Jared Sleeper on Which Software Companies Will Survive the "SaaSpocalypse"

What It Covers

Jared Sleeper, partner at Avenir growth fund, analyzes the SaaS sector selloff driven by AI code generation fears. He examines which software companies face existential risk versus which can survive by leveraging data advantages, network effects, and shifting toward outcome-based pricing models replacing per-seat revenue structures.

Key Questions Answered

• •Software moat beyond code: When evaluating SaaS vulnerability, assess three distinct value layers: herd familiarity (universal user training like Zoom or Excel), ecosystem integrations, and brand trust. Pure code generation by AI threatens only one layer. Companies like DocuSign retain value through legal compliance expertise across every country and API brand credibility that vibe-coded alternatives cannot replicate.
• •Outcome-based pricing shift: SaaS companies transitioning from per-seat pricing (roughly $1,000 per user annually) to results-based models can capture dramatically higher revenue. A tool replacing a $250,000 sales rep priced at $50,000 delivers a 5x ROI to customers while increasing the vendor's take rate approximately 50-fold, fundamentally restructuring software economics.
• •SMB versus enterprise AI risk: Enterprise software with heavy customization and complex implementations faces the highest near-term disruption risk because large organizations have resources to act. SMB-focused software serving dentists, grocery stores, or similar operators carries lower displacement risk since those owners will not rebuild core systems themselves regardless of AI capability improvements.
• •Financial floor problem: The median public software company runs at only 5% GAAP net income margin due to stock-based compensation excluded from non-GAAP reporting. Without material GAAP earnings, there is no valuation floor during selloffs. Companies like Freshworks trading at 1.5x EV/sales would attract value investors at 15x earnings if they reported even 10% GAAP margins.
• •Context data as competitive moat: AI agents require organizational context — customer histories, internal data, process knowledge — to function effectively regardless of model intelligence. SaaS companies like Salesforce holding CRM records, interaction logs, and pipeline data occupy a structural position as the system of context that any enterprise AI deployment must access to operate.