Current issues with SAS (Software as a Service) pricing model
Common belief: SAS is consistent like chicken for private equity due to predictable revenue
Shift from consistent revenue models due to external pressures
Historical Context
2010s: B2B SAS model valued for predictable revenue and low-risk exits to private equity
Private equity and VCS favored the model for its stability and ease of business valuation
Key Factors Changing the SAS Landscape
Impact of AI
AI is disrupting traditional SAS pricing power
Example: Clara moved off Salesforce to internal AI solutions, boosting profitability
AI allows for increased efficiency and customization in software solutions
Changes in Customer Expectations
Companies expect more customization and efficiency due to AI
Increased pressure for vendors to provide custom solutions, influencing margins
Shifts in Pricing and Packaging
Traditional per-seat pricing model under threat
AI features prompting new pricing strategies (e.g., per outcome, per agent)
Customization requires more resources, affecting SAS margins
Challenges with New Pricing Models
Per Seat Pricing: Vulnerable to being undercut, less attractive with AI features
Outcome-Based Pricing: Harder to value, associated with lower revenue quality
Customization and Service Revenue: Viewed less favorably than standard software revenue
Broader Implications
Inconsistent revenue models challenge traditional SAS valuation
Potential decrease in attractiveness for exits and funding
New models emerging but without established paths or guarantees
Case Studies
Clara: Using AI for profitability and potential IPO
Stripe: Staying private despite profitability, using efficient AI models
Conclusion
The SAS model faces disruption from AI
New pricing strategies will emerge, but definitive solutions are unclear
Great businesses will innovate and adapt, but the landscape is fundamentally changing
Final Thoughts
The future of SAS involves navigating uncertain pricing and valuation strategies
Fundamental shifts in the industry driven by AI require adaptive strategies
Continued observation needed to understand evolving SAS exit and valuation models
Key Takeaway: The traditional SAS model is under pressure due to AI, changing customer expectations, and evolving pricing strategies, leading to a less consistent and predictable business environment.