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.

A key risk for 'AI-native services' companies is achieving rapid revenue growth that isn't actually driven by technology. This 'mirage product-market fit' occurs when the service is delivered primarily by humans, not scalable software. This creates a false sense of traction and a business with poor, unscalable margins.

For established software companies, simply integrating AI is not enough. Investors are looking for a clear signal that AI is a true growth catalyst, not just a feature enhancement. The key question investors ask is whether AI will re-accelerate the company's growth. Without tangible proof in sales numbers, investor sentiment will remain neutral or bearish.

The market narrative suggests AI will decimate SaaS companies. However, current earnings data reveals a different story. Major players like Salesforce, GitLab, Snowflake, and Datadog are still reporting strong double-digit revenue growth. This highlights a significant disconnect between speculative fear about AI replacing software and the present-day financial performance of these companies.

Twilio's usage-based pricing seems resilient, but it faces a unique AI risk. If AI makes customer service calls more efficient and shorter, it could decrease total platform usage and therefore revenue. This 'efficiency paradox' is an under-discussed vulnerability for consumption-based business models in the AI era.

The stock market's enthusiasm for AI has created valuations based on future potential, not current reality. The average company using AI-powered products isn't yet seeing significant revenue generation or value, signaling a potential market correction.

A "SaaSpocalypse" is unfolding where public SaaS company valuations crater immediately following AI labs announcing new plugins or capabilities, regardless of the feature's actual market readiness. This shows the market is now trading on the perceived threat of AI disruption rather than on traditional financial metrics, creating immense volatility.

Simply incorporating AI features is "performative." The true measure of being an AI company is whether the technology has tangibly re-accelerated revenue growth. Without that lift, the AI label is meaningless to investors and the market, as demonstrated by Meta's successful turnaround.

Companies are spending millions on enterprise AI tools not for measurable productivity gains but for "digital transformation" PR. A satirical take highlights a common reality: actual usage is negligible, but made-up metrics create positive investor narratives, making the investment a success in perception, not practice.