Debate: AI-Related Software Disintermediation
I do not cover software, but imo the most prominent story in the markets today is legacy software companies getting disintermediated by AI. I want to pose a few questions and see if I can get the conversation rolling.
(1) The debate seems to be centered around switching costs. AI will make it easier to move my data from one platform to another, and companies will likely be created to support this transition. On this topic, how difficult is that to do today (e.g., switch from Salesforce to Microsoft Dynamics 365)?
(2)To what extent does the fear come from the prospect of companies creating custom-tailored in-house software for its specific workstreams such that no external vendor is needed at all? This one would likely only be applicable for large caps, but curious.
(3) How much would you say the debate is around pricing power vs obsolescence (e.g., I'm ServiceNow, I'm afraid that if I raise prices 5% next year, customers will seek alternatives, so I keep prices flat, or even lower prices to be defensive)?
(4) Job losses related to AI killing companies whose offering are primarily seat-based (e.g., Adobe - could be a confluence of factors just looking at the chart on this one lol)?
We’re living in the biggest disruption to software business models since the on prem to cloud transition. Who knows what the future holds. The discount rate for all the outer year cash flows has gone up.
Based on the most insightful WSO discussions and threads, here’s a breakdown of your questions regarding AI-related software disintermediation:
(1) Switching Costs: Salesforce to Microsoft Dynamics 365
Switching costs in legacy software ecosystems like Salesforce are notoriously high. Salesforce, for instance, has built a significant moat through vendor lock-in strategies. Customers are deeply integrated into its ecosystem, with custom modifications and third-party integrations that make transitions complex and costly. While AI could theoretically simplify data migration, the reality today is that switching platforms like Salesforce to Microsoft Dynamics 365 remains challenging due to: - Custom Modifications: Upgrades or migrations often break customizations, requiring significant rework. - Data Complexity: Migrating data while maintaining its integrity and usability is a technical hurdle. - Vendor Lock-In: Salesforce’s PaaS model encourages startups and vendors to build on its platform, further entrenching customers.
AI tools may reduce friction in the future, but for now, the process is far from seamless.
(2) Custom In-House Software for Large Caps
The fear of large-cap companies developing custom in-house software is valid but limited to specific scenarios. While AI can enable companies to tailor solutions to their unique workflows, the cost and expertise required to build and maintain such systems are significant. For most companies, the trade-off between customization and the scalability of external vendors like Salesforce or Microsoft Dynamics still leans toward the latter. However: - Large Caps with Resources: Companies with substantial budgets and technical expertise (e.g., FAANG-level firms) might find it feasible to develop in-house solutions. - AI’s Role: AI could lower the barrier to entry for custom software development, but the operational and maintenance costs remain a deterrent for all but the largest players.
(3) Pricing Power vs. Obsolescence
The debate around pricing power versus obsolescence is central to the software industry. Companies like Salesforce and ServiceNow face a delicate balance: - Pricing Power: Legacy software companies often hesitate to raise prices due to fear of customer churn, especially as AI-driven competitors emerge with potentially lower-cost solutions. - Obsolescence: The risk of obsolescence is heightened by AI, as it enables new entrants to offer innovative, cost-effective alternatives. However, the entrenched nature of legacy systems and the high switching costs provide a buffer against immediate disruption.
(4) Job Losses and Seat-Based Models
AI’s impact on seat-based software models is a growing concern. Companies like Adobe, which rely on per-seat pricing, face risks from: - Automation: AI tools can replace manual tasks, reducing the need for multiple seats/licenses. - Consolidation: AI-driven platforms may offer broader functionality, reducing the need for multiple software solutions. - Market Dynamics: As seen with Adobe, external factors like pricing pressure and competition exacerbate these challenges.
In summary, while AI is undoubtedly reshaping the software landscape, the extent of its impact on switching costs, in-house development, pricing strategies, and job losses will depend on how quickly companies adapt to these changes. The legacy players with strong moats and innovative strategies are likely to weather the storm better than those that remain stagnant.
Sources: Salesforce: May The Force Be With You, Salesforce: May The Force Be With You
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