As AI commoditizes software creation and data migration, the high-margin, sticky nature of SaaS will disappear. Klarna's CEO predicts that valuations will compress from historical 20-30x price-to-sales multiples down to 1-2x, similar to how low-moat utility companies are valued.
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The rise of agentic coding is creating a "SaaSpocalypse." These agents can migrate data, learn different workflows, and handle integrations, which undermines the core moats of SaaS companies: data switching costs, workflow lock-in, and integration complexity. This makes the high gross margins of SaaS businesses a prime target for disruption.
The "SaaS-pocalypse" isn't about AI replacing software overnight. Instead, AI's disruptive potential erases the decades-long growth certainty that justified high SaaS valuations. Investors are punishing this newfound unpredictability of future cash flows, regardless of current performance.
The "SaaSpocalypse" isn't about current revenues but a collapse in investor confidence. AI introduces profound uncertainty about future cash flows, causing the market to heavily discount what was once seen as bond-like predictability. SaaS firms must now actively prove they are beneficiaries of AI to regain their premium valuations.
Sridhar Ramaswamy suggests software valuation multiples are contracting because investors see through the strategy of just adding an 'AI SKU.' The market believes this approach won't win, favoring integrated, consumption-based models where customers only pay for demonstrated value from AI.
The true threat to SaaS isn't just cheap software creation, but AI agents that automate data migration between platforms. This destroys the lock-in effect of proprietary data models, turning SaaS into a low-multiple utility business where switching costs approach zero.
AI is making core software functionality nearly free, creating an existential crisis for traditional SaaS companies. The old model of 90%+ gross margins is disappearing. The future will be dominated by a few large AI players with lower margins, alongside a strategic shift towards monetizing high-value services.
The dominant per-user-per-month SaaS business model is becoming obsolete for AI-native companies. The new standard is consumption or outcome-based pricing. Customers will pay for the specific task an AI completes or the value it generates, not for a seat license, fundamentally changing how software is sold.
The primary threat of Large Language Models to the SaaS industry isn't that they will build better software, but that they will enable the creation of 50 to 100 competitors for every existing player. This massive increase in competition will inevitably compress profit margins for everyone.
Software has long commanded premium valuations due to near-zero marginal distribution costs. AI breaks this model. The significant, variable cost of inference means expenses scale with usage, fundamentally altering software's economic profile and forcing valuations down toward those of traditional industries.
The ongoing decline in growth rates for public SaaS companies has created an existential crisis around revenue durability. Investors have lost confidence that traditional SaaS models can sustain growth in the face of AI disruption, leading to a massive valuation collapse.
