Market volatility has been elevated recently amid shifting economic conditions and evolving geopolitics. Beneath the surface, investors have also been reassessing parts of the technology space based on their real or perceived exposure to artificial intelligence (AI), influencing sentiment and creating opportunities for patient, long-term investors.
In this Q&A, Chief Investment Officer Andrew Iu discusses what is driving this shift, how our Investment Team is considering the implications of AI, and where we are finding opportunity amid the volatility.
Since the rise of generative AI technologies like ChatGPT just a few years ago, enthusiasm for AI’s transformative potential has driven valuations higher across software and data businesses, from subscription-based platforms to database services built on proprietary content. In recent months, however, share prices across many of these businesses have pulled back as investors take a closer look at how AI could reshape existing business models. Sentiment has shifted from the view that AI will lift all boats to a more cautious recognition that it may also disrupt them.
With the market punishing much of the industry, and AI tools becoming increasingly capable, investors are trying to separate the companies most exposed to disruption from those best positioned to sustain growth, maintain pricing power, and defend their moats.
To better understand what is driving the repricing and what this means for Burgundy and our clients, we spoke with Chief Investment Officer Andrew Iu.
What is happening to software and data stocks?
Andrew Iu (AI): The market is concerned that AI could weaken certain business models. We have seen this unfold in three primary ways.
First, lower barriers to entry in software. Modern AI can write and customize code quickly, potentially making it faster and less costly to build competing products.
Second, there is pressure on per-seat pricing. Many software companies charge based on the number of users. If AI agents replace certain roles, seat counts (and by extension, the software company’s revenue) could decline. Another concern is that if AI agents, rather than humans, are using the software, brand loyalty and user preference may matter less. Agents do not value these things the way human users do.
Third, lower barriers in certain data businesses. AI tools can clean, structure, and process data efficiently. The worry is that this could weaken the competitive moat around some subscription-based data services.
How significant has the reset been?
AI: It has been meaningful. By early March, the average financial data or exchange company on our U.S. Watch List fell more than 30% from its highs, while the average software company dropped more than 40%. For perspective, some of those highs were set just a few months ago. This represents a meaningful reset in valuations and highlights just how much optimism was built into prices. In other words, the size of the decline mirrors the size of the earlier run-up. When expectations become elevated and skepticism enters the picture, uncertainty can trigger a sharp repricing.
It is also worth noting that Burgundy did not have a large exposure to software businesses going into this downturn. Our valuation discipline largely kept us out of software during the earlier run-up, when many companies were trading at prices we did not believe reflected a sufficient margin of safety.
Is this a structural problem for the entire industry?
AI: There will be casualties, but not every company will be affected equally. We believe the market is treating much of the group too similarly, and when selling becomes indiscriminate, important differences in business quality can be overlooked. Some business models may face genuine challenges, but others have durable characteristics that are harder to disrupt than the headlines suggest. Being able to distinguish between the two becomes critical. Otherwise, the proverbial baby gets thrown out with the bathwater.
And how do you distinguish between the two? What factors still give a software company an edge?
AI: A few characteristics stand out.
One is deep industry expertise. Some software solutions are deeply embedded in daily operations and reflect decades of industry-specific knowledge. Even if switching becomes technically easier, replicating that domain expertise is far more difficult. We find this type of business across our regional portfolios, including in recent additions like U.S. tech conglomerate Roper Technologies as well as Canadian communications software company Lumine. Our long-term holding in German enterprise software company SAP is another example.
Hard-to-replicate data assets also still matter. In regulated industries, data cannot simply be recreated by an AI start-up. Credit bureaus and financial institutions operate within strict regulatory frameworks that protect privacy. Building a competing dataset is extremely difficult, even with advanced AI tools. The recent sell-off gave us the opportunity to invest in Experian, a UK-based consumer credit reporting and data services company that benefits from this kind of data advantage.
Finally, accuracy and trust still matter. In law, medicine, and finance, errors carry significant consequences. Some information providers rely on expert-reviewed, curated content. When malpractice risk is involved, trust and accuracy matter more than speed alone. We recently invested in Canadian-based Thomson Reuters and Dutch-based Wolters Kluwer, whose databases are used by professionals and reviewed by subject-matter experts. Both showcase the strong appetite for accuracy.
“Hard-to-replicate data assets also still matter. In regulated industries, data cannot simply be recreated by an AI start-up.”
Will these technology companies have time to adapt?
AI: In many cases, yes. Companies with embedded expertise, proprietary data, and trusted brands are not standing still. They have the resources and customer relationships to build and deploy their own AI capabilities. Many also operate under long-term customer contracts, which provide stability in revenue and time to adapt their products as technology changes. These relationships often give companies the runway to integrate new capabilities without as much disruption.
Bloomberg, a core research tool for our Investment Team, provides a good example from our own experience. It recently introduced an AI agent to our team. It is a strong starting point, though it is not as fast as it could be. Still, Bloomberg’s proprietary data, deep integration, and high accuracy standards give it time to improve and remain highly relevant for our Portfolio Managers and Investment Analysts.
How is Burgundy’s Investment Team keeping pace as AI continues to evolve?
AI: Advancements in AI are happening rapidly—often on a weekly or even daily basis—and understanding their implications is an important part of our research process. Our Investment Team is actively experimenting with new tools and sharing insights internally, both informally with one another and during our weekly investment meetings.
We are also engaging directly with industry experts. Recently, we invited two specialists to speak with our Investment Team, including one who previously worked at OpenAI, the developer of ChatGPT. Conversations like these help deepen our understanding of how AI systems are evolving and how they may affect different industries. This helps us assess the risks and opportunities, both for the businesses we are already invested in and those we are watching closely.
“Advancements in AI are happening rapidly—often on a weekly or even daily basis—and understanding their implications is an important part of our research process.”
What should our clients keep in mind during periods like this?
AI: We appreciate that bouts of volatility can be unsettling, even more so when headlines suggest that an entire industry may face disruption. While talk of change creates a lot of short-term uncertainty, we stick to a disciplined, long-term approach. Our work is research-driven and bottom-up, focused on business quality, capable management, balance sheet strength, and valuation—the same priorities we regularly discuss with clients.
For patient, long-term investors, moments like this can create opportunity. We spend a lot of time—sometimes years—researching companies before we invest. In recent years, several software and data companies we would have liked to own remained outside our portfolios because their valuations were too high. However, when sentiment shifts and prices reset, as we have seen with the recent volatility, those same high-quality businesses can become available at prices that make sense to us. Ultimately, our goal is to be ready when those moments arise.
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