, whether you own or rent one. The four hyperscalers mentioned above, among others, are leading the charge to build these AI factories The four are expected to spend almost $200 billion on capex this year, a 32% increase over the amount the group spent last year. If you exclude Amazon, which doesn’t break out its data center capex from its fulfillment capex, the growth rate is 40%.

At Baron, we have experienced and understand that valuing and calculating the expected returns of a growth opportunity, like AI, which requires heavy investment, can only be done by examining and projecting the long-term opportunity. We are in the earliest, almost preliminary stages, of what might be called the AI application phase. This phase – like the early days of the desktop or mobile internet eras – will take years. As in every prior technology generational shift, some early applications will have an immediate measurable impact, while many applications will fail, and others will require iterations and not be ready for prime time until the 2.0 or 3.0 release. Most companies are still in the proof-of-concept stage while very few are ready for production today.

AI is developing rapidly across industries. Near term, there is a lot of excitement around AI for areas such as consumer chatbots, AI-based customer service, AI-based assistants for a variety of business tasks such as coding, marketing, back office, and more. A handful of AI applications and use cases have already yielded measurable impacts and ROIC. For example, in software development, AI services (from companies like Microsoft, Amazon, and GitLab) – such as code writing, revisions, documentation, vulnerability inspection, etc. – have provided meaningful productivity improvements, with reports of 30% to 60% improvements in developer efficiency.4 In customer service, generative AI chatbots can handle up to 80% of routine customer inquiries, freeing up human agents to focus on more complex issues, and saving companies 15% to 30% on their customer service operating costs.5 For the consumer internet players (like Meta, Google, TikTok), their AI investments have improved their core content and advertising platforms – algorithm and bid-rank improvements, more accurate targeting models, increased video engagement, dynamic ad insertion, and more – and have generated impressive ROIC.

Looking forward, published general economic studies have shown that up to $2.4 trillion dollars in capitalized AI investments could generate a 25% ROIC with either operating expense reductions amounting to 5% of global skilled payroll or 3% of global total payroll or revenue generation at levels of 3% of global public company revenues or 2% of global GDP. On the operating expense side, for example, eliminating one software developer would provide up to $250,000 of value or cost savings; cutting one knowledge worker out of a team might accrue up to $150,000. From the revenue generation angle, application-specific sell-side reports have demonstrated that, even at today’s pricing levels for AI services such as Microsoft Copilot or Azure AI Cloud, the returns on capital deployed and operating assets are material, though perhaps not as high as current generation cloud software or infrastructure services.6 Moreover, on NVIDIA’s May earnings call, CFO Colette Kress boldly claimed that for every $1 spent on NVIDIA systems, a hyperscaler could “generate $7 in revenue over four years.”

To repeat, we believe we are in the earliest stages of a multi-decade disruption. Longer-term avenues of development are broad and include drug discovery, in which the opportunity for AI is significant due to the long timelines for drugs to reach approval and the high probability of failure (90% of drugs fail); planning and running factories and supply chains using digital twins and AI simulation; and using AI to build robots across a variety of use cases (from autonomous machines to autonomous driving to humanoid robots). Multi-domain, multi-industry disruption.

When one considers where we are in AI today, and where we might be in a few years, one cannot ignore the pace of improvements we have already witnessed. The Chief Product Officer of one of our software investments, who is leading that company’s AI developments, told us on a recent Zoom that it is “incredible how quickly the AI models are improving.” I will just highlight a few.

• Accelerated computing chips: At NVIDIA’s June COMPUTEX conference appearance, Jensen Huang presented slides showing that AI compute had improved 1,000 times over the last eight years and energy use had improved 350 times.7 NVIDIA’s recently introduced Blackwell family of chips can produce performance improvements of up to 4 times faster for training and 30 times for inferencing compared to the prior Hopper generation. Blackwell can deliver 25 times lower total cost of ownership and energy consumption than Hopper, as well. The new Blackwell architecture provides the ability to combine a significant number of GPUs into a “single” large GPU (namely thanks to NVIDIA’s networking capabilities), and the company’s investor relations commentary stated that they expect Blackwell to be “the new unit of compute.”

• LLMs: AI LLM’s algorithms are rapidly improving as well. For example, the price of OpenAI’s GPT-3 (which cost $20 per 1 million tokens in early 2023) declined 95% with the introduction of the more capable GPT-3.5 Turbo, which costs 95% less at $1 per 1 million tokens, despite being a better model. In a recent AI publication,8 the author presented his views and evidence that “[t]he pace of [LLM] deep learning progress in the last decade has simply been extraordinary.” He argued that OpenAI’s “GPT-4 was merely the continuation of a decade of breakneck progress in deep learning. A decade earlier, models could barely identify simple images of cats and dogs; four years earlier, GPT-2 could barely string together semi-plausible sentences. Now we are rapidly saturating all the benchmarks we can come up with. And yet this dramatic progress has merely been the result of consistent trends in scaling up deep learning…Another jump like that very well could take us to [artificial general intelligence], to models as smart as PhDs or experts that can work beside us as coworkers.” For GPT-4, released in 2023, he described it as a “smart high schooler” and commented: “Wow, it can write pretty sophisticated code and iteratively debug, it can write intelligently and sophisticatedly about complicated subjects, it can reason through difficult high-school competition math, it’s beating the vast majority of high schoolers on whatever tests we can give it, etc.” He presented this chart comparing GPT-3.5 and GPT 4 (models already two-to-three years old) to human test-takers:

   

Source: Situational Awareness - The Decade Ahead, June 6, 2024, Leopold Aschenbrenner

Over time, as models continue to improve, and the cost of running them declines, an increasing number of human tasks could be augmented or replaced entirely by AI. Before long, every digital interaction–whether with business software, consumer apps, robots, cars, etc. – will be AI powered. AI will make humans more productive doing their jobs, developing drugs, designing products, writing software, being creative, and more.

Software

While AI demand and experimentation have clearly benefitted semiconductors stocks (as well as certain energy, industrial, and data center stocks), it has created market uncertainty around the state of software. In fact, year-to-date we have seen the widest discrepancy in semiconductor and software performance in 20 years.

Why has software lagged year to date? First, as we discussed above, the AI application phase – where enterprise software lies – simply comes later on the AI S-curve or series of S curves. In our myopic, short-term focused market, there is simply less investor excitement for software, and most investors are waiting to see the results – in contrast to the results they are seeing for, say, semiconductors – before jumping back into software. As we described earlier, some software applications (we highlighted AI code writing tools) are already having a profound impact, while others are still in the proof-of-concept stage and will require further iteration and 2.0 or subsequent releases before they really impress. Second, during the second quarter several industry bellwethers, including Salesforce.com and Workday, reported soft financial results and issued disappointing guidance. Among the software companies we track, more than half guided their next quarter revenue below Street expectations. These companies cited a series of reasons including longer sales cycles and tighter Information Technology (IT) spend environments. While part of the weakness could be chalked up to IT budget cyclicality, the sudden shift does beg the question: is AI investment “crowding out” software spend? What does that mean for the long-term growth of software businesses? As CIOs and CEOs are under pressure to adopt AI technology and articulate AI strategies, a bearish narrative emerged that some software models are at risk of being displaced or becoming obsolete.

With any technology platform shift – be it on-premise servers to cloud computing, desktop to mobile applications, or automation to intelligence – our job as software investors is to analyze the threat of substitution, the change to competitive dynamics, and the impact on pricing models and unit economics. While AI poses a risk to some software companies, we think the consensus that all software is at risk is incorrect. In our view, the more likely explanation for the longer software sale cycles is that customers are being more thoughtful and strategic in their software vendor selection – they want to find the right longer-term partners whose products support their 3-5-10-year AI initiatives. Thus, we think the software businesses with the right architecture, product roadmaps, and customer value creation track records should see their competitive moats widen, not contract, as AI proliferates, and ultimately capture more IT budget share over time. To be clear, our investment goal for software, and any other industry vertical, is to own the winners, not the group.

In our view, the enterprise software winners will have to be better at delivering AI services and features than build-your-own AI tools, and they will have to use their incumbency or leadership advantages to ward off upstarts. We believe the winners will be the ones that have a well-established product development culture of innovation and iteration; differentiated proprietary, industry, and customer data; distribution advantages with large customer bases, successful go-to-market efforts, and key partners; well-designed workflows where AI improves the user interface, intelligent predictions/recommendations, and automation; and established always-on connectivity and feedback from their customers; among other things.

Here are a few examples of our software investments that we believe are AI winners:

• Microsoft Corporation, a leading software vendor, where Azure OpenAI – its suite of AI services that allows customers to apply natural language algorithms on data – is now used by 65% of the Fortune 100, and GitHub Copilot – its AI code writing service – is delivering 40%- plus improvements in developer productivity and now has 1.8 million subscribers.
• Datadog, Inc., a cloud observability platform that the leading LLM providers are using today to monitor their AI apps; these AI customers are already driving nearly $100 million of annual recurring revenue for Datadog already.
• Intuit Inc., a software platform that specializes in developing and selling accounting and tax preparation software, is applying AI: (1) to provide personalized tax advice and optimize tax returns; and (2) to automate bookkeeping, provide predictive analytics to forecast and analyze financial trends and cash flow, and smart invoicing (recommending the best times to send invoices for the highest likelihood of timely payments).

We continue to run a high-conviction portfolio with an emphasis on the secular trends cited above. Among others, during the first quarter we initiated positions in or increased portfolio weights of the following positions:

• Consumer Technology Hardware: Apple Inc.
• Software: Cadence Design Systems, Inc., Datadog, Inc., and Intuit Inc.
• Semiconductors: NVIDIA Corporation, Taiwan Semiconductor Manufacturing Company Limited, eMemory Technology Inc., Park Systems Corporation, and Broadcom Inc.
• Electronic weapons and personal defense technology: Axon Enterprise, Inc.
• E-commerce: Shopify Inc.
• Electric Vehicles: Tesla, Inc.
• Digital media: Spotify Technology S.A.

We remain steadfast in our belief that exposure to the broader IT sector should be a material part of an investor’s portfolio for the long term. Technology has the power to reshape industries, disrupt business models, and create opportunities for substantial wealth creation.

Shares of CoStar Group, Inc. detracted from performance. We believe that CoStar shares were impacted by concerns that the company’s second quarter financial results will show a deceleration in net new sales of its residential product following outstanding first quarter performance. CoStar began to monetize its residential offering in February, and had an excellent start, generating $39 million of net new sales in less than two months. However, the pace of adoption seemed to slow in May and June, leading to share price declines. We believe that few businesses progress linearly and variability in results across quarters is to be expected. We view the residential real estate market as a vast and underpenetrated opportunity. As an asset class, single-family residential properties represent more than $40 trillion of value in the U.S., or around 60% of the total value of U.S. real estate. We estimate that CoStar’s residential products will address a total addressable market (TAM) that exceeds $15 billion of annual recurring revenue, or almost four times larger than the company’s flagship Suite offering currently serves. We estimate that offering a residential product in international markets could increase that TAM by a further factor of four.

Advanced Micro Devices, Inc. (AMD) is a global fabless semiconductor company focusing on high performance computing technology, software, and products including CPUs,9 GPUs, FPGAs,10 and others. Shares of AMD remain volatile, and after a strong run earlier in the year, the stock fell during the quarter as investors continue to wrestle with AMD’s competitive positioning in the AI compute market relative to NVIDIA, who continues to strengthen its full-system solution offerings at a rapid pace. AMD also updated its MI300 GPU chip revenue expectations for the full year to “greater than $4 billion” vs. prior $3.5 billion, which disappointed the market a bit relative to high expectations. Over the long-term, we believe AMD, with its unique chiplet-based architecture and open-source software ecosystem, will play a meaningful role in the rapidly growing AI compute market, where customers don’t want to be locked into a single vendor and AMD offers a compelling total-cost-of-ownership proposition, especially in inferencing workloads. Simultaneously, we believe AMD will continue to take share from Intel within traditional data center CPUs, which, while now a slower growth market, is likely to see a near-term refresh as data centers look for ways to improve energy efficiency and optimize existing footprints.

Workday, Inc. is a leading cloud human capital and financial management software vendor. The stock detracted from performance after it reported an “in-line” subscription revenue quarter, which marked the second quarter in a row of weaker-than-expected bookings growth (quarter-over-quarter change in 12-month current revenue performance obligations + subscription revenues), with bookings decelerating to 13% the fourth quarter of fiscal year 2024 and 12% in the first quarter of fiscal year 2025. Management noted it saw extended deal cycles and customers committing to lower headcount on renewals. In our view, Workday either needs to be able to reaccelerate growth or show greater margin expansion (it is tracking about 500 basis points below its closest peers, which are delivering close to 30% adjusted operating margins vs. about 25% for Workday). Given the current headwinds around IT budgets – namely, the ability for back office digital transformation projects to sustain priority amidst AI projects that tend to focus on the front office – and the company’s tardy margin expansion, we decided to exit the remainder of the position (we had trimmed it after our visit to Workday’s headquarters in the March quarter). We will revisit the name if we gain greater conviction in either faster growth or an updated target model that incorporates more operating leverage.

We invest in companies of any market capitalization that we believe will deliver durable growth from the development, advancement, and/or use of technology. We invest principally in U.S. securities but may invest up to 35% in non-U.S. securities.

At the end of the second quarter, the largest market cap holding in the Fund was $3.3 trillion and the smallest was $888 million. The median market cap of the Fund was $45.6 billion and the weighted average market cap was $1.3 trillion.

We had investments in 36 unique companies. Our top 10 positions accounted for 64.3% of net assets.

To end the quarter, the Fund had $35.2 million in net assets. Fund flows were solid during the quarter.

* Individual weights may not sum to the displayed total due to rounding.

This quarter we increased the size of our position in Apple Inc., a leading technology company known for its innovative consumer electronics products like the iPhone, MacBook, iPad, and Apple Watch. Apple is a leader across its categories and geographies, with a growing installed base that now exceeds 2 billion devices globally. The company’s attached services – including the App Store, iCloud, Apple TV+, Apple Music, and Apple Pay – provide a higher margin, recurring revenue stream that both enhances the value proposition for its hardware products and improves the financial profile. Apple now has well over 1 billion subscribers paying for these services, more than double the number it had just 4 years ago. The increasing services mix has led to healthy operating margin improvement, providing more free cash flow for Apple to reinvest in the business and to distribute to shareholders. Throughout its 48-year history, Apple has successfully navigated and capitalized on major technological shifts, from PCs to mobile to cloud computing. We believe the company’s leading brand and device ecosystem position it to do equally well in the AI age, and this was the driver of our decision to re-invest. “Apple Intelligence” – the AI strategy unveiled at Apple’s recent Worldwide Developer Conference – leverages on-device AI and integrations with tools like ChatGPT to enhance user experiences across its ecosystem. The AI suite enables users to create new images, summarize and generate text, and use Siri to perform actions across their mobile applications, all while maintaining user privacy and security. We think Apple Intelligence can drive accelerated product upgrade cycles and higher demand for Apple services. The combination of growth re-acceleration, increasing services contribution, and thoughtful capital allocation should continue driving long-term shareholder value.

For Amazon.com, Inc., Microsoft Corporation, and NVIDIA Corporation, as the Fund experienced steady inflows, we purchased shares to maintain the portfolio weights of these investments close to the 10% level.

We exited our software investments in Workday, Inc., Snowflake Inc., and Dayforce, Inc., as well as video game provider Take-Two Interactive Software, Inc., and spread that capital around to several of our other software investments, including increases in our Cadence Design, Shopify, and Datadog positions, as well as to help fund our Apple, Axon Enterprises, Spotify, and Tesla purchases.

We exited our investment in Marvell Technology, Inc., and spread that capital around to our other semiconductor investments (see companies listed in Review and Outlook above).

To conclude, we remain confident in and committed to the strategy of the Fund: durable growth based on powerful, long-term, innovation-driven secular growth trends across the broader technology space.

Sincerely,