Incumbents Leverage Domain Expertise and Data Moats to Thrive in AI Revolution

The software industry is at a critical inflection point, facing an existential question: can it survive the AI revolution? While many investors have reacted with fear, leading to significant sell-offs, a deeper analysis reveals that the narrative of AI as a pure disruptor is incomplete. This conversation with Gabriella Borges of Goldman Sachs Research unpacks the subtle but crucial ways established software companies can leverage their existing strengths, particularly domain expertise and data moats, to not only survive but thrive. The hidden consequence for investors and leaders is the emergence of a new breed of "AI-enabled native" companies and the potential for incumbents who adapt to build durable competitive advantages. Those who understand this dynamic will be better positioned to identify resilient companies and navigate the evolving landscape, gaining an edge in a market driven by fear and speculation.

The AI Paradox: Disruption or Differentiation?

The recent market turbulence in the software sector, marked by significant sell-offs, stems from a narrative shift around generative AI. What was once a source of enthusiasm has morphed into a significant concern about disruption. This anxiety is fueled by rapid advancements in coding algorithms, making AI tools accessible beyond specialized developers, and a simultaneous increase in competitive intensity. As Gabriella Borges explains, the market is grappling with the dual reality of finally seeing enterprise adoption of AI while also witnessing a surge in competing AI solutions. This has led investors to question the defensibility of existing software company "moats."

Borges highlights cybersecurity firm CrowdStrike as an example of a company with a well-established moat. For over a decade, CrowdStrike has been meticulously collecting endpoint data, employing human-in-the-loop reinforcement learning, and infusing threat intelligence. This vast dataset and accumulated domain experience create a significant barrier to entry. However, the crucial question for investors is whether this incumbency translates into superior AI-driven outcomes. Can CrowdStrike, or any established player, prove that their AI-powered solutions offer a better customer experience than newer, AI-native startups or even competitive offerings from tech giants like Microsoft with its Copilot. The onus is on these incumbents to demonstrate that their domain expertise and data advantage can be effectively translated into tangible, superior AI performance for their customers.

"We do think it's one of the more interesting moats that incumbents have now."

-- Gabriella Borges

This dynamic extends to major players like Salesforce, which faces competition from third-party AI tools like Cifornia being built on top of its platform. Salesforce must prove that its own AI technology, Agent for Salesforce, can deliver a more compelling outcome than these external solutions. Similarly, Microsoft's Copilot needs to demonstrate its superiority over tools that plug into existing workflows. The implication is that technological advancement alone is not enough; incumbents must actively leverage their unique advantages to deliver demonstrably better AI experiences. Failure to do so risks ceding ground to more agile, AI-native competitors.

Navigating the Shifting Sands: Moats in a Dynamic Landscape

The defensibility of these moats is not a static question. The rapid evolution of AI technology and enterprise adoption necessitates continuous re-evaluation. Borges outlines a rigorous due diligence process that involves understanding both the public and private market landscapes. By engaging with startups and CTOs, analysts aim to uncover the true nature of technical moats and product roadmaps.

For fundamental investors, the focus shifts to leading indicators: bookings, billings, and crucially, AI usage metrics. The adoption rate of AI functionality, measured by the percentage of monetized seats, provides a vital signal of a company's success in integrating and differentiating its AI offerings. However, the question of long-term endurance remains. Borges suggests that moats will endure, but selectively. This requires software leaders to undertake a three-pronged strategy:

1. Re-platforming the Tech Stack: Modernizing existing infrastructure, ensuring seamless data flow, and minimizing legacy technical debt are paramount. Architectures built over decades can become significant hindrances if not actively addressed.
2. Developing an Organic Roadmap: This involves securing the right engineering talent and tools to drive productivity. For companies with identified roadmap gaps, a robust M&A strategy becomes critical. Identifying and acquiring promising private companies that offer product-market fit can accelerate innovation and fill strategic voids.
3. Mastering Monetization: The ability to track, measure, and price AI-driven usage is essential. This pricing power serves as a litmus test for product differentiation and the sustainable monetization of AI capabilities.

"The question becomes okay CrowdStrike or okay company abc if you have all this domain experience you have to prove to us as an investor community and as a customer base that you can actually deliver better ai experiences because of that."

-- Gabriella Borges

This strategic approach suggests that companies that proactively address these areas today are more likely to maintain leadership positions in three to seven years. The immediate pain of re-platforming and the discipline of strategic M&A, rather than trying to build everything organically, can create a significant competitive advantage by outpacing rivals who are slower to adapt.

The Numbers Game: Stabilizing Metrics and Shifting Sentiment

The path to market stabilization for the software sector hinges on the numbers contradicting the prevailing narrative of disruption. Borges points to the deterioration of key software metrics like Annual Recurring Revenue (ARR) growth and the Loan-to-Value to Customer Acquisition Cost (LTV:CAC) ratio over the past four years. While the digital transformation boom of 2020-2021 temporarily masked these trends, a subsequent digestion period has led to slowing growth.

However, encouraging signs are emerging. During recent earnings seasons, companies that delivered solid results saw their stock prices react favorably. This market engagement with individual stock performance, driven by fundamental data points, suggests a potential recovery. This is particularly true for companies that are actively transforming and demonstrating AI adoption.

Furthermore, investor sentiment is evolving. A cohort of value-oriented portfolio managers, historically underweight or short on high-priced software assets, are now entering the space. This shift is partly driven by a greater focus on GAAP earnings power, moving away from non-GAAP metrics that often obscure stock-based compensation dilution. Software companies are also demonstrating their ability to leverage AI internally, improving operational efficiency and potentially decoupling operating expense growth from talent acquisition costs. This combination of improving fundamentals, a broader investor base, and the strategic use of AI by incumbents is creating attractive valuations and attracting a new type of investor.

The scarcity of growth, with median growth rates significantly lower than in prior years, is also reshaping the investor landscape. This is attracting Growth at a Reasonable Price (GARP) investors. Companies that have successfully expanded margins, such as Okta's rapid increase in free cash flow margin, are now being rewarded. The ability of software companies to achieve this through AI-driven productivity gains, rather than solely through headcount reduction, presents a compelling narrative for sustained profitability.

"We're finally at the point where you can actually start to do some interesting analysis around gap earnings power and we think that's bringing a set of investors into the space that historically have essentially written it off as being too expensive."

-- Gabriella Borges

The proactive adaptation of software leaders, through both organic innovation and strategic M&A, mirrors the playbook seen in the cybersecurity sector. Companies like CrowdStrike and Palo Alto Networks have consistently leveraged M&A to strengthen their platforms and reduce customer acquisition costs in the face of an active adversary. This parallel suggests that application software giants like Microsoft, Salesforce, Workday, and Intuit could similarly emerge stronger by integrating acquired technologies and continuing their organic innovation efforts.

Key Action Items

• Re-evaluate Core Technology Stacks: Over the next 6-12 months, prioritize modernizing existing tech stacks, ensuring data fluidity, and actively reducing legacy technical debt. This immediate discomfort will pay off in long-term agility.
• Develop a Robust M&A Strategy: Within the next quarter, identify 2-3 key areas where external acquisition of AI-native companies could accelerate your roadmap. This requires patience and a willingness to acquire rather than build everything.
• Quantify AI Usage and Monetization: Immediately begin tracking and measuring AI feature adoption and usage within your installed base. This lays the groundwork for future pricing power and differentiated monetization strategies.
• Demonstrate AI-Driven Outcomes: Over the next 12-18 months, actively prove to customers and investors that your AI capabilities, leveraging domain expertise, deliver superior results compared to generic AI tools. This is a critical differentiator.
• Focus on GAAP Profitability: For the next 1-2 years, prioritize demonstrating strong GAAP earnings power, managing stock-based compensation dilution, and optimizing operational expenses, potentially through AI-driven internal productivity gains. This will attract a broader, value-oriented investor base.
• Invest in Talent for Adaptation: Over the next 6 months, assess your engineering team's capabilities in integrating and leveraging AI technologies, both internally and through acquisitions. This is crucial for executing on the new playbook.
• Communicate the Long-Term Vision: Continuously articulate how your company is adapting to the AI landscape, highlighting both organic innovation and strategic M&A, to build investor confidence in enduring moats. This is a message that needs consistent reinforcement over the next 1-3 years.